Building a Pre-Design Ontology

UNIVERSIDADE TÉCNICA DE LISBOA

FACULDADE DE ARQUITECTURA

Building a Pre-Design Ontology Towards a model for urban programs

DISSERTAÇÃO PARA OBTENÇÃO DO GRAU DE MESTRE EM REGENERAÇÃO URBANA E AMBIENTAL

Orientador Científico:

Doutor José Manuel Pinto Duarte Co-orientador Científico:

Doutor George Stiny

Jurí: Presidente: Doutor Luís António dos Santos Romão Vogais: Doutor João Altino Serra de Magalhães

Doutor José Manuel Pinto Duarte Doutor George Stiny

Lisboa, Junho de 2010

Nuno Filipe Santos de Castro Montenegro (Licenciado)

building a pre-design ontology: towards a model for urban programs

ii


iii

Nuno Filipe Santos de Castro Montenegro

Departamento: Urbanismo

Orientador Científico: Professor Doutor José Manuel Pinto Duarte

Data: Junho de 2010

TÍTULO

Uma Ontologia para Planeamento1: Contributo para um modelo de programas urbanos

RESUMO

O objectivo deste estudo é a formulação de soluções para problemas urbanos. O estudo parte

da teoria da linguagem padrão de Christopher Alexander e de um conjunto de informações

relativas a regulamentos e recomendações aplicáveis ao planeamento urbano, para elaborar

um sistema que gera as especificações ou os ingredientes de um plano; dada uma escala, um

local e uma comunidade específicos. O sistema apoia-se num conjunto de dados necessários

ao plano, designadamente estratégias, regulamentos, características físicas do local e

socioeconómicas da população. O sistema inclui a classificação e a organização desses dados

através de uma sequência de eventos, fases, categorias, métodos e utilizadores. O objectivo é

descrever os níveis taxonómicos do sistema e as relações de interdependência entre as

entidades que compõem o referido sistema. Essa ontologia permitirá fornecer, em

investigações futuras, uma estrutura pré-codificada para viabilizar a sua aplicação num modelo

computacional, apoiada no modelo espacial SIG (Sistema de Informação Geográfica). O modelo

de formulação urbana é essencialmente concebido para incrementar índices de qualidade,

reduzindo ambiguidades, e permitindo administrar o planeamento urbano através de um

processo mais flexível e automático.

Palavras-chave: Planeamento urbano, Ontologias, Linguagem Padrão.

1. Planeamento no contexto desta investigação está relacionado com a ferramenta administrativa que normalmente ocorre numa fase anterior ou simultânea ao desenvolvimento do desenho de um plano urbano, permitindo a interpretação de uma dada realidade urbana existente ou desejada, de forma a avalia-la e a estruturar percursos adequados para a implementação. Trata-se de um processo de deliberação que escolhe e organiza acções, antecipando resultados esperados. De acordo com o conceito defendido por Peter Drucker (2007) existem dois critérios indispensáveis ao planeamento: eficácia e eficiência. A eficácia é o critério mais importante, já que nenhum nível de eficiência, por mais alto que seja, compensa a má escolha dos objectivos do planeamento, isto é, a eficiência no desempenho das actividades de implementação de um plano sobrepõe-se a eventuais falhas na definição dos objectivos da sua organização.


iv

Nuno Filipe Santos de Castro Montenegro

Department: Urbanism

Supervisor: Professor Doutor José Manuel Pinto Duarte

Date: June 2009

TITLE

Building a Pre-Design Ontology: Towards a model for urban programs

ABSTRACT

This study is concerned with the formulation of solutions for urban problems. It departs from

Alexander’s pattern language theory and from a series of urban design guidelines, to create a

system for generating specifications or the ingredients of a plan, given a scale, a site and a

community. It takes into account strategies, regulations, guidelines, physical features of the

site, and furthermore, the social, cultural and economic characteristics of the population. This

system, organised according to a sequence of events, through stages, categories, methods and

agents, describes taxonomic levels and their inner relations. Such ontology will provide, in

future research, a pattern encoding structure towards a computational model within the

capabilities provided by the spatial data modelling of GIS (Geographic Information System).

The urban formulation model is conceived to increase qualitative inputs, reducing ambiguities,

through a flexible while automated process applied to urban planning.

Keywords: Urban Planning, Ontology, Pattern Language.


v

The author owns any copyright© in this thesis and has given the “Technical University of Lisbon, TULisbon” the right to use such Copyright for any

administrative, promotional, educational and/or teaching purposes. Copies of this thesis, either in full or in extracts, may be made only in accordance

with the regulation of the Faculty of Architecture (FAUTL). The ownership of any patents, designs, trademarks and any and all other intellectual

property rights except for the Copyright (the “Intellectual Property Rights”) and any reproductions of copyright works, for example graphs and tables

(“Reproductions”), which may be described in this thesis, may not be owned by the author and may be owned by third parties. Such Intellectual

Property Rights and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the

relevant Intellectual Property Rights and/or Reproductions.

Cover figure: Pre-design ontological classes and slots sketched in the VizTab of the Protégé 2000 editor.


vi

1.1. Contents

1.1. Contents vi

1.2. List of diagrams viii

1.3. List of tables and figures ix

1.4. Acknowledgements x

1.5. Preface xii

Chapter 1 14

Introduction 14 1

1.6. Introduction 1

1.7. Research context 1

1.8. Problem Definition 2

1.9. Proposed methodology 4

1.10. Concerns involving the creation of the formulation model 6

1.11. Expected outcomes and future work 8

1.12. Organization 10

Chapter 2

Precedents 14

2.1 Introduction 15

2.2 The precedents 15

2.3 Conclusion 20

Chapter 3 23

Methodology 23

3.1. Introduction 24

3.2. Adopted methodology 24

3.3. Ontologies within computer science - (AI) Artificial Intelligence 26

3.4. Ontology Interoperability 27

3.5. Representation of ontologies 27

3.6. Editing ontologies 29

3.7 Comparing ontology editors: Ontolingua and Protégé 32


vii

Chapter 4 36

Conceptual and Formulation Models 36


4.2. The conceptual model 37

4.3. The formulation model 40

Chapter 5 49

The Formulation Process 49


5.2. Operational structure of the formulation process 50

5.3. The formulation phases 52

5.4. Categories enclosing the main processes of the pre-design phase 53

5.5. Strategies 54

5.6. Regulations 59

5.7. Site and population context 62

5.8. Document synthesis 70

5.9. Planner’s Language – pattern language 71

Chapter 6 75

The Sketch of a Planning Language 75


6.2. The nature of language 76

6.3. Semantics and Syntax 77

6.4. Planners and Language processing 80

6.5. Lexicon 82

6.6. Pattern Language 84

6.7. Urban Pattern Language Sketch (UPL) 93

6.8. The UPL Ontology 93

6.9. The UPL Syntax and Core Components 94

6.10. The UPL lexicon 100

6.11. The UPL Semantics 104


viii

6.12. Conclusion 112

Chapter 7 114

Conclusion 114

7.1 The Context

7.2 The Problem

7.3 The goal

7.4 The model

7.5 The outcome

7.6 Reflexions for future research

7.7 The opportunity

Glossary 120

References 134

Annexes 145

1.2. List of diagrams

1. Basic process of interaction among the three partial modules of the CI project .......................... 1 2. Change of planning paradigms along time ................................................................................. 3 3. The core matters of the research ............................................................................................... 4 4. Basic diagram: conceptual outline ............................................................................................. 6 5. Formulation research outline……………………………………………………………………..………….. ................. 10 6. Basic diagram: conceptual outline ........................................................................................... 13 7. The core matters of the research (2) ........................................................................................ 13 8. The main laws of urban programs ............................................................................................ 22 9. Example of an urban pattern top level ontology (see Pinho & Goltz)......................................... 29 10. Protégé edition of a climatic pattern........................................................................................ 31 11. The visualization tab of the cold region edited by the Protégé editor ........................................ 31 12. Ontological class hierarchy of the climatic patterns .................................................................. 32 13. Methodology framework…………………….. .................................................................................. 35 14. Duarte’s model (discursive grammar for housing) (Duarte 2007) (Stiny 1981), (Pedro 2001a)

and (Stiny & Gips 1972)………………. .......................................................................................... 38 15. The conceptual model of the urban formulation process (FMo)................................................ 38 16. The urban formulation ontology (Montenegro, N. and Duarte, J, 2008) .................................... 44 17. PD classes, subclass-superclass hierarchy, slots and instances, and the Protégé VizTab ............. 47 18. Urban formulation process (FMo) ............................................................................................ 48 19. A planning schedule………………….. ............................................................................................ 51


ix

20. Pre-design phases – the PD1 (data acquisition) and the PD2 (data translation) ......................... 53 21. The four categories of the formulation process ........................................................................ 54 22. SWOT analysis - the strengths, the weaknesses, the opportunities, and the threats. ................. 57 23. Strategies shown in the simplified diagram . ............................................................................ 59 24. Regulations and codes shown in the simplified diagram . ......................................................... 61 25. Site and population contextual data shown in the simplified diagram . ..................................... 70 26. Pattern language “specifications for design” shown in the simplified diagram .......................... 73 27. The four different categories of the formulation process. ......................................................... 74 28. Language processing………………….. ........................................................................................... 79 29. The ontological diagram of an urban pattern (Montenegro, N.C. and Duarte, J.P., 2008) ........... 83 30. An example of Pattern’s taxonomy (the created links between patterns).................................. 88 31. Syntax class hierarchical tree (above left), exported piece of XML Schema, CPL diagram

classes, subclass-superclass hierarchy, and slots, CPL diagram zoom ........................................ 99 32. UML diagram of the top level class hierarchy (CityGML) ......................................................... 103 33. The core structure of the CPL………… ...................................................................................... 113 34. Syntax class hierarchical tree, exported piece of XML Schema, CPL diagram classes, subclass-

superclass hierarchy, and slots, CPL diagram zoom (2). .......................................................... 160

1.3. List of tables and figures

1. Benefits of design regulations (Evans et al. 2007) ..................................................................... 60

2. Gate Counter Map and Observation sheet example (Space Syntax). ......................................... 68

3. Participants in the planning process (Evans et al. 2007) – part 1 ............................................... 72

4. Participants in the planning process (Evans et al. 2007) – part 2 ............................................... 72

5. Active drawing phase during the design (Lindekens 2004) ........................................................ 81

6. An example of a CityGML code list for city objects (City GML) ................................................ 103

7. Table with Guterres social patterns (Guterres 2004). ............................................................. 106

8. Form and proportions of buildings in different regions (Olgyay 1963) ..................................... 108

9. Design rule formalization on Protégé Axiom Language (Trento 2009) ..................................... 146

10. Class edition on Protégé 2000………………………………………………………………..……………………………….147

11. PEST analysis (Chapman 2005)………. ...................................................................................... 154

13. Attributes applicable to urban formulation (Gil et al 2009) .................................................... 159


x

1.4. Acknowledgements

The author would like to express particular gratitude to José P. Duarte, supervisor of this

dissertation, for the interest, accuracy, and furthermore a high regard for his scientific depth of

outstanding relevance.

The author is also thankful to the City Induction project team members, José N. Beirão

(TULisbon2/TUDelft3) and Jorge Gil (SpaceSyntax4/TUDelft), through the City Induction project 5

(PTDC/AUR/64384/2006), for the engaging discussions and valuable comments during the

development of the research.

I dedicate this thesis in memory of my father

2. http://www.moveonnet.eu/directory/institution?id=PTLISBOA04 3 . http://www.tudelft.nl/ 4 . http://www.spacesyntax.com/

5 . The project PTDC/AUR/64384/2006 was funded by Fundação para a Ciência e Tecnologia and hosted by ICIST (FCT).


xi

Good planning is good urban design (Evans et al. 2007)


xii

1.5. Preface

When, in 2006, Jorge Rocha6 encouraged me to submit a research project on ontologies7 for

funding purposes, I was far from realizing the dimension the challenge that this step implied.

I started my research in urban planning during the Urban Regeneration Master Program

at the Technical University of Lisbon (TU Lisbon) where, by a gentle suggestion of Leonel

Fadigas8, Jose P. Duarte9 accepted to supervise my research, introducing me to a wider

framework then I initially expected. This reality delayed the elaboration of the study, but it has

also provided a deeper research context. In fact, Jose P. Duarte brought up a unique

experience consolidated at MIT10, today widely recognized by the international community as

a hub for the application of technologies to the field of architecture.

Based on his own experience, Duarte proposed me to develop an ambitious project

inspired in his work (Duarte 2007). The idea was simple and innovative: to create, in a similar

way to the one he had devised for mass customized housing (Duarte 2003), a system for

generating design proposals based on contextual criteria, but this time focusing on flexible

urban plans. As in the system that Duarte invented for housing, it was necessary to create a

structure involving three interdependent modules; one to formulate the urban program, one

to generate spatial solutions, and another one to evaluate solutions according to

programmatic criteria. Part of such a process, applied to the urban context, has already been

tested by Beirão and Duarte (2005) with students from TU Lisbon. The pioneering experience

was followed by the presentation of an article at the eCAADe conference in the same year

(eCAADe 2005).

This overall context led to the creation of the City Induction research project (CI), being

the development of the formulation model - one of the project modules - my core task.

Although some papers have already been published and presented at international

conferences on this matter (Montenegro & Duarte 2008) (Montenegro & Duarte 2009), and on

6 . Jorge Rocha is a geographer, and lecturer assistant at the University of Lisbon (UL), Portugal. 7 . Ontology consists in the selected methodology of this research, and is described in the 3

rd chapter. Moreover an ontology is a

data model that helps to support the development of the urban formulation model by providing a common vocabulary for users who need to share information in this specific domain. 8. Leonel Fadigas is an urban planner, landscape architect, and professor at the Technical University of Lisbon (TUL) , Portugal. Fadigas is the coordinator of the Urban Regeneration Master at the same university. 9 . José P. Duarte is an architect, scientist researcher, and professor at the Technical University of Lisbon (TUL), Portugal. Duarte is the coordinator of the City Induction project. His CV is available at: http://home.fa.utl.pt/~jduarte/ 10. The Massachusetts Institute of Technology (MIT) is a private research university located in Cambridge, Massachusetts. USA.

http://en.wikipedia.org/wiki/Private_university

http://en.wikipedia.org/wiki/Cambridge,_Massachusetts

http://en.wikipedia.org/wiki/Massachusetts


xiii

related topics in collaboration with other of the project’s team members (Gil et al 2009)

(Beirão et al 2009), this study comprises a wider research effort on the formulation field.

More precisely, this work corresponds to a first volume that attempts to frame the

context of the urban formulation model by providing its basic ontology. A further volume will

be dedicated to the future implementation of such a model.


xiv

This chapter describes the context,

the problem, and the expected

outcome of the research - The

formulation model and the definition

of its basic ontology.

Chapter 1

1. Introduction


1

formulation urban

programsevaluation generation

designevaluation urban plans

sustainable

1.6. Introduction

This first chapter describes the context, the problem, and the expected outcome of the

research.

1.7. Research context

As previously mentioned, the present study evolved simultaneously to the development of the

City Induction project: a model to formulate, evaluate and generate urban plans. This R&D

project aims at the integration of models areas within urban research; the urban formulation

model, the urban evaluation model and the urban generation model. The basic idea is to

create a full system to generate sustainable11 urban plans12.

The interoperability of such system comprises the elaboration of a common ontology (a

language13 based on ontological descriptions further explained in the 3rd chapter), which will

provide the recursive tool used by the partial models to produce integrated results.

This dissertation concerns a CI partial model: the formulation model14 and the definition

of its basic ontology - a knowledge modelling structure15 to support the development and the

management of urban programs.

Such context helps to clarify the mission of the study.

1. The diagram depicts the basic process of interaction among the three partial modules of the CI project.

However, the CI full model foresees more complex types of interaction.

11. A generally accepted definition of sustainability is described by ‘(…) the development that meets the needs of the present generation without compromising the ability of future generations to meet their own needs’. This definition has three key ideas: development, needs and future generations (Moughtin et al. 2003). 12. The model will be applied at a neighborhood scale, setting a vision for an urban extension at the site scale or within a new neighborhood centre. 13 . Language is a system of signs to express meanings (Chomsky 1965). 14. One will use the terms ‘pre-design’ and the more common term ‘planning’ interchangeably to refer to the formulation of the urban program. 15. In data modeling the task is to organize data so that it represents as closely as possible a real world situation, however feasible in computers’ representation. A data knowledge model encapsulates three main elements: objects’ structure, behavior and integrity constraints (Vazirgiannis & Wolfson 2001).


2

1.8. Problem Definition

Any territory16 has potential that must be recognized and used in benefit of its population. This

seems to be clear. However a large part of the urban problem is today a direct consequence of

an inefficient use of the spatial resources (Sanchirico & Wilen 1999).

By planning space, one can prevent the waste of resources allowing, at the same time,

to maximize the satisfaction of population needs. Planning plays, therefore, a key role in

spatial and social organization (D. Harvey 2009). First, because it defines objectives that clarify

the mission of the territory, and second because it establishes levels of effectiveness and

efficiency by implementing measures to attain defined goals (Drucker 2007).

However, concerns related with urban planning surpass today the exclusive satisfaction

of local needs. Today, there is the common belief that any population, in a limited space,

contributes towards a balance at a higher scale (Hirst et al. 1996). This scale, that I will call

global ecosystem17, seems to affect each portion of the territory, at the same time as each

territory’s portion also seems to contribute to the global ecosystem (Pieterse 2009) - ranging

from globalized financial markets (Lin & Mele 2005) to climatic global warming (Houghton

2005). In effect, humans pay today, for the first time in history, the global air that they

breathe18 (Hoel 1991).

The perception of this interdependence of scales is relatively recent in history, and

current planning instruments are still not prepared to act in such a complex level (Terrados et

al. 2007). In fact, there it seems to be a lack of mechanisms for providing the participants of

the planning process with a set of efficient tools integrating multiple scales in order to respond

adequately to current needs. This means that it is not enough to develop an instrument

exclusively focused on the optimization of local resources (Buyya et al. 2005). It is necessary to

create one that can be also capable to surpass local scale towards a more global

consciousness. The diagram 2 depicts the timeline sketch of these changes.

A computational platform, I would argue, can facilitate the creation and the

management of such a more complex planning instrument.

16. Territory is a geographic area under control of a single governing entity such as state or municipality; an area whose bord ers are determined by the scope of political power rather than solely by natural features such as rivers and ridges (Britannica & inc. 2002). 17. An ecosystem is a system combined of organic and inorganic matter and natural forces that interact and change, intricate together by chains and cycles. It’s a sum greater than its parts. Its complexity and dynamism contribute to its productivity, but make it challenging to manage (Rosen 2000). 18 . CO2 international taxes and tradeable quotas (Hoel 1991).


3

timeline

site

(local needs)

site

surrounding areas

global

(concerns at the planet scale)

site

surrounding areas

(concerns with surrounding areas)

2. Change of planning paradigms along time. First, a focus on local needs, then on spatial relations

established between site and surrounding areas, and finally, a focus on a more global scale

This overlook constitutes a brief of a vast and complex context that will be addressed in the

next chapters of this study. Meanwhile, it is useful to define some of the key concepts used in

the study.

EFFICIENT URBAN PROGRAMS: First is crucial to increase the existing levels of

efficiency in the management of spatial resources in order to improve the quality of

life of the population. Therefore is important to plan space throughout the

development of efficient urban programs in order to attain defined goals. This is the

first problem that this study aims to solve. However, is not the only one.

SUSTAINABLE URBAN PROGRAMS: The current urban paradigm is recent and strongly

related to a more global consciousness of urban problems. This means that it is

necessary to create a tool for supporting the development urban programs from this

particular perspective.

URBAN PROGRAMS BASED ON A COMPUTATIONAL PLATFORM: Finally, it is

important that such a tool, capable of generating efficient and sustainable urban

programs, can be supported by a computational platform. Such platform allows one

to act in a simpler and partially automated way, thereby enabling the planning

participants an easier management of the contextual data.

The main argument that motivates the development of this study is therefore:

The creation of an efficient model for developing and managing sustainable urban

programs, supported by a computational platform (the formulation model).


4

Although based on basic principles, the initial mission of this study is far more complex. Urban

planning deals with extended variables (Mabert et al. 2003), becoming difficult to establish the

right ingredients to develop urban programs (Jabareen 2006). One way to solve the amount of

information is to clarify it by creating a knowledge model called ontology (Gruber 2005) that

permits to organize an extended database, relating all its components. Another

complementary way is to describe the phase of the design process where urban formulation

occurs - the pre-design phase (Best & De Valence 1999), to reveal the way programs are

usually developed.

To formulate urban programs is, therefore, crucial to organize a resourceful database,

and understand how pre-design evolves within the design process (Lawson 2006).

The title of this study - Building a Pre-Design Ontology - is a direct result of two research

issues: a) the pre-design phase, and b) the ontology. However is important to keep in mind

that the core subject behind those two issues is the formulation of sustainable urban

programs. One will call this process Formulation Model (FM), and its development is the core

of the present research. The diagram 3 depicts the frame of these main issues of the research.

3. The core matters of the research: the pre-design phase, the formulation model, and the ontology

1.9. Proposed methodology

It was mentioned above that the most efficient method to solve the problem of managing the

extended data that is necessary process in the formulation model is through the development

of an ontology (Gruber et al 1993). However, there are two different types of methodology to

Formulation Model

creation of urban

programs

Pre-Design Phase

Ontology

knowledge database


5

describe the formulation model, corresponding to different scales of approaching the research

problem:

a) the first, is the basic methodology that frames the overall process, that defines the

core path for the work, and

b) the second, is the applied methodology (an ontology), created in order to solve

specific problems of the process (the formulation model process), as for example data

management and definition of formulation rules.

In this introductory chapter is important to describe the basic methodology of the research

developed to accomplish the desired goals. The applied methodology will be described in the

3rd chapter. The basic methodology equates two structural concepts of the planning process:

a) urban complexity19 (Healey 2007), which represents the main problem that this study

aims to solve, and

b) the quality of life of the population (QOL)20 (Maclaren 2004) that represent the main

goal of the formulation model.

Such a methodology requires the creation of a system capable to uncover the urban

complexity (the global problem) and its codification into a coherent set of rules that can be

used for producing plans that ensure the quality of life of urban communities (the global aim).

The solution for such a problem encompasses two steps: 1) to generate successful programs

(formulation model) and, 2) to generate successful plans.

The first corresponds to a pre-design phase, which is the aim of this study, and it can be

diagrammed as follows:

19 . ‘Complexity is the physical fact of problems existing at multiple scales simultaneously. Complex systems solve these problems by adopting geometric structures that have structure at multiple scales simultaneously, that is to say fractal geometry. The architectural scientist Christopher Alexander elaborated on the link between fractal geometry and life by defining the theory of centers, which are parts or features that are distinguishable from the whole and cooperate with the whole to survive in the complexity of the universe. Because centers are themselves made of centers, they fit the recursive definition of fractals. Most important of all, complex structures can only be made through generative processes that draw from a previous step, repeated infinitely. The science of complexity is thus focused on discovering how things are produced, their final form being far too complex for one mind to fully grasp’ (Helie 2009). 20 . A Quality-of-Life (QOF) concept is: ‘The approach to the measurement of the quality of life derives from the position that there are a number of domains of living. Each domain contributes to one's overall assessment of the quality of life. The domains include family and friends, work, neighborhood (shelter), community, health, education, and spiritual ’ - The University of Oklahoma School of Social Work (Notes on 'Quality of Life' Website).

http://en.wikipedia.org/wiki/Fractal_geometry

http://en.wikipedia.org/wiki/The_Nature_of_Order

http://en.wikipedia.org/wiki/The_Nature_of_Order


6

4. Basic diagram: conceptual outline

1.10. Concerns involving the creation of the formulation model

In the way is understood today, research should contribute towards society (Fritsch 2004). The

evaluation of such contribution is generally located between the purpose and the relevance of

the thematic field under study. In the case of this research, the best way to start exploring it is

by answering a simple question:

Why create a formulation model?

Since the dawn of civilization, humans have made cities to support their societies. Although

these urban settlements have been a source of progress, they have never been totally

understood, relying on traditions and trial-and-error processes for their expansion. Attempts

at planning and bringing them under the control of a planner have not entirely resulted yet

(Hélie 2009). However, there is the relatively common belief that planning can be efficient in

the sense that it can manage spatial resources to provide for QOL.

In fact, the main work of planning, embodied by a large quantity of rules and codes

(building, zoning, etc.), is essentially algorithmic21 , and made up of if-statements. - If

something: do this or that. So in this sense, creating the formulation model is a matter of

developing protocols for how a city will grow (Helie 2009). But protocols must to be built upon

21 . ‘In mathematics, computing, and related subjects, an algorithm is an effective method for solving a problem using a finite sequence of instructions. Algorithms are used for calculation, data processing, and many other fields. Each algorithm is a list of well-defined instructions for completing a task. Starting from an initial state, the instructions describe a computation that proceeds through a well-defined series of successive states, eventually terminating in a final ending state’ (Blass & Gurevich 2003). 21a. ‘In computer systems, an algorithm is basically an instance of logic written in software by software developers to be effective for the intended "target" computer(s), in order for the software on the target machines to do something. For instance, if a person is writing software that is supposed to print out a PDF document located at the operating system folder "/My Documents" at computer drive "D:" every Friday at 10PM, they will write an algorithm that specifies the following actions: "If today's date (computer time) is 'Friday,' open the document at 'D:/My Documents' and call the 'print' function". While this simple algorithm does not look into whether the printer has enough paper or whether the document has been moved into a different location, one can make this algorithm more robust and anticipate these problems by rewriting it as a formal CASE statement or as a (carefully crafted) sequence of IF-THEN-ELSE statements’ (Kleene C. & Kleene C. 1936).

complexity of urban space

• global problem

urban programs

quality of urban life

• global aim

pre-design phase

design phase

plan

http://en.wikipedia.org/wiki/Mathematics

http://en.wikipedia.org/wiki/Computing

http://en.wikipedia.org/wiki/Effective_method

http://en.wikipedia.org/wiki/Calculation

http://en.wikipedia.org/wiki/Data_processing

http://en.wikipedia.org/wiki/Computer_systems

http://en.wikipedia.org/wiki/Logic

http://en.wikipedia.org/wiki/Software

http://en.wikipedia.org/wiki/Software

http://en.wikipedia.org/wiki/PDF

http://en.wikipedia.org/wiki/Computer_drive

http://en.wikipedia.org/wiki/Friday

http://en.wikipedia.org/wiki/Printer_%28computing%29

http://en.wikipedia.org/wiki/Case_statement


7

theoretical models in order to support a development vision for a site or a region. That’s why

the formulation model is so extremely dependent on the type of paradigms on which is built-

on (Clark 2000). If the paradigm fails so fails its program. Chapter 2 give an idea about the

relevance of urban paradigms for the development of cities, within a large sort of perspectives.

In the effort to understand the impact of urban paradigms on cuty growth, one even needs to

consider those paradigms that aim to demonstrate the advantages of the absence of planning

methods (Mintzberg 2000), by defending the emergence22 of urban morphologies along time,

as a preferable option for urban space (Burgess & Park 2002).

One fact seems to be clear. There are several models of urban programs used in the

creation of urban plans. It seems also clear that some have failed tremendously23; and others

have lacked implementation, remaining as theoretical guides24. Still, its common flaw appears

to be the absence of crucial urban matters like sustainability factors. This has resulted,

systematically, in the creation of inappropriate plans that are far from satisfying the necessities

of the urban populations, and far from making an appropriate use of site features. But there

are prime questions for such a framework.

What are the main concerns behind the idea of producing a plan?

The task of creating a good plan in order to increase the quality of urban life seems to be

relatively easy at a first glance, by simply producing a full and resourceful program, built

according to urban codes, regulations, and standard parameters. However the problem is

more complex. Other urban programs have taken upon that task and have failed in the

implementation of the plan. Creating a plan seems to be similar to the task of creating a

language (Deacon 1998), or even to use a new language, requiring an additional effort to

understand its new rules. In natural language (linguistics) there is an interaction between two

crucial components: the semantics (the ideas), and the syntax (the form according to which as

ideas are organized) (Chomsky 2002). Connecting the two, in order to create logic, is an

22 . ‘Emergence is the creation of systems of greater dimension than the elements that create it, sometimes also called self-organization, through the application of localized rules of action. The most elementary emergent systems are the binary, one-dimensional cellular automatons studied by Stephen Wolfram that create complex fractals when shown in two dimensions. Emergence is also behind all forms of multicellular life, the cells of a plant or an animal following the instructions coded in their DNA to organize themselves into a much bigger organism. Those organisms will then also create emergent structures by following simple rules of action, like the termite cathedrals often used as an icon for emergence. Emergence is also behind human societies, from the invisible hand of economics (invisible because it is a dimension greater than any one of us) to the astonishing grow th of the Internet and later of Wikipedia. Studying the rules that enable emergence will allow us to build the systems to deal with the complexity of the universe (…) Urbanity is the cooperation and mutual-support of large numbers of people in close proximity. It is inevitably emergent, and to understand the science of emergence is the key to inventing the first fully emergent urbanism, capable of resolving all the complexities of a 21st century, sustainable city‘(Helie 2009). 23 . See a further explanation of the CIAM Athens charter’s principles, in the 2nd chapter. 24 . See a further explanation of the Alexander’s pattern language (1977) theory, in the 2nd chapter.

http://en.wikipedia.org/wiki/Cellular_automaton

http://en.wikipedia.org/wiki/Image:Termite_Cathedral_DSC03570.jpg


8

enormous task, partially because the urban language (or the planning language) is not the

natural language of human beings. Understanding and clarify it needs a supplementary

research effort. However, this does not constitute the unique reason to envisage a good plan.

What is then behind a proficient urban program?

Embedding more knowledge into an urban program could be one of the answers. It is

recognized that the urban environment is extremely complex because it involves an endless

collection of matters and subjects, ideas, time, capital, and technology. All these subjects are

too vast to compile and describe in short terms. Some studies took more than ten years and

still have great difficulties in the implementation of the methodology. The first intuition is that

such a task will never be accomplished. But the other preferable conclusion is that the task

could be in fact achieved by a detailed survey focused on urban core concepts through a

sequence of judicious steps. It is important to understand how such steps are processed and

under which matters they rest. There are two ways of achieving this.

The breadth and depth of such core concepts is further developed in the Chapter 2,

describing the particular occurrences of recent history, and the impact it had on former urban

programs. Chapter 2 is, thus, as an extension of the description of the problem definition just

presented.

1.11. Expected outcomes and future work

The objective is to create a formulation model that encloses two important aspects:

a) One is the development of an ontology that is necessary to describe the urban

space; first the context, and later the context with the solution, and which is also

necessary to capture the structure of the planning framework.

b) The second aspect is the description of the set of rules that describe the solution.

This goal of this study is closer to the first aspect - the development of a sketch of an ontology

to describe the formulation model.

In summary, one can say that this study provides the basic theory and some of the basic

instructions for the functioning of the formulation model. Future work will concern the

development of the mechanism that gives operability to the formulation model. There are

relevant motives to proceed in such a sequence.


9

Starting by creating a mechanism for a purpose whatever, without understanding its

wide contextual framework induces often, the creation of efficient laboratory prototypes. This

happens mainly because such prototypes are developed within a very limited context, with

rules that are only efficient in such limited laboratory environments (Pickering 1992).

Unfortunately, such prototypes are in general inefficient when dealing with real world

problems.

One of the main objectives of this research is precisely to capture the rules of interaction of

urban phenomena in its wide context. The best way to trail such an objective is by start digging

in the formulation framework in order to describe its basic structure. Thus, the study

comprises two tasks:

a) the first task is trying to understand the way formulation deals with a large

amount of matters,

b) the second task is to disclose the characteristics that can make it efficient when

dealing with real urban plans.

The complexity involved in the creation of the formulation mechanism25 is somewhat different.

The specific methods and techniques required for its development demand it to be addressed

in subsequent research. Future work will be based on the theory and the set of instructions

proposed by this research, to make the model amenable to computer implementation. The

goal is to sketch the prototype of such an implementation.

How it will be made?

The formulation model will be encoded into a description grammar26 (Stiny 1980) to establish a

protocol with the generation model27 (shape grammars) of the CI project, which is being

developed according to this theory28. The advantage of using grammars is that it provides the

means to encode a certain degree of automated reasoning. One of the additional tasks is to

allow the model to function with other formats, notations or platforms, in way to provide an

ample base for a computational implementation. An ontology editor seems to be one of the

model’s preferable platforms, because it can easily build up a bridge between the structure

and rules of the model. In fact, some of the ontology software editors possess today a

25 . The formulation language will be the formulation operational tool developed to act in real or simulated urban contexts, supported by a computational platform. 26 . Description Grammar is a context-free grammar specialized for mathematical formulae. 27 . The generation model of the City Induction R&D project 28 . In a similar way it was used in Duarte mass customized housing.


10

procedural framework to develop rules29, and also protocols for design solutions (Trento

2009). The straight linking process between data-taxonomic-structure (ontology class editor)

and data-description-rules (ontology rules editor) allows one to eliminate part of the hard task

of translation that usually occurs when is necessary to transfer information between different

platforms.

Following the encoding framework the model will be implemented and tested. This will

be achieved by structuring the ontology, so that extensions or refinements of the model will be

accomplished by adding or changing parameters, aiming at a flexible and sustainable data

management. The idea is that the creation of the urban formulation model can be improved by

using one or more case studies. In summary, the division of content between the present and

the future work corresponds to the sequence shown in the diagram 5 as follows:

5. Formulation research outline

1.12. Organization

The current research is divided into a sequence of stages, each representing a defined task

designed to achieve a specific goal and described in a specific chapter, as follows:

29 . ‘A ‘Knowledge Structure’ (KS) is composed of a set of Entities (…) dependent on a set of ‘Rules’.‘Rules’ can be classified in: • Reasoning Rules and Algorithms: formal codes for analysis, checking, evaluation and control of concepts associated to specific entities with inferential procedures of ‘If-Then’ type. • Codes, Laws and in force Rules: context dependant rules referred to the in force law that will become constraints for the entities which they are related to; • Consistency Rules: algorithms to check the consistency of values, parameters, attributes, instances, relationships and prop erties referring to the specific meanings associated to each entity in the specific context on which it is used; • Traditional Rules: non-formalized rules, practices and concepts that represent part of the reasoning process of each actor on his own specific disciplinary domain during the design process. By means of Inference Engines able to match rules among the ontologies - all of which formalized into a syntactically coherent IT structure - a deductive layer allows the designers to use in a coherent manner different levels of abstraction, or to exploit a conceptual interoperability (Calvanese D. et al, 2008). The dynamic and semantically-specific representation detecting incoherent/favourable situations by means of a constraint rule mechanism can allow them to be highlighted and managed in real time (Figure 3). At the same time it allows actors to make alternatives, more consciously reflecting on the consequences of their intents. In this way the impact of a networked ontology based system can make actors more aware of overall design problems, helping them in operating more participative and shared choices. ’ (Trento 2009) (continue. see annex 1 of this research)

volume 1

formulation 'theory and basic instructions'

•descriptive (present work)

volume 2

formulation 'machine'

•operative (future work)


11

1st Chapter – Introduction - The first chapter describes the context, the problem, and

the expected outcome of the research - The formulation model and the definition of

its basic ontology.

2nd Chapter – Precedents - The contribution of this chapter is 1) to supply the context

that leads to the necessity for elaborating a platform for urban programs and 2) to

define the essential qualities of the urban design paradigm that such platform needs to

support. The text describes theories and paradigms that implicit particular visions of

how a city must be organized and, therefore, the specific forms of designing it that

influence the substance of urban programs and the form of formulating them.

3rd Chapter – Methodology - This chapter describes a methodology for finding a

solution for the following problem: How to develop a formulation model for urban

programs? The creation of the formulation model requires the development of an

ontology that can be used to describe the urban space; first the initial context, and

later the context with the solution. The ontology, a knowledge database model, is also

important to reveal the way in which urban programs can be structured. The

contribution of this chapter is, therefore, to describe an urban ontology, which will

provide the basis for supporting the development of the formulation model.

4th Chapter - Conceptual and Formulation Models - The objectives of the fourth

chapter are: the description of the basic conceptual model and the description of the

three parts of the formulation model: b1) the input, b2) the mechanism and b3) the

output.

5th Chapter - The Formulation Process - The contribution of the fifth chapter for this

research is: to describe the formulation process as well as the design phase where it

occurs – the pre-design phase. The main objective is to provide a better understanding

of the formulation framework and, at the same time, to generate the sketch of its

semantic ground, that is, a consensual vocabulary to enable appropriate specifications

(or the “communication acts”) towards design solutions.


12

6th Chapter - The Sketch of a Planning Language - Language, like a seed, is the genetic

system which gives our millions of small acts the power from the whole. The chapter 6

explores the most important category of the formulation model: the system language

that is used by planners to formulate urban solutions. In summary, planner’s language.

7th Chapter - Conclusion


13

complexity of urban space

• global problem

urban programs

quality of urban life

• global aim

pre-design phase

design phase

plan

This research equates two core concepts of the planning process: a) The urban complexity, and b) The quality of life of the population. The goal is to create a system capable to uncover urban complexity (the problem) codified in a coherent set of rules that can be used to produce plans that ensures the quality of life of urban communities (the goal) – as depicted in diagram 6.

6. Basic diagram: conceptual outline

The title of this study - Building a Pre-Design Ontology - is a direct result of two research issues: a) the pre-design phase of the planning process where formulation occurs, and b) an ontology (a methodology that correspond to a data model). The core subject behind these two issues is the formulation of sustainable urban programs. The following diagram 7 shows these related matters in an interactive model.

7. The core matters of the research: the pre-design phase, the formulation model, and the ontology

The main goal that motivates the development of this study is the creation of a model for developing and

managing sustainable urban programs, supported by a computational platform.

01

•research mission

Formulation Model

creation of urban

programs

Pre-Design Phase

Ontology

knowledge database


14

The contribution of this chapter is 1)

to supply the context that leads to

the necessity to elaborate a platform

for urban programs and 2) to define

the essential qualities of the urban

design paradigm that such platform

needs to support. The text describes

theories and paradigms that imply

particular visions of how a city must

be organized and, therefore, the

specific forms of designing it that

influence the substance of urban

programs and the form of

elaborating them.

Chapter 2

2. Precedents


15

2.1 Introduction

The contribution of this chapter is:

1. to supply the context that leads to the necessity for elaborating a platform

for urban programs and,

2. to define the essential qualities of the urban design paradigm that such

platform needs to support.

The text describes theories and paradigms that imply particular visions of how a city must be

organized and, therefore, the specific forms of designing it that influence the substance of

urban programs and the form of elaborating them.

2.2 The precedents

An overview of the spatial transformations30 that led to current urban environments allows

one to understand how their present problems emerged. The objective is to take into account

the course of urban actions in recent past to establish better solutions for future planning

actions. This fact invokes the need to take a closer look at the space and time mechanism to

extract the elementary laws for formulating urban programs.

Why?

Because urban programs can be reinforced by laws that respond efficiently to typical

(sometimes recurrent) occurrences of space. This leads one to a pathway. The best way to

extract laws - due to long-term process of urban transformations - is by depicting the context

of the recent historical events.

First, one will try to disclose a critical event for urban communities - the European chain

of revolutions in the 18th century.

In the 18th century European society changed dramatically. The chaotic city growth that

followed the rural exodus caused by industrial revolution created the need for regulating such

growth to prevent its flaws. However, such changes have deeper origins. In fact, the extensive

mutation of political and geographic configurations that occurred in the modern era stemmed

30. Correspond to urban transformations, as well as the hybrid ones that are at the origin of the urban formations - even if not totally considered as urban.


16

initially from the consequences of the French Revolution of 178931. In this period, the

foundation of the illuminist spirit and the Newtonian positivism transformed the organic

structures of states into open systems, promoting the principle of equal opportunity, within a

new democratic political framework. This paradigm had the following shortcomings; a cycle of

social convulsions that shook European societies unchaining revolutionary movements,

controlled by an intellectual nucleus located in France (Lefebvre & Evanson 1962). Territorial

modifications were then largely implemented, and the birth of the “urban government”

originated a new political order. With the free-ground concept, and the destruction of

dominant models (monarchy and nepotism)32 , the territorial borders of new states in

formation build up drastic alterations within a new political and administrative space,

sprouting new ownership relations, based on a new property domain outline. Property

appeared then associated to an omnipresent battle for territorial ownership imposing the

urgency to implement new laws, in order to control spatial transformations (Rossi 1984).

This context was rapidly amplified by the technological dimension of the industrial

revolution (Harvey 2006) which was commanded by private-initiative model, oppressing the

ideals formed in the French Revolution of 1789. The way communities survived in the

sprawling cities created by the industrial revolution evoked the necessity of creating an

embryonic figure of urban government towards the implementation of laws and rules, in order

to organize the urban environment (Rodrıguez et al. 2001, 415).

Although the reaction to territorial flaws imposed the creation of new forms of

government, the 20th century was marked by a quiet urban expansion developed without a

critical support of communities. According to David Harvey (2006) “perhaps the chief sin of the

twenty-century was that urbanization happened and nobody much either care or noticed in

relation to the other issues of the day judged more important.” The establishment of

participative urban political measures was therefore misled, according to Harvey, by self

31 . The French Revolution of 1789 was the ‘Revolutionary movement that shook France between 1787 and 1799 and reached its first climax there in 1789. Hence, the conventional term “Revolution of 1789,” denotes the end of the ancien régime in France, serve also to distinguish that event from the later French revolutions of 1830 and 1848. Although historians disagree on the causes of the Revolution, the following reasons are commonly adduced: (1) the increasingly prosperous elite of wealthy commoners—merchants, manufacturers, and professionals, often called the bourgeoisie—produced by the 18th century’s economic growth resented its exclusion from political power and positions of honour; (2) the peasants were acutely aware of their situation and were less and less willing to support the anachronistic and burdensome feudal system; (3) the philosophes, who advocated social and political reform, had been read more widely in France than anywhere else; (4) French participation in the American Revolution had driven the government to the brink of bankruptcy; and (5) crop failures in much of the country in 1788, coming on top of a long period of economic difficulties, made the population particularly restless’ (Britannica & inc. 2002). 32 . Nepotism, in politics, is when the relative of a powerful figure ascends to similar power seemingly without appropriate qualifications.

http://www.britannica.com/EBchecked/topic/178400/economic-growth

http://www.britannica.com/EBchecked/topic/467681/political-power

http://www.britannica.com/EBchecked/topic/456709/philosophe

http://www.britannica.com/EBchecked/topic/495327/reform

http://www.britannica.com/EBchecked/topic/617805/American-Revolution


17

exclusion of the critical mass of urban communities who avoided facing the extension of the

urban problem. The result was an absence of efficient politics in urban governance, hence in

plans.

Despite the absence of communitarian participation, some European elites tried to

develop new theories for organizing urban space, aiming to change social ideals. In 1933, with

the Athens Charter published at the 5th CIAM 33 (Congrès International d’Architecture

Moderne), the paradigms although seaming innovative and ideologically perfect to respond to

a lack of collective beliefs, led to an uncontrolled sprawl, induced by mono-functional zoning

program, which created a set of negative impacts on the urban environment. Several of the

contemporary urban problems are still an indirect consequence of that paradigm or, as some

modernists defend, is the result of an inefficient interpretation of the Charter’s philosophy.

In the 1960’s, the Athens Charter began to be criticized, and urban communities

embarked on a discussing of the post-war urban paradigms, mainly represented by a mono-

functional model that had abundant implementations. In his book “framework of the invisible

city” Mumford (1968) depicts a vision of such a model - a city built upon an impersonal “net,”

essentially functional, characterizing an urban “Megamachine” that grows over nature by a

dominant expansion of human activities. Jane Jacobs was a top figure of the sociologic

movement that was spreading in opposition to the modernist vision. Some of her concepts

concerning security, social cohesion, and communitarian participation (Jacobs 1961) are later

embedded in several urban codes and programs.

Amidst the critics of the “urban modernism” emerged a series of studies led by

Christopher Alexander where the most known is A Pattern Language, (1977) which combined

the design process with genetics34 and linguistics theory (Chomsky 1965). Its concepts mainly

react to the repercussions of the zoning model that was embedded in the CIAM vision,

33 . ‘CIAM's early attitudes towards town-planning were stark: "Urbanization cannot be conditioned by the claims of a pre-existent aestheticism; its essence is of a functional order… the chaotic division of land, resulting from sales, speculations, inheritances, must be abolished by a collective and methodical land policy’ (CIAM). 34 . ‘- The idea that materialized in the published pattern language was first of all, of course, intended just to get a handle on so me of the physical structures that make the environment nurturing for human beings. And, secondly, it was done in a way that would allow this to happen on a really large scale. And, what I mean by that is that we wanted to generate the environment indirect ly, just as biological organisms are generated, indirectly, by a genetic code. Architects themselves build a very, very small part of the world. Most of the physical world is built by just all kinds of people. (…) How could one possibly get a hold of all the mass ive amount of construction that is taking place on Earth and, somehow, make it well, that means let it be generated in a good fashion and a living fashion. This decision to use a genetic approach was not only because of the scale problem. It was important fro m the beginning, because one of the characteristics of any good environment is that every part of it is extremely highly adapted to its particularities. in (The Origins of Pattern Theory - The Future of the Theory, and The Generation of a Living World, by Christopher Alexander, in a presentation recorded live in San Jose, California, in October of 1996, at The 1996 ACM Conference on Object-Oriented Programs, Systems, Languages and Applications (OOPSLA)) (Origins of Pattern Theory website).


18

proposing a set of organized ideas related with the quality of the communitarian spaces, and

the advantage of functional mixing applied to urban programs. The book describes a “planning

language” designed to be applied across different scales, by applying specific patterns, in a

sequence of steps, in a creative design process. The book encodes clear ideas about the quality

of life of urban communities and gives instructions for how to achieve it by surveying urban

problems and setting solutions for them. However, the Pattern Language theory, in contrast to

the Athens Charter, had very limited implementation and planners had difficulties in applying

the envisioned planning method. The results of the scarce implementation of the proposed

language seem to be, in opposition to an axiom of the theory (the timeless way), recurrently

stylistic and dated35.

In the meanwhile, city growth and organization was strongly influenced by information

technology. The “Informational City” presented in the “Theory of Space Flows” (Castells 2004),

contributed for a refreshed vision of the organization of contemporary society by depicting it

as a network society. The flows within a city referred by Castells “do not represent only one

element of the social organization: they represent the processes that dominate our economic,

symbolic, and politic life.” These invisible nets of communication, associated to the urban

dispersion and the accumulation of information, led to form urban constellations like the

North-American Edge-Cities (Garreau 1998). These cities encompass a group of incorporeal

connecting networks (technology and software), as well as material nets (highways and roads).

The interconnection of those two nets has originated a chaotic urban development. This

chaotic growth presents difficulties to the recognition of patterns. However and according to

James Gleick, (1987) the chaos can be object of study, using the “irregularity patterns” concept

based on dynamics theory.

The end of the traditional city was then announced as a victim of the deindustrialization

and the depletion of local resources. It represents one of the deepest alterations in

contemporary urban systems, that led to an increasing urban sprawl36, defined by a territorial

35 . ‘- Although we intended that the pattern language would be generative, that is, would allow people to generate buildings and building designs, for themselves - truthfully, this does not happen. The patterns provide many profound ideas, and geometrical "nuggets": which are needed to make the environment work. But, as written in 1977, they do not actually allow a person to generate a good design, step by step. They do not place the emphasis on morphological unfolding, as they should’. (Pattern Language author website). 36 . Sprawl: ‘A haphazard and disorderly form of urban development. There are several elements that characterize sprawl: a) Residences far removed from stores, parks, and other activity centers, b) Scattered or “leapfrog” development that leaves large tracts of undeveloped land between developments, c) Commercial strip development along major streets, d) Large expanses of low-density or single use development such as commercial centers with no office or residential uses, or residential areas with no nearby commercial centers, e) Major form of transportation is the automobile, f) Uninterrupted and contiguous low- to medium-


19

dystopia37 (Harvey 2006) that generates a grid of distant economic free markets and sets out

an invisible net that feeds some top-regions more technologically developed (Castells 2000),

thus promoting an exclusion of the less developed ones. This fragmentation could have been

prevented by the State in an early phase of reaction to such a powerful urban model, however,

the State played a limited role (Stanlake & Grant 1994) facing the hegemony of monopolist

companies, leading to the fall of the state-regulator figure, which dominated traditional cities

before the last post-war (Harvey 2006). The end of the traditional city becomes hence a spatial

transformation factor, where fragmentation emerges as a result of a continuous adaptation to

a spatial model in constant development (Castells 2005). These dynamics appeared partially as

a consequence of a dispersive free market model, in clear opposition to the previous

industrial-localization. Quoting previous works, David Harvey (Harvey 1995:44) refers that

capitalism as a production factor was specially developed to break spatial barriers, speeding up

the “time” factor, with the intention of an exclusive accumulation of capital. Urban economy

seemed then to influence crucial factors in the development of cities, generating new urban

phenomena. The concept of privatization of the communal space and the lack of democratized

access to cities, developed in “Splintering Urbanism” (Graham & Marvin 2001) is directly

associated with the phenomena of social exclusion and stigmatization. This effect is well

represented by the collapse of the traditional city of exchanges and interdependent markets,

supplied by the neoclassical economy, and inspired in the prosperity of the organic and multi-

functional medieval society. Salvador Rueda (1998) evokes this urban phenomenon concluding

that it conduces to the death of Cities; “a planificación funcionalista y el mercado van creando

espacios exclusivos según los niveles de renta, creando de nuevo un puzzle territorial,

desconectando el tejido social y diluyendo el sentido que tiene la ciudad como una civis”. The

contemporary city seams therefore to become a consequence of a globalized community,

generating phenomena of inclusion and exclusion, deeply rooted in its particular economy and

in strong technological development.

Other theories described the welfare-state as a model that sprouted a group of

bureaucratic instruments in an attempt to solve the urban expansion phenomenon, assuring

density (one to six du/ac) urban development g) Walled residential subdivisions that do not connect to adjacent residential development’ (Evans et al. 2007). 37 . A dystopia (from the Greek δυσ- and τόπος, alternatively, cacotopia, kakotopia, cackotopia, or anti-utopia) is the often futuristic vision of a society in which conditions of life are miserable and characterized by poverty, oppression, war, violence and/or terror, resulting in widespread unhappiness, suffering, and other kinds of pain. (Harper & Harper, Douglas n.d.)

http://en.wikipedia.org/wiki/Poverty

http://en.wikipedia.org/wiki/Oppression

http://en.wikipedia.org/wiki/War

http://en.wikipedia.org/wiki/Violence

http://en.wikipedia.org/wiki/Fear

http://en.wikipedia.org/wiki/Depression_%28mood%29

http://en.wikipedia.org/wiki/Suffering

http://en.wikipedia.org/wiki/Pain


20

an accomplishment of a classic planning dichotomy - “agreed goal” and “known

technology”(Christensen & Bang 2003); a perfect functional and closed system. This system,

supported and strengthened by the modernist ideals, defined a rigid set of goals and

techniques in order to implement plans. Christensen defends that such dichotomy, although

effective during a specific period of time, falls upon social dynamics, due to its relative

inflexible administration. A group of concepts defined in the “Governance of Europe’s City

Regions,” highlights the need for the implementation of flexible policy in order to produce

more efficient plans, acting as a reaction to the classic welfare-state conceptions. In fact, the

changing nature of regions moving from fixed territory (spatial container) to regional groupings

of specialized production clusters, requires flexible policy responses (Herrschel & Newman

2002). Flexible plans appeared therefore to act as the natural reaction to the dynamic

character of the contemporary urban reality, guaranteeing a balance between the public and

the private sectors (within their particular interests), towards a “common good” and “public

interest” (Healey 2007). Plans can thus be executed efficiently (Christensen & Bang 2003) by

implementing flexible models supported by local communities, acting as a protective shell

against the unpredictable dynamics of the urban space.

2.3 Conclusion

In summary one can say that until the 19th century the problem was situated within geographic

borders, and after the 60’s decade of the 20th century concerned a much different subject - the

collapse of the traditional city, and a change imposed by market laws, the zoning concept, and

the free economy model, promoting an endless functional grid involving the expansion of the

urban structure. It is also manifest that at the beginning of the 21st century, another core

discussion appeared on the research bench; the development of an abstract “urban net”

within a new theory of space - “the informational net” (Castells 2003) - an invisible network

connecting new urban space formations. Hence, while prior to the 19th century the urban

phenomenon was related essentially with the physical and visible aspects of property (Rossi

1984), the post-war conceptions appeared to involved a more complex and dynamic problem -

the difficulty of a physical recognition of the urban structures in expansion (Portas et al. 2003:

221); partially attributed to the spreading of an incorporeal society network (Castells 2005)

and to a specific economic model. The immateriality of the contemporary urban space is, in

the words of João Ferrão (Portas et al. 2003), part of the “real city today”; an unrecognized city


21

due to the fact that it appears to be morphologic and politically invisible. More recently

numerous group of researchers are pointing out to climatic factors and energy resources as

key issues of urban planning, responding to increasing global concerns as are the climatic

changes (Houghton 2005).

Throughout all these periods, the concerns of planners with solving urban problems

changed dramatically; and several experiences on the field, following new urban theories (as in

the CIAM vision), caused even more problems, creating a sense of a global collapse. Despite all

efforts and failures, important planning concepts remained more or less consensual;

1. The first concept represents a repercussion of the Industrial Revolution,

establishing the need to regulate urban space to guaranty the quality of urban life

- urban goals, rules, and guidelines.

2. The second concept describes the need for implementing a more

multifunctional and organic urban system, refuting the hegemonic economic

model of the dispersive free market, and the modernist CIAM vision - mix-uses/

social space/ participation.

3. A third concept describes a flexible system within urban plans, capable of

absorbing the social mutations, reacting to the unpredictable dynamics of the

urban phenomena - flexible programs,

4. The fourth concept describes the relevance of the sustainability factor, as a

balance between all factors of the urban phenomena, including the economic,

social, energetic and climatic factors - sustainable programs.

As a result, today is more or less consensual that urban programs should: a) be regulated by

goals, rules and constraints, b) be multifunctional, c) be participative, d) be flexible, and e) be

sustainable.

These are the features that the study City Induction project tries to respond to and this

research is concerned with the development of a formulation model according to the same

principles.


22

It is more or less consensual that urban programs should: a) be regulated by goals, rules and constraints, b) be multifunctional, c) be participative and collaborative, d) be flexible, and e) be sustainable. These are the features that the formulation model tries to respond to.

The diagram 8 shows the interaction of the main laws of urban programs depicted in this chapter.

8. The main laws of urban programs

Urban programs core structure:

1. Rules and Constraints | goals, rules and constraints

2. Multifunctionality | diversity and mixture

3. Participation | involvement of communities

4. Flexibility | strategies for future actions (change of uses and concepts)

5. Sustainability | environmental and social philosophies

02

• urban programs - the main laws

participation and collaboration

goals, rules and constraints

flexibilitymultifunctionality

sustainability


23

This chapter describes a

methodology to find a solution for

the following problem: How to

develop a formulation model for

urban programs?

The contribution of this chapter is to

describe an ontology, which is the

selected methodology to support the

development of the formulation

model.

Chapter 3

3. Methodology


24

3.1. Introduction

This chapter describes a methodology to find a solution for the following problem:

How to develop a formulation model for urban programs?

The creation of the formulation model encloses an important aspect - the development of an

ontology that is necessary to describe the urban space; first the context, and later the context

with the solution. The ontology is also important to reveal the way formulation model can be

structured in order to establish communication protocols within the planning process, that is,

the process of generating the urban program from the context.

The goal of the study in which context this thesis evolves is therefore to find a solution

for the formulation model. The solution for this problem includes two steps: one is the

development of an ontology to describe urban space, as well as the ontology of the

mechanism by which a program is generated from the analysis of the context; and the other is

the inference of the mechanism itself, that is, of the rules that composed it. This chapter is

concerned with the development of the ontology to describe urban space.

The ontology corresponds basically to at a knowledge based model applied to urban

programs. The outline and the details involving such methodology will be described in this

chapter.

3.2. Adopted methodology

The planning process requires the establishment of an adequate communication with

stakeholders, to share ideas within a planning team, or to present a strategy to a community.

Urban formulation - an important part of the planning process - requires a method to select

and organize data that describes the urban context, to generate a description of the solution

for that context, and to share and communicate the solutions to the stakeholders. An

ontology, a Knowledge Representation (KR) model is a data modelling process or a language

that is capable to represent and convey such type of information (Caneparo et al. 2007). KR is

not a “picture” of the problem, but rather a device for the attainment of knowledge about it

(Kaplan, 1963). Indeed, sometimes the most important outcome of a data modelling process

(here represented by an ontology) may not be the process itself, but rather the insight one

gains as one struggles to articulate, to structure, to critically evaluate, and agree upon it

(Moore & Agogino, 1987). “Therefore, the purpose of the modelling process - an ontology - is


25

not only a proper representation of a process but rather how this process can help one to

better understand the domain of knowledge represented in it” (Luca 2007).

What is an ontology?

The concept comes from a theory that concerns the study of existence born from the legacy of

the Aristotelian philosophy meaning “a systematic explanation of the existence” (Gruber et al

1993). The “Aristotelian ontology offers primitive categories, such as substance and quality,

which were presumed to account for “All That Is”. Ontologies, in general, are created to

facilitate the understanding about a specified a domain by defining its entities, its classes, its

functions and the relationships between all those (Fonseca & Egenhofer 1999). Despite its

philosophical legacy, the ontology described in this research is related with a technical term

and method that is used in computer science.

Why create an ontology?

An ontology is a resourceful data model that helps to support the development of the urban

formulation model by providing a common vocabulary for users who need to share

information in this specific domain (Sachs et al. 2006). In addition, an ontology is an accurate

mechanism to explicit and increase the knowledge about a specific subject matter, in the case

of this research concerning urban space as well as the set of solutions to intervene in it. Some

of the reasons to create an ontology are:

1. A share of a common understanding of the structure of information among people or

software agents,

2. To enable the reuse of the domain knowledge,

3. To make domain assumptions explicit,

4. To separate domain knowledge from operational knowledge, and finally

5. To analyze domain knowledge.

In the context of the formulation model such process permits to:

1. Create a common shared structure of information to support urban programs,

2. To recycle such information in order to use it recurrently in different urban contexts,

3. To explicit the formulation concepts by defining the way entities operate within the

ontology,

4. To separate the domain conceptions from the urban operative descriptions, and

5. To assess the data model in order to improve the pre-design ontology (Noy et al.

2001).


26

How to create an ontology?

The best method to create an ontology is with an ontology editor which is a software platform

composed by three basic blocks that include: 1) classes, 2) slots (sometimes also called roles or

properties), and 3) facets (sometimes called role restrictions). An ontology of such type

together with a set of individual instances of classes constitutes a knowledge base (Sachs et al.

2006). There is no one correct methodology, nor is there a single correct result for developing

ontologies. “Developing an ontology is an iterative process. Usually one can start with a rough

first pass at the ontology, and then revise and refine the evolving ontology in order to fill in the

details” (Noy et al. 2000). In practical terms developing an ontology includes: 1) defining

classes in the ontology, 2) arranging the classes in a subclass-superclass hierarchy, 3) defining

slots and describing allowed values for these slots, and 4) filling in the values for slots for

instances. The undertaking of the production of ontologies is according to Smith and Mark,

(Fonseca & Egenhofer 1999): 1) to help to understand the way different communities share

information, 2) to help to discover certain distortions in the cognitive processes of conception

of the world, and 3) to supply patterns towards the development of a process.

3.3. Ontologies within computer science - (AI) Artificial Intelligence

Due to its capabilities, ontologies have been adopted in many business and scientific

communities as a way to share, reuse and process domain knowledge. “Ontologies are now

central to many applications such as scientific knowledge portals, information management

and integration systems, electronic commerce, and semantic web services” (McGuinness et al.

2000). This demonstrates the potential of ontologies within computer science. In this

application field the ontology term denotes a method that is designed for a purpose, which is

to enable the modelling of knowledge about a real or imagined domain. Gruber (1993)

describes in a very explicit way how to develop ontologies and the implications of such

method. He noticed that the term had been adopted by early Artificial Intelligence (AI)

researchers, who recognized the applicability of the work from mathematical logic (McCarthy

1980). He also noticed that AI researchers could create new ontologies as computational

models that enabled certain kinds of automated reasoning (Hayes-Roth 1985). Gruber refers

that “in the 1980's the AI community came to use the term ontology to refer to both a theory

of a modelled world (Hayes 1979) and a component of knowledge systems; referring that


27

“some researchers, drawing inspiration from philosophical ontologies, viewed computational

ontology as a kind of applied philosophy” (Sowa 2000).

3.4. Ontology Interoperability

It was patent in an effort, in the early 1990's, to create interoperability standards by identifying

a technology stack that called out the ontology layer as a standard component of knowledge

systems (Neches et al. 1991). “A widely cited web page and paper associated with that effort

is credited with a deliberate definition of ontology as a technical term in computer science”

(Gruber et al. 1995). The paper defines ontology as an “explicit specification of a

conceptualization,” which is, in turn, “the objects, concepts, and other entities that are

presumed to exist in some area of interest and the relationships that hold among them.”

While the terms specification and conceptualization have caused much debate, the essential

points of this definition of ontology are, according to Gruber that: 1) an ontology specifies the

concepts, relationships, and other distinctions that are relevant for modelling a domain, and 2)

the specification is in the form of definitions of representational vocabulary (classes, relations,

and so forth), which provide meanings for the vocabulary and formal constraints on its

coherent use. One of the essential characteristics of the ontologies is the sharing of

information - the shared knowledge, which allows the creation of common systems. The

advantage is to provide an integration of different studies on the same substance of inquiry,

through a recurrent general procedure. This focus allows and prevents ambiguities between

results. However the difficulties caused by the heterogeneities of the information have

invoked an increased necessity of creating a science of integration (Fonseca et al. 2002).

According to Fonseca and Egenhofer (1999) the elaboration of ontologies requires also the

participation of diverse entities in permanent interaction. They described the key elements of

such system by; “container” (container of objects from diverse sources), “data repository”

(within a defined research and classes), “user interface”, and the “ontologies” (dynamic

structures - objects in a pattern catalogue). In such scheme, these forms are mediated by the

“coordinator”.

3.5. Representation of ontologies

The ontology allows one to act within two differentiated and complementary levels: the “top

level ontology” (Guarino 1997) where the concepts and the macro scale relations are located;

and the “application ontology”, where are specified the concepts describing the nature of its


28

particular interactions. The “top level ontology”, as mentioned, describes relations at a macro

scale – for example the core components of the pre-design phase. The “application ontology”

specifies particular concepts belonging to particular fields which include detailed tasks - for

example the urban codes of the pre-design phase. The representation of ontologies can also

be expressed through standard associations (Novello et al. n.d.) to qualify the relations

between entities, namely: taxonomy (is a, type of), partonomy (part of), mereology (“part-of-

all” theory), chronology (precedents between concepts) and topology (theory of limit and

border). Moreover, ontologies can be represented by; a) dominions, which describes the

vocabulary used in a specific field of knowledge; b) tasks, which describes the vocabulary used

in a specific activity of a field; and c) representations, which explains the concepts of formal

entities.

Urban ontologies are usually called spatial ontologies, due to its relevant spatial

descriptions. The development of research in the field of spatial ontologies has created a

specific lexicon and a theoretical structure, to responds to the specific demands of spatial

domain. Smith and Mark (1999) purposed the creation of spatial ontologies with the objective

of getting a better understanding concerning the geographic world. The same authors allege

that the use of this type of ontologies can assist users in the exchange of information

preventing distortions from human cognition, which is a recurrent idea in the creation of

generic ontologies. To Pinho and Goltz (n.d.) the geographic objects, that are the spatial

fundamentals of the urban space, can be divided in two types, defined by the Latin axioms

Bona Fide and Fiat. The first type congregates objects that possess a physical delimitation

more or less accepted between people of different cultures. They are tangible objects as a

road, a mountain, or a lake. In opposition Fiats referrers to objects with abstract limits. Such

Fiats can be divided by 1) Fiats and vagueness that are geographic objects that do not possess

a well-defined border, and were limits are diluted in space, b) Consensus Fiats that are objects

that have its existence on a consensual form by given information from inhabitants in a

determined area, c) Legal fiats that are objects that have their limits defined on some legal

basis, as the land use or the airspace, and the GIS fiats that are objects generated from the

mathematical logic of the GIS.

The development of the diagram (9) is inspired in the urban plot diagram developed by

Pinho and Goltz [n.d.]. This diagram aims to explain some types of entities that compose an

ontology, in this case involving urban patterns.


29

9. Example of an urban pattern top level ontology. This diagram is inspired in the urban plot diagram

developed by Pinho & Goltz [n.d.]

3.6. Editing ontologies

As aforesaid the best method to create an ontology is with an ontology editor.

But what is an ontology editor?

According to Sachs (2006) an ontology editor is an integrated software tool used by system

developers and domain experts to develop knowledge-based systems. The applications

developed with an ontology editor are generally used in problem-solving and decision-making

in a particular domain. In the case of this research the problem is focused on the urban space

phenomena, and the decision-making on the set of solutions to implement in such a space in

order to support sustainable communities.

Where to start?

One might start by determining what the ontology is going to be used for, and how detailed or

general the ontology is going to be. In this thesis the objectives is clear: to define a formulation

model for urban programs. Such ontology, in a primary phase, will describe basically the

general concepts of the formulation model, and eventual methods to categorize its entities.

is

is

is part of

urban patterns

inanimate objects

geographic object

fiat

legal fiat

urban codes

GIS fiat

GIS data

consensus fiat

field analysis

fiat and vagueness

private space (example)

bona fide

common object


30

There are several viable alternatives for creating the ontology. One of the first steps will

be to determine which alternative would work better for the projected task, be more intuitive,

more extensible, and more maintainable - remembering that an ontology is a model of a real

domain in the world, ant that the concepts in the ontology must reflect this reality. After

defining an initial version of the ontology, one can evaluate and debug it by using it in

applications or problem-solving methods or by discussing it with experts in the field. Such a

framework is crucial to develop the formulation model. “As a result, one will almost certainly

need to revise the initial ontology. This process of iterative design will likely continue through

the entire lifecycle of the ontology” (Sachs et al. 2006).

To explicit the functioning of an ontology editor it will be develop an example with the

Protégé editor (Noy et al. 2001) following the trail of its tutorial. The case is simple. Suppose

one wants to develop an ontology for climatic patterns. The “examples/climatic patterns”

subfolder of the ontology editor installation directory contains a completed Editor-Frames

project, - urban climatic patterns, which provides one possible ontology for this domain. Some

of the questions one want to answer are:

1. What are the components responsible for each climatic pattern?

2. What is the content of each pattern, and what is the theory behind it?

3. To what matters each pattern is related with?

4. What is the layout of each pattern?

Once one has an idea of what one wants to cover, one can list some of the important terms

needed. These can include basic concepts, properties they might have, or relationships

between them. To begin with, one can just collect the terms without regard to the role they

might play in the ontology. In the climatic patterns example one have particular patterns. Each

one contains content such as “type of climatic aspect that is covered” and “applicability” and it

has a theory behind it that is responsible for the validity of its existence. Each pattern has a

form, and that form may or may not be material. For each material pattern, one wants to

know its name and subject, and to what it relates with. As one continues to generate terms,

one are implicitly defining the scope of our ontology, by deciding what to include and what to

exclude. For example, upon initial examination of the term material pattern, one might want

to add “sun protected windows” or “wind impact on streets”. However, upon reflection, one

might realize that one wants the ontology to focus on the costs associated with the content of

the “sun impact on buildings”. Therefore, one would decide not to include “wind impact on


31

streets” as a term of interest. When one has a fairly complete list, one can start to categorize

the different terms according to their function in the ontology. Concepts that are objects, such

as “pattern” or “site application”, are likely to be best represented by classes. Properties of the

classes, such as “wind” or “sun”, can be represented by slots, and restrictions on properties or

relationships between classes and or slots, are represented by slot facets (Noy et al. 2000).

Above is shown an example of the development of a climatic pattern in the Protégé editor. The

example presents cold region patterns.

10. Protégé edition of a climatic pattern

11. The visualization tab of the cold region edited by the Protégé editor


32

12. In the left is shown the class hierarchy of the climatic patterns ontology. In the top right is shown the

type of role of each class (related with a different symbol) – abstract or concrete. In the bottom right is

shown an instance slot (bioclimatic pattern slot) where one can fill up all required data

3.7. Comparing ontology editors: Ontolingua and Protégé

The Ontolingua editor was developed by the Knowledge Systems Laboratory of Stanford

University (KSL), and it offers a set of program tools that allows the editing of ontologies. The

system is referred to by several authors, amongst them, Fonseca and Egenhofer (1999).

Ontolingua is an online software where web sessions are authorized for registered users. The

Stanford KSL Ontology Editor allows, through a set of procedures, to create series of axioms,

made within a hierarchy of classes, and associations of “Examples” or individual “Instances”,

establishing “Functions”, “Facets” e “Slots” as schematic entities. This comprises the basic

vocabulary of the Ontolingua Editor. The generation of these entities is clarified by the

program’s tutorial (Farquhar 1997), within a set of suggestions and guidelines for the

development of ontologies, namely; “1) to write a few sentences describing the ontology

including the general subject area that is intended to cover with the ontology, 2) to make a list

of what one would like to state in the ontology, 3) to make a list of the concepts that one think

should be included in the ontology, 4) to look for ontologies in the library of ontologies that

may contain terms which one can use to develop the ontology, and5) review and make

modifications to one’s lists as needed throughout these steps” (Farquhar 1997).


33

Like the Ontolingua editor, the Protégé editor is a free, open-source platform that

provides a growing user community with a set of tools to construct domain models and

knowledge-based applications with ontologies. One of its advantages is the plain access to

formats and protocols towards the creation of resourceful data models. The Protégé

implements a rich set of knowledge-modelling structures and actions that support the

creation, visualization, and manipulation of ontologies in various representation formats.

Protégé can be customized to provide domain support for creating knowledge models and

entering data, appearing to be friendlier than the Ontolingua in the management of its

platform. Furthermore, Protégé can be extended by way of a plug-in architecture and a Java-

based Application Programming Interface (API) for building knowledge-based tools and

applications. The Protégé platform supports two main ways of modelling ontologies: 1) The

Protégé-Frames editor enables users to build and populate ontologies that are frame-based, in

accordance with the Open Knowledge Base Connectivity protocol (OKBC). “In this model, an

ontology consists of a set of classes organized in a subsumption hierarchy to represent a

domain's salient concepts, a set of slots associated to classes to describe their properties and

relationships, and a set of instances of those classes - individual exemplars of the concepts that

hold specific values for their properties” (Sachs et al. 2006). 2) The Protégé-OWL editor

enables users to build ontologies for the Semantic Web, in particular in the W3C's38 Web

Ontology Language (OWL). “An OWL ontology may include descriptions of classes, properties

and their instances. Given such an ontology, the OWL formal semantics specifies how to derive

its logical consequences, i.e. facts not literally present in the ontology, but entailed by the

semantics. These entailments may be based on a single document or multiple distributed

documents that have been combined using defined OWL mechanisms”. Protégé ontologies can

be exported into a variety of formats including RDF39(S), OWL, and XML Schema40. Protégé is

38 . W3C: World Wide Web Consortium. 39 . The RDF data model is similar to classic conceptual modeling approaches such as Entity-Relationship or Class diagrams, as it is

based upon the idea of making statements about resources (in particular Web resources) in the form of subject-predicate-object

expressions. These expressions are known as triples in RDF terminology. The subject denotes the resource, and the predicate

denotes traits or aspects of the resource and expresses a relationship between the subject and the object. For example, one way

to represent the notion "The sky has the color blue" in RDF is as the triple: a subject denoting "the sky", a predicate denoting "has

the color", and an object denoting "blue". RDF is an abstract model with several serialization formats (i.e., file formats), and so the

particular way in which a resource or triple is encoded varies from format to format. http://www.w3.org/TR/PR-rdf-syntax/

"Resource Description Framework (RDF) Model and Syntax Specification".

40 . An XML schema is a description of a type of XML document, typically expressed in terms of constraints on the structure and

content of documents of that type, above and beyond the basic syntactical constraints imposed by XML itself. These constraints

are generally expressed using some combination of grammatical rules governing the order of elements, Boolean predicates that

http://en.wikipedia.org/wiki/Entity-relationship_model

http://en.wikipedia.org/wiki/Class_diagram

http://en.wikipedia.org/wiki/Statement

http://en.wikipedia.org/wiki/Resource_%28Web%29

http://en.wikipedia.org/wiki/Trait_%28biological%29

http://en.wikipedia.org/wiki/Aspect

http://en.wikipedia.org/wiki/Subject_%28grammar%29

http://en.wikipedia.org/wiki/Predicate_%28grammar%29

http://en.wikipedia.org/wiki/Object_%28grammar%29

http://en.wikipedia.org/wiki/Serialization

http://www.w3.org/TR/PR-rdf-syntax/

http://en.wikipedia.org/wiki/XML

http://en.wikipedia.org/w/index.php?title=Boolean_predicates&action=edit&redlink=1


34

based on Java, is extensible, and provides a plug-in environment that makes it a flexible basis

for rapid prototyping and application development. “Protégé is also supported by a strong

community of developers and academic, government and corporate users, who are using

Protégé for knowledge solutions in areas as diverse as biomedicine and intelligence gathering.

Protégé is a U.S. national resource for biomedical ontologies and knowledge base supported

by the U.S. National Library of Medicine, and is a core component of The National Centre for

Biomedical Ontology, within the Stanford Centre for Biomedical Informatics Research” (Noy et

al. 2000).

the content must satisfy, data types governing the content of elements and attributes, and more specialized rules such as

uniqueness and referential integrity constraints.


35

data knowledge model

(an ontology editor)

attributes

data

relational rules

how to develop a pre-design model

to launchsustainable

urban programs

1

•problem

2

•methodology

3

•management tool

helps to create and manage

urban programs

classes slots

instances

phases procedures

rules constraints

sorted data

The objective is to create a model that encloses the development of an ontology to describe the urban space; first the context, and later the context with the solution. The objective is to make use of an administrative tool that helps to create and manage urban programs.

The framework is described in the following diagram scheme.

13. Methodology framework

Methodology (management tool):

1. Problem |Complexity of urban data

2. Methodology |Organizes data elements to facilitate understanding

3. Management tool |Help with the creation and management of urban programs

03• an ontology (a data model) is a good way of describing the pre-design phase of the urban design process.

•an ontology helps with the creation and management of urban programs.


36

The objectives of the fourth chapter

are:

a) the description of the basic

conceptual model, and

b) the description of the three parts

of the formulation model: b1) the

input, b2) the mechanism, and b3)

the output.

Chapter 4

4. Conceptual and Formulation Models


37

4.1. Introduction

The objectives of the fourth chapter are:

a) the description of the basic conceptual model, and

b) the description of the three parts of the formulation model41: b1) the input, b2)

the mechanism, and b3) the output.

4.2. The conceptual model

The basic conceptual model represents the core concept of this research, and the goal of this

chapter is to explain it.

The conceptual model describes the structure of the formulation process and is inspired

in a more global mathematical model invented by Duarte (2003) for developing interactive

systems for generating design solutions. Duarte’s model is called discursive grammar, and it

includes a program system for formulating the design brief based on contextual data, a design

system for generating a solution that matches the brief, and an evaluation system for

guaranteeing that the brief fits the context and the solution matches the brief (Diagram 14).

The program or formulation system is encoded by a description grammar (Stiny 1981),

whereas the design system is composed of a description and a shape grammar (Stiny 1980).

The evaluation system compares the description in the design brief with the description of the

evolving design. A set of heuristics is then used to guide the generation of solutions towards to

ensure that the solution’s description matches the brief’s description. The model developed in

this research explores the “formulation system” (the first module in Duarte’s model), adapting

it to the urban context, and introducing additional functions, such as a clear definition of the

design brief ‘s description structure by means of an ontology and its encoding using an

ontology editor.

41. The ‘formulation’ term, in this research, denotes the planning framework of producing an urban program (and also its process). Formulation occurs during the ‘pre-design phase’ and it corresponds to an initial phase of the urban design process where the actions to intervene in the urban space are planned by deriving the program from the context.


38

•contextual data

INPUTS

•machine

INTERPRETER

•design specifications

OUTPUTS

portuguesehousing

program system

Siza’sMalagueira

design system

portuguesehousing

evaluation system

context program design

Pedro, 1999Description grammar (Stiny, 1981) Shape grammar (Stiny and Gips, 1972)

Pedro, 2000

14. Duarte’s model (discursive grammar for housing) based in three theories (Stiny 1981), (Pedro 2001a)

and (Stiny & Gips 1972)

1. INPUT Read Data (urban context) DATA COLLECTION

2. INTERPRETER Interpretation of data MECHANISM (for generating the program)

3. OUTPUT Define Specifications (for design) PROGRAM (design brief)

As in Duarte’s model, in FMo there is a clear path information flow between the three basic

functions or components of the model, as shown in Diagram 15.

information path

information path

15. The conceptual model of the urban formulation process (FMo)


39

Model’s functioning

In a sense, the conceptual model is a representation of a “computational procedure that takes

some value or set of values, as inputs and produces some value, or set of values, as output”

(Cormen et al. 2001). One can also argue that, informally, this process corresponds to an

algorithm which is any well-defined computational procedure based on data exchanges - a

sequence of computational steps that transform the input into the output. An algorithm that

can also be interpreted as a tool for solving a well-specified computational problem - a

problem here focused on the generation of urban solutions.

As mentioned, the algorithm describes a specific computational procedure for achieving

the input/output relationship. For example, one might want to sort a sequence of numbers in

decreasing order. This problem arises frequently in practice and provides a ground for

introducing standard design techniques. Alexander’s Pattern Language (Christopher Alexander

et al. 1977) was created under a similar conceptual structure. Here is how one formally can

define a sorting problem:

Input: A sequence of n numbers a1, a2,..., an .

Output: A permutation (reordering) a’1, a’2,..., a’n of the input sequence such

that a’1 a’2 ... a’n.

Given an input sequence 41, 61, 79, 36, 61, 78, a sorting algorithm returns as output the

sequence 36, 51, 61, 61, 78, 79. Such an input sequence is called an instance of the sorting

problem. In general, an instance of a problem consists of the input (satisfying whatever

constraints are imposed in the problem statement) needed to compute a solution to the

problem. The input corresponds to urban contextual data, being the constraints part of its

interpretation. The solution corresponds thus to the interpretation output, which in this case

corresponds to specifications for design solutions.

An algorithm is said to be correct if, for every input instance, it halts with the correct

output. Cormen (2001) says that a correct algorithm solves the given computational problem,

as an incorrect algorithm might not halt at all on some input instances, or it might halt with an

answer other than the desired one. An algorithm can be specified (in computer science) as a

computer program, or even as a hardware design. The requirement is that the specification

must provide a precise description of the computational procedure to (Cormen et al. 2001) - if

something, then do something - the recurrent concept of the urban formulation framework.

Now, a closer look at the model’s basic components:


40

INPUT: The first component of the conceptual model (Data) corresponds to the set of

information that is gathered to describe the urban space (among other contextual

information). Data corresponds to the set of information collected on the site and

the local population, as well as on the promoter’s features, mandatory requirements,

or ideas about the plan’s development.

INTERPRETER: The Interpreter42 corresponds to the core of the model - a centre of

data exchange and interpretation (analysis, conversions, and synthesis). The

Interpreter defines the flow between the contextual data and the final list of

requirements or ingredients that will be used as patterns for design.

OUTPUT: The third component corresponds to the set of specifications that will help

to described spatial solutions (organization, shape, and morphology). The data

structure43 organization of the output data (its ontology) is particularly important. An

adequate formulation ontology (the structure of the urban program) will facilitate

the generation of better solutions as it facilitates the flow between descriptions

(urban program) and generation (urban solutions). Model descriptions will be

created on the foundations provided by the Alexander’s Pattern Language theory.

4.3. The formulation model

The formulation model (FMo) is the detailed structure of the conceptual model that enfolds a

simple sequence of ideas, that is;

a) a problem is perceived (the urban context),

b) thus emerges the need to intervene to solve the problem,

42 . A classical translating machine stands with one foot on the input text and one on the output. The input text is analyzed by the

components of the machine that make up the left leg, each feeding information into the one above it. Information is passed from

component to component down the right leg to construct the output text. The components of each leg correspond to the chapters

of an introductory textbook on linguistics (…), then syntax, semantics, and so on. (…) We cannot be sure that the classical design is

the right design, or the best design, for a translating machine. But it does have several strong points. Since the structure of the

components is grounded in linguistic theory, it is possible to divide each of these components into two parts: a formal description

of the relevant facts about the language, and an interpreter of the formalism. The formal description is data whereas the

interpreter is program. The formal description should" ideally serve the needs of synthesis and analysis indifferently. (…) (Kay

1984).

43 . A data structure is a way to store and organize data in order to facilitate access and modifications. No single data structure

works well for all purposes, and so it is important to know the strengths and limitations of several of them (Luger 2005).


41

c) however the problem needs to be made explicit to enable the development of

solution(s),

d) thus it has to be created a path towards solution(s),

e) this requires the creation of an instrument to manage the e1) contextual data, e2)

the interpretation of the problem, and e3) the coding of specifications for the

solution(s).

The formulation model consists of the instrument to solve this finite sequence of instructions.

Its framework fits well in the typical workflow of an expert system44, which involves the

following steps (Forsythe & Hess 2001):

a) collecting information from one or more human informants and/or from

documentary sources.

b) ordering that information into procedures (e.g., rules and constraints) relevant to

the operations that the prospective system is intended to perform.

c) designing or adapting a computer program to apply these rules and constraints in

performing the desired operations.

So, one of the most important aspects of the formulation model is how entities are

congregated to accomplish an efficient role in the formulation process.

What is then the role of the model’s entities?

In urban space problems are complex and multifaceted. In general, a model built to respond to

urban problems involves a great amount of entities45 (classes’s46 data) in intrinsic competition

within the definition of its basic structure. The questions involving such a competition are

diverse. One can set a trial question: If contradictory, is an urban rule (from an urban code)

more or less important than a political measure to define a specification? At a first glance, it is

44 . ‘Expert systems are designed to emulate human expertise; they are constructed using computer languages that can represent

and manipulate symbolic information. Each system is intended to automate decision-making processes normally undertaken by a

given human ‘expert’ by capturing and coding in machine readable form the background knowledge and rules of thumb

(‘heuristics’) used by the expert to make decisions in a particular subject area (‘domain’). This information is encoded in t he

system’s ‘knowledge base’, which is then manipulated by the system’s ‘inference machine’, in order to reach conclusions related

with the task at hand’(Forsythe & Hess 2001).

45 . In the urban context such entities (data classes and processes) covers a wide range of matters such as political visions, mandatory policies, participation actions (experts and communities), but they also comprise a data collection process, a cont extual analysis, and a design paradigm. 46 . Classes describe concepts in the domain.


42

easy to suppose that such type of problems is out of the model. However, entities have to be

positioned in the formulation structure so as its meaning enables the description of their

precise role. If a role of an entity is not initially (or entirely) described, the formulation model

has to frame it somehow. This means that classes and instances of information that are

distributed and related with each other can impact the way the model functions. So, in a

sense, the model entities are intrinsically the builders of their structure. But there are other

difficulties. One is the range and diversity of the contextual data (rules, recommendations,

existing population, existing morphology, culture, economy, strategies, etc.). The type of

relations established among model entities changes according to the context. Such an

unpredictability amplifies the difficulty to setup a closed chart of formulation rules. Therefore,

theoretically, such a closed system is not viable because knowledge consists of a flexible

structure to enable appropriate activities for specific problem demands. The conclusion is

simple: knowledge acquisition and translation is often problematic (Forsythe & Hess 2001). This

leads one to be faced with a problem. How to deal with model flexibility while, at the same

time, data rules need to be encoded into a machine-usable form?

It is important to a understand that formulation model entails a decision-making process

of data acquisition and translation which calls for the use of a knowledge base model47 (KBS)

here represented by a computational ontology48. Such a particular model provides the basis for

dealing with the problems mentioned above, as explained in Chapter 3.

Then, how to create such an ontology?

As mentioned in Chapter 3 there is no one correct way to build an ontology49. An ontology is a

formal explicit description of concepts in a domain of discourse, and its framework

consists of a data modelling process within a problem-solving method that, in some measure,

depends on assumptions of model’s manager (the formulation ontology engineer).

Assumptions require a clean–up process of precision and accuracy. With precision, the non-

47 . An ontology together with a set of individual instances of classes constitutes a knowledge base. In reality, there is a fine line where the ontology ends and the knowledge base begins (Noy et al. 2000). 48 . Computational ontologies, in the way they evolved unavoidably mix together philosophical, cognitive, and linguistic aspects. Ignoring this intrinsic interdisciplinary nature makes them almost useless (Guarino 1998). 49 . An ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine-interpretable definitions of basic concepts in the domain and relations among them (Noy et al. 2000).


43

intended models are excluded, and with accuracy50 the negative examples are excluded

(Guarino 1998). This means that capturing all intended entities, concepts, and models, is not

sufficient to create a “perfect” ontology.

To create an ontology, one has also to look at its application dominion. It is well known

that cultural frameworks vary widely. Therefore, it is improbable that a sole model functions,

properly, in a wide universe. This constraint demands for the establishment of a limit for the

model’s applicability. For now one will consider the Western culture range. A complementary

issue is the definition of territorial scale to which the ontological model can be applied. The

site planning scale will be the application scale for the intended model.

Framing the ontology

The first approach to the ontology is by means of cognitive domain knowledge and the domain

of discourse, together with a definition of conceptual relations and ontology relations. Such an

approach demands the creation of a conceptual diagram where main model classes are

described. The diagram requires a formal structure of (a piece of) reality as perceived and

organized by an agent, independently of the vocabulary used, and the actual occurrence of a

specific situation, which is the base of an ontological conceptualization (Gruber 2005)51.

The conceptual ontology52 shown in the next page was first developed to be presented

at the eCAADe 2008 conference, and it corresponds to a domain of discourse or a

conceptualization of the urban planning domain. The objective of the elaboration of the

diagram was to organize the concepts and the relations that are established in a very initial

phase of the planning process. Such phase corresponds to the pre-design phase, where the

development and the management of an urban program occur.

The developed ontology model is summarized in Diagram 16 (Montenegro, N. and

Duarte J.P. 2008).

50 . When is a precise and accurate ontology useful? a)When subtle distinctions are important, b) When recognizing disagreement is important, c) When general abstractions are important, d) When careful explanation and justification of ontological commitment is important, e) When mutual understanding is more important than interoperability. 51 . Example of a conceptualization: A conceptualization for D is a tuple C = <D, W, ℜ>, where ℜ is a set of conceptual relations on <D, W>. A model for a language L with vocabulary V is a pair structure <S, I>, where S = <D, R> is a world structure and I: V→D∪R is the usual interpretation function. A model encodes a particular extension interpretation of the language. Analogously, we can encode an intension interpretation by means of a structure <C, ℑ>, where C = <D, W, ℜ> is a conceptualization and ℑ: V→D∪ℜ is an intension interpretation function. We call such a structure K=<C, ℑ> an ontological commitment for L. L commits to C by means of K. C is the underlying conceptualization of K (Guarino 1998). 52 . There is an ontology of the process (how is organized the pre-design phase?) and an ontology of the context and the product (description of the precise context of the urban space, its regulations, its sorted data, and the description of a series of solutions, and guidance).


44

16. The urban formulation ontology (Montenegro, N. and Duarte, J, 2008)

Now, the description of the diagram

The diagram is organized in two distinct parts:

a) the pre-design phase 1, and

b) the pre-design phase 2.


45

In general, in the pre-design phase 1 (PD1) site and population are analysed, as well as general

strategies and constraints imposed on the territory. PD1 corresponds to the input data of the

basic conceptual model. In the end of this first phase, a document53 will synthesise all

collected data enabling its use in the second phase of the pre-design process. Then, in the pre-

design phase 2 (PD2), data is codified towards the creation of specifications for urban design.

Such specifications correspond, in summary, to the urban program. The output of PD2 is,

therefore, the output of the basic conceptual model.

The diagram depicts the different phases of the formulation process, the type of data

manipulated, and the participants involved.

In the gray boxes positioned in the bottom of the diagram are located the crucial components

of the formulation model. These elements encompass core categories of the model that

correspond to diverse functions or tasks. Their related elements are disposed in a vertical line

within the diagram, as the example described below; to the component “stages” corresponds

two different vertical entities:

1. pre-design phase 1, and

2. pre-design phase 2.

This vertical reading dominates over other elements. Overlaid on the described vertical

dominant line co-exists a horizontal reading. Considering the example above, the pre-design

phase 2 is one of the stages of the overall pre-design phase that includes two types of planning

actions/themes:

1. the language, and then to

2. the design patterns.

While the vertical line represents the thematic domains of the formulation process drawn in

chronologic order, the horizontal line embodies a scale decomposition of the necessary

subjects essential to produce an urban program. Both vertical and horizontal readings are

complementary.

53 . The Document Synthesis (DS).


46

To facilitate the understanding of the diagram, a set of additional representation aids,

such as arrows and small gray titles, were located among diagram entities to qualify and clarify

their roles in the process (role representation). Moreover, the way arrows are pointing

indicates the action flow that usually occurs between adjacent entities.

Sketching the ontology of the formulation process

Although the structure of Diagram 16 seems clear, it is not entirely precise. The relations

established among PD classes and instances do not correspond to the result of a

computational ontology. On the contrary, the structure of the diagram corresponds to a

conceptual sketch (or a conceptual discourse of the urban domain) to feed the final ontology-

building.

By computing an ontology (using an ontology editor like Protégé), one can assure a high

level of accuracy of the developed model. Such precision drifts from the embedded rules of

the editor that constrain how ontological objects are linked and impose implicit conventions

on their relational status. At the same time, such edition maintains a hierarchical order within

model classes54. It is important to mention that, in future research the diagram components

will be encoded into such a knowledge modelling structure (a computational ontology) to

enable the description of implicit rules in an explicit way.

This means that it is important to establish instances within the model classes in order

to portray value and partnership, defining if an element or entity is part of another one, or if

an entity interacts with another one in some way, or even what an entity “is” following the

description of “what is”.

The diagram 17 shows the sketch of an edition built in the Protégé-Frames editor in

accordance with the Open Knowledge Base Connectivity protocol (OKBC).

The ontology describes PDP (dark green box), PD1, and PD2 (light green boxes) super-

classes, between context and final specifications slots.

54 . The descriptions of such methodology are defined in the third chapter of this research.


47

17. PD classes, subclass-superclass hierarchy, slots and instances (in light blue). Bellow the Protégé VizTab


48

•contextual data

INPUTS

•machine

INTERPRETER

•design specifications

OUTPUTS

The objectives of the fourth chapter are: a) the description of the basic conceptual model, and b) the description of the three parts of the formulation model: b1) the input, b2) the mechanism, and b3) the output.

The basic conceptual model consists of three basic components that represent the three basic functions of the model as shown in Diagram 18:

1) the Input (to read contextual data), 2) the Interpreter (to interpreter received data), and 3) the Output (to describe specifications for design).

data path

data path

18. Urban formulation process (FMo)

The formulation model (FMo) is the detailed structure of the conceptual

model as shown in the ontological Diagram 12 of this research.

04

• basic conceptual model

• formulation model


49

The contribution of the fifth chapter for

this research is to describe the

formulation process as well as the

design phase where it occurs – the pre-

design phase. The main objective is to

provide a better understanding of the

formulation framework and, at the

same time, to generate the sketch of its

semantic ground, that is, a consensual

vocabulary to enable appropriate

specifications or the “communication

acts” towards design solutions.

Chapter 5

5. The Formulation Process


50

5.1. Introduction

The contribution of the fifth chapter of this research is to describe the formulation process as

well as the design phase where it occurs – the pre-design phase. The main objective is to

provide a better understanding of the formulation framework and, at the same time, to

generate the sketch of its semantic ground, that is, a consensual vocabulary to enable

appropriate specifications (or the “communication acts” (Gandon 2002)) towards design

solutions.

The formulation process corresponds to a specific phase of the design process called

pre-design phase. In short, the pre-design phase consists in a phase of analysis and

interpretation of data that occurs before design begins (Best & De Valence 1999). Usually,

during this phase, studies are done to analyze the context and identify the requirements,

constraints, and opportunities for a given site (McCallum et al. 1996). In general, it also

consists of a set of guidelines to assist planner’s actions.

5.2. Operational structure of the formulation process

The relevance of the pre-design phase becomes apparent when placed in a project scheduling

diagram. PD phase represents about of the overall design process varying in significance

throughout different stages55 (Best & De Valence 1999). PD represents the query phase of the

design process, and its main objective is to facilitate the definition of strategies towards design

solutions. To understand more clearly the purpose of the formulation model it is essential to

observe its different stages.

All starts with a contract that corresponds to an agreement between two parties -

frequently a promoter and a design team. These parties, governed by particular interests,

configure a team searching for specified goals (occasionally dissimilar). The possible existence

of different visions among different parties requires an effort of convergence to benefit the

55 . In fact, the planning process can be present during all design process mainly because it deals with permanent factors such as collaboration, and participation.


51

end result. The success of a collaborative environment is therefore crucial for the

accomplishment of formulation goals (Mabert et al. 2003).

Following the contractual agreement, the process usually continues with a definition of a

schedule to implement the necessary steps required for generating the plan. It is important to

mention that the pre-design process frequently extends to the schematic design phase (SDP)

(Alexander et al. 1987) - the phase that follows the pre-design phase. This happens because it

is essential to monitor the development of this early and conceptual phase of the design

process. This means that the formulation process56 corresponds more to an extended course

of action rather than a locked process (Huberman & Miles 1994).

Diagram 19 describes a summary of the conventional main actions of the design process.

19. A planning schedule57

56 . It is also usually called as the programming phase (or framework) of the design process. 57 . The Bid phase described in the schedule corresponds at the following concept: Design-bid-build (or design/bid/build, and

abbreviated D-B-B or D/B/B accordingly), also known as Design-tender (or "design/tender"), is a project delivery method in which

the agency or owner contracts with separate entities for each the design and construction of a project. Design-bid-build is the

traditional method for project delivery and differs in several substantial aspects from design-build. There are three main

sequential phases to the design-bid-build delivery method: 1. The design phase, 2. The bidding (or tender) phase, and 3. The

construction phase.

http://en.wikipedia.org/wiki/Design

http://en.wikipedia.org/wiki/Construction

http://en.wikipedia.org/wiki/Design-build

http://en.wikipedia.org/wiki/Call_for_bids


52

5.3. The formulation phases

It seems that the formulation process includes two different main phases:

a) pre-design phase 1 (PD1) and

b) pre-design phase 2 (PD2).

How and why such phases are subdivided from the generic pre-design phase?

In general, the division into different parts writes to a method of simplification. The main idea

behind this method is - divide a problem (PDP) into sub-problems (PD1 and PD2) of the same

type58 to simplify and explicit the problem domain.

As a computer programming technique this is called divide and conquer (dialecting59),

and it is key to the design of many important algorithms, as well as a fundamental part of

dynamic programming.

The division of the formulation process into different parts (here represented by its

phases) has an additional logic - it corresponds at two different conceptual sub-dominions of

the formulation framework, as is shown in the diagram 16;

a) A data acquisition phase (that occurs in the PD1) and,

b) A data translation phase (that occurs in the PD2).

Data acquisition corresponds to a phase where contextual data (site and population as

well as strategies politically framed and existing regulations) are acquired (Kay 1984). This

phase is crucial to identify the essential ingredients that will constitute the formulation

database.

Data translation corresponds to the creation of the set of specifications to describe a

solution (or a set of solutions) by applying rules that link contextual data to programmatic

features, that is, the translation of the contextual data (from the formulation database) into

design specifications. The idea is to build-up such specifications under the form of patterns

58 . Such type of division will be implemented throughout all formulation process where divided parts will describe sub-classes of

super-classes of the overall process (here refer to as an ontology).

59 . Dialects are domain specific sub-languages of a programming language or a data exchange language. (See also Grammar-

oriented programming, Language oriented programming, Reflection and Metaprogramming.) A language supporting this paradigm

encourages users to create new dialects for specific problematic domains.


53

des

ign

(so

luti

on

)

con

text

(p

rob

lem

)

according to Alexander’s formalities (Alexander 1979). The rules are inferred a priori for a

general context, say, Portuguese urban environments, and then applied to a specific context,

that is, a specific site and its surroundings. The ultimate goal of this research is to infer such

rules.

In this chapter, it will be presented, when necessary, a simplified version of the

ontological Diagram 16 to explicit the main phases and processes of the formulation

framework. In the first simplified diagram reproduces below are highlighted (in black text) the

different phases of the pre-design phase – the PD1 (data acquisition) and the PD2 (data

translation).

20. Pre-design phases – the PD1 (data acquisition) and the PD2 (data translation)

5.4. Categories enclosing the main processes of the pre-design phase

The categories shown in Diagram 21 are to ontological super-classes that correspond to two

information acquisition and processing phases that occur during the formulation process; in

the first phase the goal is to analyze the context (PD1), in the second phase (PD2) it is to

develop specifications to guide the planner’s design process.

There is a conventional hierarchy in the use of that information.

Normally, in the PD1, the first required data (that will guide and constraint the other

categories of information) is composed by the set of political strategies defined for a territory

under study (I). The second data category usually includes urban regulations that are

PD1 data acquisition

strategic plans

site andpopulation context

urban codes and guidelines

PD2 data translation

pattern language

'specifications for design'


54

des

ign

(so

luti

on

)

con

text

(p

rob

lem

)

applicable to the intervention site (II), and the third one is a portrait of the site and population

(III).

In the second phase (PD2) there is only one category group that is used to translate the

PD1 database into a set of specifications encoded as urban patterns (IV).

Despite the description above, the hierarchy of the described categories can changed

according to specific demands of the problem (such as particular procedures undertaken,

involved parties, or strategic procedural recommendations, etc.).

21. The four categories of the formulation process

Now, a closer look at each one of the categories, starting by the formulation strategies.

5.5. Strategies

Strategies consist of the first category of the pre-design phase 1 (PD1) and are related with

high level decisions that are usually taken even before the intention of producing a particular

plan is completed. Strategies are crucial in the implementation of urban programs because

they represent a crucial benchmark towards sustainable development.

The outcome of an urban design process is an urban plan. So, in urban design the resulting

design is delivered in the form of a plan, and one of the top requirements for urban plans is

the definition of a strategy, to identify the main purposes of the plan (Ulrich & Eppinger 2003).


strategic plans (I)

urban codes and guidelines(II)

site and population context (III)


pattern language


(IV)


55

How to manage strategy in the ontology?

The flow between strategies and design can collapse if strategies reveal insufficiency,

ambiguity, or inaccuracy within its conceptualizations (Gruber 1991). The creation of a

computational ontology helps to surpass this difficulty as its main goal is to disambiguate60 by

means of an inference61 machine that attains at data consistency (Caneparo et al. 2007).

Therefore, there is a clear advantage in the use of a knowledge modelling62 structure (in this

case an ontology) to frame a plan’s strategies. Above all, an ontology can avoid ambiguities

within design outcomes and, at the same time, it can reinforce its accuracy.

The chain seems to be clear. Designs well supported by precise and proficient strategies

(or development visions) lead to better plans.

What are the subject matters behind a plan’s strategies?

Strategies are framed by political visions supported by national and regional policies

established for territories (Graham & Marvin 2001a). The economic, the cultural, and the social

foci in urban communities correspond to core components of such policies (Herrschel & P.

Newman 2002b). These policies are difficult to capture in the formulation database model. The

main reason for such difficulty stems from the variable character of policy which changes

according to the nature of the administration. Political parties hold particular conceptions

about governance and management of spatial resources. This has a direct influence on the

definition of urban programs and thus on plans. Strategies also work at different scales. In

abstraction, strategies may start with policies endorsed to vast regions and may end in rules or

60 . In computational linguistics, word sense disambiguation (WSD) is an open problem of natural language processing, which comprises the process of identifying which sense of a word (i.e. meaning) is used in any given sentence, when the word has a number of distinct senses (polysemy). The solution of this problem impacts other tasks of computation linguistics, such as discourse, improving relevance of search engines, anaphora resolution, coherence, inference, and others. Research has progressed steadily to the point where WSD systems achieve consistent levels of accuracy on a variety of word ty pes and ambiguities. A rich variety of techniques have been researched, from dictionary-based methods that use the knowledge encoded in lexical resources, to supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples, to completely unsupervised methods that cluster occurrences of words, thereby inducing word senses. Among these, supervised learning approaches have been the most successful algorithms to date (Navigli & Velardi 2005). 61 . Inference is the process of drawing a conclusion by applying rules (of logic, statistics etc.) to observations or hypothesis; or by interpolating the next logical step in an intuited pattern. The conclusion drawn is also called an inference. 62 . Knowledge management (KM) comprises a range of strategies and practices used in an organization to identify, create, represent, distribute, and enable adoption of insights and experiences. Such insights and experiences comprise knowledge, either embodied in individuals or embedded in organizational processes or practice. An established discipline since 1991 (see Nonaka 1991), KM includes courses taught in the fields of business administration, information systems, management, and library and information sciences (Alavi & Leidner 1999). More recently, other fields have started contributing to KM research; these include information and media, computer science, public health, and public policy.


56

recommendations at the urban site scale covering a wide spectrum of influence. At site scale,

strategy represents the very initial step towards the implementation of the plan. However site

strategies also entail a higher sphere of decision that usually defines a development path for a

wider region (Evans et al. 2007). Such path focuses on a site within a region (a “part” within

“whole”). The purpose is to obtain a territory balance surpassing the neighbourhood scale

range (J. C. Moughtin et al. 2003).

When this process starts?

An important aspect concerning planning actions is related with the necessity of defining a

plan’s strategy before planers start their design conceptions. Planners have to consider the

demands of the strategy to fit the design into the strategy’s goals.

What is the role of planners in this process?

If one states the problem in a plain and rhetorical way, on one side of the formulation process

stands the promoter, public or private, with a particular set of goals and, on the other side,

complementarily, stands the planer with its particular design approach. The first has the

responsibility to conceive the macro strategy and the general confinements for the plan. The

second has to collect data from the defined strategy and the plan’s confinements to transform

it into spatial solutions, supporting these on design guidelines. However, the involvement of

planners in the development of plans varies according to the context. Planners are usually

skilled to participate throughout the formulation of the program and in the creation of designs.

However, they also can participate in the elaboration of strategic plans contributing within an

ample participative process (Graham & Marvin 2001b).

How to elaborate a strategy?

One can say there is no universal formula to define a strategy. The existing

methodologies are varied, however, the instruments that regulate the elaboration of strategic

plans are relatively well known.

What is then a strategic plan?

A strategic urban plan63 (SUP) is a planning instrument that holds in its framework a wide

number of subjects to inquire (McCallum et al. 1996). Such variety requires the use of a

multiplicity of methodologies and tools to identify and clarify the subjects focused on

63 . A strategic plan lets an organization know where they are now and where they want to be some time in the future.


57

population and site features (Healey 1997). At the core of the strategic outline are placed the

economic, the cultural, the social and the political features, frequently identified using tools

like SWOT (Strengths, Weaknesses, Opportunities, Threats) depicted in Diagram 22, and PEST

(Political, Economic, Social, and Technological analysis) methods. In addition, other tools may

be use to deal with complementary matters such as “PERS (Pedestrian Environment Review

System) by TRL Software (http://www.trlsoftware.co.uk/: May 2008) or “Design Quality

Analyser” by CABE (http://www.whichplaceswork.org.uk/: May 2008)”, (Gil 2008) amongst

others.

Despite its relevance. SUP methods are not generalized in urban design processes yet.

22. SWOT analysis - the strengths, the weaknesses, the opportunities, and the threats.

SUP is today considered a type of Governance. According to Borja and Castells (1998), “the

definition of a city project that unifies diagnoses specifies public and private actions and

establishes a coherent mobilization framework for the cooperation of urban social actors. A

participative process is a priority when defining contents, as this process will be the basis for

the viability of the objectives and actions proposed. The result of the Strategic plan should not

necessarily be the creation of regulations or a government program (although its adoption by

the State and Local Government should mean the instigation of regulations, investment,

administrative measures, policy initiatives, etc.) but rather a policy contract between public

institutions and civil society.”

What are SUP’s assessment instruments and methodology?

Strategic plans are framed by specific frameworks towards the definition of a plan’s

purposes (Mintzberg 2000b). Usually they consist of the following five main concepts:


58

1) Vision – to define the vision / to set a mission statement with a hierarchy of goals;

2) SWOT – to analyse according to the desired goals;

3) Formulate64 - to prepare actions and processes that can be taken to attain these

goals;

4) Implement – to implement of the agreed upon processes; and

5) Control – to monitor and get feedback from implemented processes to fully control

the SUP operation.

There are a number of ways to develop SUP’s but typically a three-step process may be

used to support the development of the mentioned concepts above (Mason & Mitroff 1981):

1) Situation – to evaluate the current situation and how it came about,

2) Target – to define goals and/or objectives (sometimes called ideal state),

3) Path – to map a possible route from the goals to the objectives.

One alternative approach is called Draw-See-Think;

1) Draw – where the key question is: what represents the ideal image or the desired end

state?

2) See – setting the relevance of observing reality: what is today's situation? What is the

gap from ideal and why?

3) Think – defining a prospective path: what specific actions must be taken to close the

gap between today's situation and the ideal state?

4) Plan – and finally, how to make it: what resources are required to execute the

activities?

An alternative to the Draw-See-Think approach is called See-Think-Draw;

1) See - what is today's situation?

2) Think - define goals,

3) Draw - map a route to achieving the goals.

The diagram 23 depicts strategies as the initial phase of the pre-design process (PD1).

Moreover an example of SWOT and PEST tables is presented in Annex 3.

64 . Formulate corresponds here to a precise term used in SUP theory.


59

des

ign

(so

luti

on

)

con

text

(p

rob

lem

)

23. Strategies shown in the simplified diagram (highlighted in black text and box).

5.6. Regulations

Now, a closer look at the description of the second category of the formulation model -

regulations.

In summary, regulations consist of a compilation of urban rules, codes, standards, and

sorted requirements (Carmona et al. 2006). An important aspect related to its nature is the

width of its application.

Such aspect set a difficulty.

Urban rules generally differ from country to country, and from site to site within particular

geographic locations. In fact, this variation can be extended to the universe of cultural contexts

which comprise an infinity of variables. To solve width and differentiation one can search for

agreed upon rules applied to vast regions of the globe as the regulations applied to the

European Union (EU). However the dilemma remains. The EU is just a part of a wider universe

of urban realities and cultures, and additionally its administration largely lacks the legal means

to enforce regulations. This occurs since the “EU policy is mainly focused on structural policies

to regulate international trade and economic markets, employment, and industry sectors” (Hix

1999).

How rules are defined?

Rules usually depend on policy within state administrations, however social involvement of

communities in the definition of specific constraints or rules for plans, can add performance

into plans by adequating designs to community’s particularities (Harvey 2009). Such


strategic plans (I)

urban codes and guidelines

(II)site and population context

(III)


pattern language


(IV)


60

community participation generally occurs during the SUP framework or following it, in the

definition of the plan’s final layout. However, it can also occur in other phases of the pre-

design framework as in the definition of urban rules to apply in the development of particular

plans.

Meanwhile one can argue that such processes involve several formalities and nuisances.

For now it will be just acknowledged the relevance of such a participation process.

Who should be involved in such a participation process?

In fact, all society can be involved in participation but some civil sectors seem to receive more

direct benefits with the application of regulations or design-codes than others. Thus they are

able to contribute with additional knowledge that can bring benefits to plans requirements as

a result of their particular visions and experiences.

Traditionally such sectors are composed by landowners, developers, local authorities, as

the community in general (represented by individuals or a committee) (Evans et al. 2007).

Table 1 shows the mentioned participants and the roles that they play in the

formulation framework.

1. Benefits of design regulations (Evans et al. 2007)


61

des

ign

(so

luti

on

)

con

text

(p

rob

lem

)

In future research it will be elaborated a catalogue of regulations within a case-study. The

objective is to create a consensual directory of standards. Such directory will represent a

particular universe giving the prototype model of the formulation process some accuracy. Such

directory will be organized into modules that can be retrieved and manipulated by planners to

inform the development of a particular plan.

In general the directory will contain a sort of regulatory controls (includes on-site and

off-site considerations) such as:

a) LRDP land-use designation,

b) urban codes and requirements,

c) precinct or area plans,

d) site zoning and surrounding area zoning,

e) existing land-use type and density,

f) permitted uses and exemptions,

g) deed restrictions and covenants,

h) setbacks (lot coverage, and height limitations),

i) parking requirements, and

j) signage requirements.

The diagram 24 shows the regulation’s box that follows the strategies box in PD1. In Annex 4

are presented some core attributes, which are usually necessary to apply regulations.

24. Regulations and codes shown in the simplified diagram (highlighted in black text and box)


strategic plans

(I)


site and population context

(III)


pattern language


(IV)


62

5.7. Site and population context

Site and population data is normally acquired during (or as sequence of) the strategic phase

(Rogers & Vertovec 1995). In such a phase, if the SUP defines, for example, a specific goal

towards the increasing of social cohesion within a plan. A planner may also want to extend or

transform SUP defined goals to respond to other needs of the plan. In this case is important to

maintain a high level of flexibility within the formulation model so that management of the

formulation database (by planners) can be made in a flexible and extensive way.

The selection of such data is basically built on:

i) population statistics, and

ii) site analysis (sub-divided into ii1, ii2, ii3, and ii4).

Now, let us take a closer look at each of the data groups.

i) Population statistics

A relevant part of population data can be directly gathered from national and international

statistical institutes, (UNESCO) 2009; Balachandran 1980) which generally provides for vast

amounts of information related to people. Usually such information supports community’s

characteristics where profiles are ordered by age, ranges, skills, professions, genders,

economic stratification, etc. This information is frequently well supported by relational65

diagrams that help the visualization of data correlation66. Data correlation can be managed by

the organization of features into charts in order to express specific values that can be divided

by timeline periods. These diagrams portray global tendencies (Chenery & Taylor

1968)(Montgomery 2008) to define or correct planning streams.

65 . A relational database matches data by using common characteristics found within the data set. The resulting groups of data are organized and are much easier for people to understand. 66 . A correlation function is the correlation between random variables at two different points in space or time, usually as a function of the spatial or temporal distance between the points. If one considers the correlation function between random variables representing the same quantity measured at two different points then this is often referred to as an autocorrelation function being made up of autocorrelations. Correlation functions of different random variables are sometimes called cross correlation functions to emphasize that different variables are being considered and because they are made up of cross correlations. Correlation functions are a useful indicator of dependencies as a function of distance in time or space, and they can be used to assess the distance required between sample points for the values to be effectively uncorrelated. In addition, they can form the basis of rules for interpolating values at points for which there are observations.


63

Population represents a crucial factor towards the definition of a plan which is quite

simple to explain. Urban space is designed by people and for people. Communities are the

main purpose and the main constraint of a plan - its first ingredient. Therefore population

characteristics determine and deeply influence spatial configurations.

ii) Site analysis67

Site analysis is the second top group of contextual data, which consists of a crucial task

of the pre-design process implying spatial perception (Pereira 1996).

Such a relevance is easy to depict.

Space is perceived, apprehended, and seized by humans. Lynch (1960) wrote that users

understood their surroundings in consistent and predictable ways by forming mental maps. By

providing a framework to take this world of visual and formal perception into account, Lynch’s

“The Image of the City” has had important and durable influence on the field of urban

planning. Today’s methods of site analysis, deeply embedded in Lynch’s principles, are

elaborated through a collection of visual annotations, and interpreted through simple

statistical charts. Nevertheless, such a process reveals a variety of flaws due to cognitive

constraints on the behalf of the observer.

Site analysis is also extensive. To explicit its different implications and methods one

presents a partition within four types of site analysis.

The first is based on a method to read the image of an urban area (Pereira 1996), as

described in following.

ii.1) Site Analysis (The image of the city)

According to Pereira (Pereira n.d.), the analysis an urban area needs to take into account:

1) a global interpretation,

2) the problems and potentials,

3) the urban character,

4) the dynamics of transformation, and

67 . The analysis phase is a chronological step of the design process. This step involves programming the site as well as site and user analysis, which is focused on in-depth below. There are numerous site elements related to the analysis during this phase.

http://en.wikipedia.org/wiki/Urban_planning

http://en.wikipedia.org/wiki/Urban_planning


64

5) the objectives, the politics, the strategies, the solutions, and intervention actions.

Such site scrutiny requires a methodology of implementation, that is;

a) the preparation of a technical team,

b) the organization of its actions, and

c) the compilation of assessment studies.

In general, this type of analysis starts from the description of the urban context (Rogers &

Vertovec 1995), namely through the description of its morphology, the stages of its historical

evolution and urban demography, its social-economic growth, its accessibilities, its general

distribution of population and land uses, its landmarks (centrality, historical, and touristic), its

planning strategies, and its urbanization plans and public works (existing or approved, its

constrains and commitments).

Its fieldwork68 usually involves the use of cartography and photography, toponymic

analysis, street interviews and registration of opinions through blogs, sites or other media,

noise levels registration (in the case of an absence of noise-charts these data are registered

directly on site), etc. The use of complementary studies also helps on data collection in matters

such as landscape morphology (plateaus, lines of the landscape, slopes, three-dimensional

representation of land), urban historical evolution (the stages of urban formation, the

demassification and growth of the urban structure), social patterns, ideals and the models of

city, social-economic characterization of the population (age, composition of the families,

activities), neighbourhood relationships, as well as the forms of access to the interior of the

urban site under study.

The physical and visual inspection of the site is considered a crucial step in this type of

urban analyses. There are several aspects that need to be taken into account in the analyses,

namely;

1) the recognition of approach pathways,

2) the definition of the site peripheral edge,

3) the recognition of connections with the surroundings,

68 . Field work is a general descriptive term for the collection of raw data. The term is mainly used in natural and social sciences

studies. It is more technically known to scientific methodologists as field research. Field work, which is conducted in situ, can be

contrasted with laboratory or experimental research which is conducted in a quasi-controlled environment.


65

4) the morphologic continuities and discontinuities, and

5) the observation of distant focal points.

Such analysis follows the elaboration of a chart list (Pereira 1996) that can be described as

follows:

a - Site Data

a1 Morpho-typological structure:

a1.1 Land form and features (topography: slope and aspect).

a1.2 The formation bases of the urban structure (site morphology and

historical evolution),

a1.3 Land use (general indicators of land use),

a1.4 Urban mesh (grids and their orientation, mesh geometry, public spaces

network),

a1.5 Urban space (public space, private space and semi-public space),

a1.6 Built space (building construction dates, types of construction, current

and state of conservation, typologies, patrimony, delimitation of rundown

areas, costs, regeneration strategies).

a2 Active structure: Activities during the day, night and weekends:

a2.1 Housing, equipment, public administration and economic activities,

a2.2 Transportation, parking, loading docks and circulations,

a2.3 Leisure, culture and exchanges.

a3 Social structure:

a3.1 Characteristics of the population: Socio-economic, age and ethnic

groups69,

a4 Significant structure:

a4.1 System of orientation,

a4.2 Axes,

a4.3 Focal points,

a4.4 Passages,

a4.5 Reference areas,

69 . a) 3.1 can overlap with content in i), population statistics. One of the tasks of the Protégé 2000 Editor is to clean up such type

of occurrences in order to prevent ambiguities.


66

a4.6 Tourist routes.

b - Urban furniture and symbols, plant and animal species:

b1 Illumination,

b2 Culture and leisure,

b3 Information and orientation,

b4 Services to the community,

b5 Security,

b6 Vegetal and animal species.

c - Urban character:

c1 Sequences of public spaces,

c2 Sequences of façades,

c3 Significant details,

c4 Pallet of colors,

c5 Human environment,

c6 Environment of the installed activities and circulation,

c7 Sound and light environment,

c8 Vegetation and landscape.

d - Urban Dynamics:

d1 Modernity or traditionalism of the activities,

d2 Socio-economic contrasts.

e - Climate:

e1 Prevailing winds (direction and velocity),

e2 Solar orientation (including shade and shadows),

e3 Temperature ranges and seasonal norms,

e4 Humidity,

e5 Precipitation.

f - Environmental influences:

f1 Noise levels,

f2 Odors,

f3 Fumes,

f4 Dust,

f5 Smoke from adjacent sites,


67

f6 Air quality,

f7 Vibration, and

f8 General nuisances.

ii.2) Site Analysis (Data Mining)

Data mining comprise a very specific type of site analysis related with the use of technology.

The relevance of its methodology and specificity calls for its description.

What is then Data Mining?

A data mining (DM) process is “characterized by a recursive withdrawal procedure

enthused by a statistical platform towards data emergence, and is commonly used to perform

three main tasks” (Fayyad et al. 1996):

1) Classification - arranging the data into predefined groups,

2) Clustering - where the groups are not predefined and the algorithm tries to group

similar items together, and

3) Regression - to find a function which models the data with the least error.

Technically data mining is the process of finding correlations or patterns among dozens of

fields in large relational databases. The relevance of these techniques to the planning process

is that they allow users to analyse data from different angles, categorise, and summarise the

relationships identified. Data mining seems to facilitate the discovery of patterns that would

be difficult to reveal in a complex urban space, today controlled by a bursting environment of

economic and social phenomena.

ii.3) Site Analysis (Space Syntax and other methodologies - Hillier, 1984)

This group embraces another important chapter in spatial analysis.

Space syntax is based on linear representations of space (desire lines theory) through

definitions of lines and segments (axial mapping methodology). One of the methodologies

used by space syntax (SpS) is characterized by network measurement through graphs

representations. The results of the applied theory seem to provide accurate results that are

essentially focused on social and traffic assessment. The aphorism of the space syntax (Bafna

2003) is based on visual space perception and physical space recognition; “to see how much

we can learn about (…) surroundings without taking into account intent” , defining as main

subjects under study; “moving people, bicycles or vehicles”. The method usually encloses a


68

number of street locations covering a range of well-used, moderately-used and poorly-used

spaces, in and around a defined area of study. SpS methodology identifies a set of “gate”

positions. “The more gates, the more accurate the picture of movement patterns” (Batty 2004).

Finally, it defines a simple procedure, by “stand at each gate position and draw an imaginary

line crossing the street space (the line should be at a right angle to the direction of the street)

(see map and observation sheet 2). Those gates are spots where one will count the people or

vehicles that cross this line for a set period of time. Some of the instructions are; “Count only

the people or vehicles that have crossed the line. The time periods vary from 2.5, 5 to 7.5

minutes depending on the busyness of the area: shorter times for busy areas and longer times

for quieter areas. The time period should be as precise as possible down to the nearest

second. Always record the time period. This is so that when times are multiplied up to arrive at

rates per hour no mistakes are made” (Hillier et al. 1976).

2. Gate Counter Map and Observation sheet example.

ii.4) Site Analysis (Measuring quality)

The last group of site analysis is focused on quality standards. It states that “measuring quality

means involving a variety of interested people to define how well a space works. Through this

process one can learn about requirements of different groups of people to understand if their

needs are being met. It will identify both good and bad characteristics and stimulate new ideas

for improvements and how it could be managed” (Dempsey 2008). This process will help to


69

develop good relations between users and people who run space and will help one prioritise

improvement. By measuring quality70 one is basing such decisions on good evidence.

In summary the existing tools for measuring quality can be used for:

a) identifying the strengths and weaknesses of a space,

b) establishing what is most important to people,

c) comparing different people’s views,

d) measuring how well the space meets everyone’s needs,

e) stimulating new ideas for improvements,

f) tracking changes in people’s views over time,

g) bringing staff and users together in a structured way to discuss the space (Hurley et

al.)

The use of Site Analysis within the formulation model.

The techniques used in SUP’s and in the site analysis phase, involving this type of information,

can be similar despite the different moments in which they occur during the pre-design phase.

The question concerning the necessity of covering twice the same problems using similar

methodologies (in SUP and in the Site Analysis) needs to be addressed by the participants of

the plan that should decide if the sources of the SUP are sufficient or even if they should be

presented taking into account the specific requirements of the design and pre-design phase.

Finally, the diagram 25 represents the site and population contextual data acquisition as

the last process of the pre-design phase 1 (PD1). Once more it can be mentioned that order of

the processes described in the diagram can be structured differently, according to the plan’s

specificity.

70 . A quality (from Latin qualitas) is an attribute or a property. Attributes are ascribable, by a subject, whereas properties are

possessible. Some philosophers assert that a quality cannot be defined. In contemporary philosophy, the idea of qualities, and

especially, how to distinguish certain kinds of qualities from one another remains controversial (Thomson et al. 2003).


70

des

ign

(so

luti

on

)

con

text

(p

rob

lem

)

25. Site and population contextual data shown in the simplified diagram (highlighted in black text and box)

5.8. Document synthesis

There is too much information for analysis?

The wide-range of matters under analysis seems to be endless, sometimes confusing and

frequently overlapping. Such extended data is difficult to understand in order to organize and

select. The task is especially hard when such information is manipulated by several parties with

different visions about its management.

The data from the analysis phase is useful for the plan’s stakeholders only when

inclusive, limited and precise. Though is crucial to have the perception of what to do with it.

So, how to deal with such an amount of information?

In this phase, it is essential to elaborate a final document to summarize all the compiled data.

This document is usually called synthesis document (SD) and its structure is defined by: a)

objectives, guidelines and strategies, as well as b) a formulation of hypotheses towards

implementation.

Pereira (1996) recommends a set of five instructions to define SD’s.

a) the first describes the need to elaborate general cost estimates and execution phases

for the plan,

b) the second defines all formulation rules for implementation on site,

c) the third defines the financial resources required to implement the plan,

d) the fourth identifies the social participants involved in such implementation, and


strategic plans

(I)urban codes and guidelines

(II)



pattern language


(IV)


71

e) the fifth develops a series of public discussions through media (meetings, blogs, etc.).

What are the data attributes?

It is difficult to describe with precision what represents (in a moment) each contextual entity

because it changes according to the plan’s requirements. However, is possible to describe a

relatively consensual database of their related attributes71.

Annex 4 provides a list of attributes related to contextual data, including their proposed

codes, the description of their types, their reference values, their data sources, and also their

related calculations. The tables express a set of attributes usually used in Buildings, Blocks,

Plots, and Streets configurations. Such tables were developed as the database for data-mining

research (Gil et al 2009).

5.9. Planner’s Language – pattern language

This category of the formulation process is located in the second phase of the pre-design

process – the PD2, and it is essentially related with the description of design specifications.

A first question arises immediately concerning this topic.

The development of the formulation database seems to reduce the role of planners in decision

making concerning the planning actions. In contrast, planners are crucial elements in the

management of the formulation model, as they can also act as its system engineers (the

administrators of the formulation ontology and its rule based system). The idea is that some

balance can be made.

Another important role of planners is to achieve consensus amongst stakeholders by

harmonizing different visions regarding a site. Consensus is hard to manage specially before

the wideness of participants possessing dissimilar interests. Table 3 and 4 gives one a notion

concerning stakeholder’s roles, as well as their short-term value/ long-term value within a

plan’s development. This management consists, largely, of a planner’s language the planner’s

communication acts.

71 . The word ‘attribute’ can express: in philosophy - property, an abstraction of a characteristic of an entity or substance; in social sciences - a characteristic of a variable; in linguistics - a syntax unit, either a word, phrase or clause, that modifies a noun; in computing - attribute (computing), a factor of an object or other kind of entity.

http://en.wikipedia.org/wiki/Philosophy

http://en.wikipedia.org/wiki/Property_%28philosophy%29

http://en.wikipedia.org/wiki/Abstraction

http://en.wikipedia.org/wiki/Characteristic

http://en.wikipedia.org/wiki/Entity

http://en.wikipedia.org/wiki/Social_sciences

http://en.wikipedia.org/wiki/Syntax

http://en.wikipedia.org/wiki/Attribute_%28computing%29


72

3. Participants in the planning process (Evans et al. 2007) – part 1

4. Participants in the planning process (Evans et al. 2007) – part 2


73

des

ign

(so

luti

on

)

con

text

(p

rob

lem

)

During the second pre-design phase planners are called to produce the largest part of their

work before starting the design of a plan. It is a complex activity involving different knowledge

domains, some linked to personal assumptions72.

Planners communicate their ideas by a sort of language, a planning language composed

by; a) a particular lexicon73 (built of patterns), b) a specific organization (syntax74), congregating

a c) group of intentions (a semantic corpus).

This language will determine the structure of the design specifications.

Diagram 26 shows the location of the planning language box (the planner´s pattern language)

in the overall pre-design process - phase 2 (PD2). This process is further described in the

Chapter 6.

26. Pattern language “specifications for design” shown in the simplified diagram (highlighted in black text

and box)

72 . An assumption is a proposition that is taken for granted, as if it were true based upon presupposition without preponderance of the facts. Assumption may also refer to: in logic, natural deduction systems are defined as an assumption is made in the expectation that it will be discharged in due course via a separate argument; in mathematical modeling it can be used to map the outcome of different assumptions on the system being modeled. 73 . In linguistics, the lexicon (from the Greek: Λεξικόν) of a language is its vocabulary, including its words and expressions. More formally, it is a language's inventory of lexemes (Podgorski 2008). 74 . In linguistics, syntax (from Ancient Greek σφνταξις "arrangement" from σφν syn, "together", and τάξις táxis, "an ordering") is the study of the principles and rules for constructing sentences in natural languages. In addition to referring to the disc ipline, the term syntax is also used to refer directly to the rules and principles that govern the sentence structure of any individual l anguage, as in "the syntax of Modern Irish. Modern research in syntax attempts to describe languages in terms of such rules. Many professionals in this discipline attempt to find general rules that apply to all natural languages. The term syntax is also s ometimes used to refer to the rules governing the behavior of mathematical systems, such as logic, artificial formal languages, and computer programming languages (Santorini & Kroch 2000).


strategic plans

(I)urban codes and guidelines

(II)site and population context

(III)


pattern language


(IV)

http://en.wikipedia.org/wiki/Proposition

http://en.wikipedia.org/wiki/Truth


74

des

ign

(so

luti

on

)

con

text

(p

rob

lem

)

This chapter describes the formulation process as well as the design phase where it occurs – the pre-design phase.

The main objective is to provide a better understanding of the formulation framework and, at the same time, to generate the sketch of its semantic ground, that is, a consensual vocabulary to enable appropriate specifications (or the “communication acts” towards design solutions. The main categories of information that compose the framework are:

1. Strategic plans 2. Codes and guidelines 3. Contextual data 4. Specifications for design.

27. The diagram shows the four different categories of the formulation process. Each one enclosing a

crucial process within the pre-design framework (black text and boxes).

05

• detailing each category of the pre-design-phase


strategic plans (I)




pattern language


(IV)


75

The chapter 6 explores the most

important category of the

formulation model: the

systemic language that is used

by planners to formulate urban

solutions, in short, the planner’s

language.

Chapter 6

6. The Sketch of a Planning Language


76

6.1. Introduction

Language, like a seed, is the genetic system which

gives our millions of small acts the power from the

whole (Alexander et al. 1977).

The chapter 6 explores the most important category of the formulation model: the systemic

language that is used by planners to formulate urban solutions, in short, the planner’s

language. When a designer is designing something, whether it is a house or a computer

program or a stapler, s/he must make many decisions about how to solve problems. A single

problem, documented with its most common and recognized good solution seen in the wild, is

a single design pattern. Each pattern has a name, a descriptive entry, and some cross-

references, much like a dictionary entry. A documented pattern must also explain why that

solution may be considered a good one for that problem, in the given context.

How to describe such a language?

The description of a specific domain language beyond natural languages requires an

appropriate disclosure of its specific system. The first step to find out how language is

structured is by searching deep its semantic field, here consisting of particular meanings

encoded in the form of patterns. Another complementary way is to explore the structure of

language in the way subjects are described (in multiple combinations), to understand how

logic is created.

To better understand this process is important to discover the way natural languages

function.

6.2. The nature of language

Language is a system of signs to express meanings - a simple algorithm to produce

communication. What seems to be interesting in the universe of communication is the analogy

that can be established between different fields of knowledge that seem to be linked by

equivalent concepts. This seems to be the case of the planning language and the linguistics

theory.

Natural language is composed of a sort of definitions supported by:

http://en.wikipedia.org/wiki/Design_pattern

http://en.wikipedia.org/wiki/Dictionary


77

a) a syntax and

b) semantics.75

While syntax corresponds to structure, semantics is associated with meaning (Chomsky 1965).

Together they compose a medium for communication - a definition that seems to fit in the

description of the planning language. Such a link was established in the ‘70s by Christopher

Alexander (1977) who inspired by natural languages attempted to adapt the linguistics76

concept to planning.

6.3. Semantics and Syntax

Now, a closer look at the main components of language.

In summary, language is composed by:

a) syntax, which contains Classes - taxonomies of “speech” (ideas / semantic level),

Inflections - changes or mutations of the language ingredients, and Codes - description

of possible combinations, and

b) semantics, which is based on cognitive schemas that are mental models of different

aspects of the world, containing knowledge, opinions, suppositions, associations, and

expectations.

75 . Semantics is the study of meaning, usually in language. The word "semantics" itself denotes a range of ideas, from the popular to the highly technical. It is often used in ordinary language to denote a problem of understanding that comes down to word selection or connotation. This problem of understanding has been the subject of many formal inquiries, over a long period of time. In linguistics, it is the study of interpretation of signs or symbols as used by agents or communities within particular circumstances and contexts. Within this view, sounds, facial expressions, body language, and proxemics have semantic (meaningful) content, and each has several branches of study. In written language, such things as paragraph structure and punctuation have semantic content; in other forms of language, there is other semantic content. The formal study of semantics intersects with many other fields of inquiry, including proxemics, lexicology, syntax, pragmatics, etymology and others, although semantics is a well-defined field in its own right, often with synthetic properties. In philosophy of language, semantics and reference are related fields. Further related fields include philology, communication, and semiotics. The formal study of semantics is therefore complex. Semantics is sometimes contrasted with syntax, the study of the symbols of a language (without reference to their meaning), and pragmatics, the study of the relationships between the symbols of a language, their meaning, and the users of the language (Yule 2006). 76 . Linguistics is the scientific study of natural language. Linguistics encompasses a number of sub-fields. An important topical division is between the study of language structure (grammar) and the study of meaning (semantics and pragmatics). Grammar encompasses morphology (the formation and composition of words), syntax (the rules that determine how words combine into phrases and sentences) and phonology (the study of sound systems and abstract sound units). Phonetics is a related branch of linguistics concerned with the actual properties of speech sounds (phones), non-speech sounds, and how they are produced and perceived. Other sub-disciplines of linguistics include the following: evolutionary linguistics, which considers the origins of language; historical linguistics, which explores language change; sociolinguistics, which looks at the relation between linguistic variation and social structures; psycholinguistics, which explores the representation and functioning of language in the mind; neurolinguistics, which looks at the representation of language in the brain; language acquisition, which considers how children acquire their first language and how children and adults acquire and learn their second and subsequent languages; and discourse analysis, which is concerned with the structure of texts and conversations, and pragmatics with how meaning is transmitted based on a combination of linguistic competence, non-linguistic knowledge, and the context of the speech act.

http://en.wikipedia.org/wiki/Language

http://en.wikipedia.org/wiki/Linguistics

http://en.wikipedia.org/wiki/Agent_%28grammar%29

http://en.wikipedia.org/wiki/Community

http://en.wikipedia.org/wiki/Proxemics

http://en.wikipedia.org/wiki/Proxemics

http://en.wikipedia.org/wiki/Lexicology


http://en.wikipedia.org/wiki/Pragmatics

http://en.wikipedia.org/wiki/Etymology

http://en.wikipedia.org/wiki/Philosophy_of_language

http://en.wikipedia.org/wiki/Reference

http://en.wikipedia.org/wiki/Philology

http://en.wikipedia.org/wiki/Communication

http://en.wikipedia.org/wiki/Semiotics


http://en.wikipedia.org/wiki/Pragmatics


78

The language is also structure by a sort of combinations, namely by:

b1) structuring sounds into digitized segments (phonemes) which are sequenced into

fixed combinations (words/morphemes);

b2) treating these sequences as symbols for concepts (meanings) that can be used in

many different situations; and

b3) organizing sequences of words/morphemes into hierarchical phrases and sentences

(syntactic structures) whose meanings are constructed systematically from the

meanings of the words (Chomsky 2002).

Alexander’s pattern language (PL) (1977) is organized in a similar way despite its specific

vocabulary involving space and people, public and private, natural and artificial things and

concepts. As natural languages, PL morpho-syntax describes the elements of language. For

example, PL number 19 relates to shops, people needs, centres, services, and then organizes

them into a simple sequence of meanings (three interdependent concepts); “catch basis”77 for

services to serve people’s needs. At the end, it sets a design solution; the creation of a “web of

shopping”.

In what consists PL structure?

Alexander considers that planning language has a more complex functioning than a tree of

hierarchies as proposed by Chomsky for natural languages (Chomsky 1965). For Alexander

such a tree does not explain language usage, since language seems to depend much more on

the type of selection of the lexicon to produce particular results that express particular

meanings. Alexander calls it semi-reticulated language due to its recursive and combinatorial

system based on user multiple choices and heuristics.

Diagram 28 represents a Chomsky’s tree structure78 hierarchy for natural languages,

dissimilar from Alexander’s PL.

77. Alexander’s expression. 78 . A tree structure is a way of representing the hierarchical nature of a structure in a graphical form. It is named a "tree structure" because the classic representation resembles a tree, even though the chart is generally upside down compared to an actual tree, with the "root" at the top and the "leaves" at the bottom. In graph theory, a tree is a connected acyclic graph (or sometimes, a connected directed acyclic graph in which every vertex has indegree 0 or 1). An acyclic graph which is not necessarily connected is sometimes called a forest (because it consists of trees).


79

28. Language processing79

What are the main concepts of modern linguistics that fit in the planner´s language?

In linguistics theory language is described as a semantic phenomenon “situated in the mind of

the user” based on cognitive schemas (CS) (Croft & Cruse 2004) that represent mental models

of different aspects of the world. In design languages such a cognitive process could be

interpreted as the creative facet of planners. CS are difficult to encode into the formulation

model once it represents an important aspect of the planner’s role within the design process.

It is possible to establish analogies between modern linguistics theories and design languages,

which supports the idea that planners possess an analogous system of communication.

A closer look at three important concepts of modern linguistics is revealing and useful

for the remaining of our discussion:

1) Mentalism - Where language is situated in the minds of speakers. Herein language is

an idealization of the practices of a homogeneous community with a vast individual

variation and mixture - where every speaker commands a number of different records of

vocabulary and usage to be used in different contexts (Katz 1964).

2) Combinatoriality - Where language is a combinatorial system that can be used

creatively and systematically following a non conscious process. Linguistics and

psycholinguistics are concerned with discovering such a process as recent research on

psychology of vision suggests. Herein the important question concerns what aspects of

language does a speaker store in memory (including words, but also much else), and

what does a speaker construct in the course of speaking? The latter involves the use of

rules/schemas/principles that are applied creatively (Sugita & Tani 2005).

79 . Language processing refers to the way human beings process speech or writing and understand it as language. Most recent theories back the idea that this process is made completely by and inside the brain.


80

3) Inner resources for language learning - Where one acquires language by learning on

the basis of experiencing practices community. Herein the important question is what

are the inner resources that one brings to such a task? Some of the resources are fairly

general (e.g. ability to interact socially, ability to imitate). But some are specific to

language, a “language instinct” - a cognitive specialization of humans (Manning &

Schütze 2002).

6.4. Planners and Language processing

Planners manage creativity by formulating associations or bridges between patterns in a

process of multiple combinations – similar to the concepts of modern linguistics described

above. Such a process is related with two different aspects of creation: systematization and

instinctiveness. Formagio (1976) citing Heidegger refers that a piece-of-art is an exercise of

design over meaning only present within a context of illumination (systematization) and

occultation (instinctiveness).

Creativity seems thus to enclose;

1) revealed information (systematized) and

2) unexpected information (created by associations and inner management).

Several studies focused on design phenomenology have targeted their case-studies at the

iterative nature of creation. The aim of such protocol80 studies is the disclosure of the action

paths that occur during a planning phase when resolution and judgment (decision making) are

focal points. One of the techniques used is centred on monitoring discussions, drawings, and

comments of design teams, capturing the flow of ideas that lead to decisions and design

outputs.

80 . In natural sciences a protocol is a predefined written procedural method in the design and implementation of experiments. Protocols are written whenever it is desirable to standardize a laboratory method to ensure successful replication of results by others in the same laboratory or by other laboratories. Detailed protocols also facilitate the assessment of results throug h peer review. Protocols are employed in a wide range of experimental fields, from social science to quantum mechanics. Written protocols are also employed in manufacturing to ensure consistent quality. Protocol is also an agreement that governs the procedures used to exchange information between cooperating entities; usually includes how much information is to be sent, how often it is sent, how to recover from transmission errors, and who is to receive the information.


81

In a similar study, Lindekens (2004; 2007) describes a monitored process showing a

sample of solutions generated by a planning team. Moreover he describes how solutions are

converted into designs and how designs express personal assumptions in a cycle that flows

continuously. Such framework is useful to understand how humans behave when manipulating

concepts in order to produce results. The recognition and the description of these heuristics81

represents an opportunity for the definition of a common set of procedures with the objective

of creating design solutions in a more organized and systematic fashion.

5. Active drawing phase during the design (Lindekens 2004)

The following lines were taken from the discussion between planning team members. This

“chat” and drawing session was recorded by Lindekens (2004) as part of his research. Herein

team members express verbally their thoughts while making a set of sketches depicted in the

above figure. The session was recorded and surveyed for a time period (follows an extract of

the team’s comments).

0:43:54 - actually an important circulation … connection is still between… 0:44:03 - the elbow of the mill here 0:44:05 - because here the important steps are situated 0:44:08 - also the shortcut towards auditorium “de molen” [the mill] 0:44:12 - so this is an important circulation 0:44:17 - we the stairs over here … 0:44:20 - will be restored

81 . Heuristic is an adjective for experience-based techniques that help in problem solving, learning and discovery. A heuristic method is particularly used to rapidly come to a solution that is hoped to be close to the best possible answer, or 'optimal solution'. Heuristics are "rules of thumb", educated guesses, intuitive judgments or simply common sense. A heuristic is a general way of solving a problem. In more precise terms, heuristics stand for strategies using readily accessible, though loosely applicable, information to control problem solving in human beings and machines (Dechter & Pearl 1987).

http://en.wikipedia.org/wiki/Rule_of_thumb

http://en.wikipedia.org/wiki/Common_sense

http://en.wikipedia.org/wiki/Problem_solving


82

0:44:21 - the former stairs are continued till the ground floor 0:44:25 - so that a better connection is created that … 0:44:30 - that corridor with the gothic arches … towards … the stone staircase 0:44:37 - so we will make sure that this actually is the most important 0:44:41 - the most important route 0:44:43 - maybe here somewhere...

This fragment is insufficient to evaluate the planner’s language. The mechanism of thoughts is

difficult to understand, although it often seems to follow a random order where concepts and

scales are presented without a particular logical sequence. In such mechanisms, cognitive

assumptions are usually abundant in problem solving82. In fact, one of the challenges in the

elaboration of the formulation model is to create a system of predefined rules to avoid

randomness, enabling at the same time the contribution of planner’s cognitive assumptions.

The idea is to provide a logical structure to the model, as well as flexibility and manipulability.

6.5. Lexicon

The elaboration of “speech” in a language requires the manipulation of a lexicon (or

vocabulary). The urban design language enables a specific speech that entails a particular

glossary associated with spatial features. The difficulty related with such lexicon is the

vagueness of its semantic field and the breadth of its context. This is why it is so important to

create an ontology to define a right path towards the description of the urban lexicon.

How lexicon can thus be described?

The urban lexicon consists of descriptions of single entities (vocabulary) that compose of

groups of entities (patterns) ranging from concepts, geometric representations, land

classifications, urban object’s descriptions, to physical delimitations (see Diagram 29), etc.

Lexicon is thus the descriptive basis of patterns.

The way lexicon is combined to define patterns is in the way different matters concerning

urban space converge to form concepts (for space).

Diagram 29 gives an idea about such type of convergence.

82 . Problem solving is a mental process and is part of the larger problem process that includes problem finding and problem

shaping. Considered the most complex of all intellectual functions, problem solving has been defined as higher-order cognitive

process that requires the modulation and control of more routine or fundamental skills. Problem solving occurs when an organi sm

or an artificial intelligence system needs to move from a given state to a desired goal state (Goldstein & Levin 1987).


83

In the diagram, the abstract pattern entity located at its centre connects to a variety of matters

such as economy, geometry, population, boundaries, rules, etc. These matters correspond to

the vocabulary that supplies pattern’s semantics. Put simply, urban patterns are made of a

combination of entities (lexicon) and Diagram 29 depicts the basic ontological structure of

urban patterns.

29. The ontological diagram of an urban pattern (Montenegro, N.C. and Duarte, J.P., 2008)

Concluding with a simple syllogism: A given amount of entities or subject matters (a pattern’s

lexicon) congregated, create a pattern. The value and the combinations established between

these subject matters enable the creation of a set of different patterns. A consistent set of

patterns, running collectively, creates a particular language.


84

6.6. Pattern Language

The creation of a formulation instrument for urban design, proposed in this research, is

inspired in the principles described in Alexander’s series of books published in the 70’s, which

has created a new theory for urban design.

“A pattern language”83 (PL) provides a method for designs based on a language of

patterns for things ranging from rooms to towns. The “Timeless Way of Building” (Alexander

1979) provides the theory and instructions for the use of the language that made it possible to

use the patterns to create a building or a town. Alexander’s premises depart from linguistics

theory, which defines language as a combinatorial and creative method of communication in

different contexts, with clear instructions, and within a defined vocabulary. The aim for this

language is to allow users to manipulate creatively its ingredients to develop an appropriate

speech that is a design adequate to a given context.

In planning, such characteristics are particularly useful for enabling a creative and

flexible process.

Pattern Language main concepts

Pattern Language is a term denoting elements of language (Alexander et al. 1977).

In the formulation model presented here, language is the metaphor for urban design

whilst urban space. Patterns or elements of language correspond then to recurrent urban

features or events where each pattern has a particular structure described by;

a) a composition with a set of minor elements related to urban space,

b) a definition of a recurrent urban problem or event, and

c) a description of a solution to solve the urban problem.

Patterns are akin to recipes in which by combining a collection of ingredients or metadata84

according to specified routines one may arrive at adequate descriptions of solutions and then

at solutions themselves through design generation.

83 . Patterns hide a lot of cultural and conceptual "baggage," providing a compressed intensity and an economy of expression in return. Patterns users who experience the power of patterns have acquired this baggage, either tacitly (through repeated use) or explicitly, by studying the literature. This is the opposite of models, for which syntax and semantics act as decoder rings for the model message. But it also explains the complementary ability of models and patterns as tools for successful and quality-focused software development (Evitts 2000). 84 . The first use of the term “Metadata” has been attributed to Jack E. Myers who subsequently trademarked the word. Since then the fields of library science, information technology and GIS have widely adopted the term. In these fields the word metadata


85

The idea of recurrence85 within patterns was developed by Alexander and defines an important

logic within the language and its syntax, that is the combinatorial and recursive way patterns

can be manipulated allows one to use them indefinitely and extensively. This feature permits

one to use the language in a creative way.

Urban patterns are basically formulated to assist planners86. Thus planners are pattern

administrators possessing an intermediary level of intervention towards the development of

solutions. In the formulation model patterns should generate some kind of proficient solutions

even with a low managing intervention of planners. This means that the formulation model

comprise a mechanism for, departing from a description of a context; arrive at a description of

a solution.

Formulation patterns, which are deeply inspired in Alexander’s formalities, follow its basic

scheme, that is;

1) all patterns have the same format,

2) patterns are defined by a picture with the archetypal example of the pattern,

3) each pattern has an introductory chapter that sets its context,

4) patterns are defined by one or two sentences defining the essence of its problem,

5) the problem is described in the longest section of the structure – pattern background,

its validity, and the ranges of its manifestation,

6) the solution, the heart of the pattern, given by a set of precise instructions to

guarantee instances of patterns,

7) the description of the solution in a diagram with its main components,

is defined as “data about data”. While this is the generally accepted definition, various disciplines have adopted their own more specific explanation and uses of the term (Dudley n.d.). 85 . In mathematics, a recurrence relation is an equation that recursively defines a sequence: each term of the sequence is defined as a function of the preceding terms. 86 . • From a broad perspective, a pattern can be seen as a form for documenting best practices. In a profession that lacks centuries of experience and scientific underpinnings of engineering, architecture, or urban planning, best practices have become a touchstone for ensuring that risks are understood and that commonly accepted techniques and technologies are used. Patterns provide a standard format and repository for them—replacing what has been, until now, anecdotal reporting and documentation of the best ways to do things. • From a narrower perspective, a pattern can be seen as a rule of thumb: a heuristic—quick way of providing a starting point for solving a problem. The craft of software development has generated many rules of thumb in its brief history. Patterns can provide a home for them that is formalized without being fussy. • Finally, and even more narrowly, a pattern can be viewed and used as a template. This definition captures a critical aspect of patterns: they can be applied over and ov er again, in different situations. They are not specific solutions, but rather the basis for a solution. And, in software development, they derive from the fact that software solutions themselves tend to be repetitive. There is only a small set of solutions for any design problem in information systems, whether the problem is in software architecture or in development organization (Evitts 2000).


86

8) the set of smaller patterns related with the specific pattern in order to complement or

enrich its description (similar to (Alexander’s, 1977: xi)).

The following text corresponds to a stretch from the Alexander’s Pattern Language book

(1977). Texts and figures are taken from the book to portrait the above pattern structure. The

pattern shown in following is the 26th of Alexander’s patterns, called “Life Cycle”.

”26. LIFE CYCLE

(…) real community provides, in full, for the balance of

human experience and human life - COMMUNITY OF 7000 (12).

To a lesser extent, a good neighborhood will do the same -

IDENTIFIABLE NEIGHBORHOOD (14). To fulfill this promise,

communities and neighborhoods must have the range of things

which life can need, so that a person can experience the full

breadth and depth of life in his community (…)

Therefore

Make certain that the full cycle of life is represented and balanced in each community. Set the ideal of a

balanced life cycle as a principal guide for the evolution of communities. This means: 1. That each

community include a balance of people at every stage of the life cycle, from infants to the very old; and

include the full slate of settings needed for all these stages of life; 2. That the community contain the full

slate of settings which best mark the ritual crossing of life from one stage to the next.

To live life to the fullest, in each of the seven

ages, each age must be clearly marked, by

the community, as a distinct well marked

time. And the ages will only seem clearly

marked if the ceremonies which mark the

passage from one age to the next are firmly

marked by celebrations and distinctions.

By contrast, in a flat suburban culture the seven ages are not at all clearly marked; they are not

celebrated; the passages from one age to the next have almost been forgotten. Under these conditions,

people distort themselves. They can neither fulfill themselves in any one age nor pass successfully on to

the next. Like the sixty-year-old woman wearing bright red lipstick on her wrinkles, they cling ferociously

to what they never fully had. This proposition hinges on two arguments. a) The cycle of life is a definite


87

psychological reality. It consists of discrete stages, each one fraught with its own difficulties, each one

with its own special advantages. b) Growth from one stage to another is not inevitable, and, in fact, it

will not happen unless the community contains a balanced life cycle.

(…) To re-create a community of balanced life cycles requires, first of all, that the idea take its

place as a principal guide in the development of communities. Each building project, whether the

addition to a house, a new road, a clinic, can be viewed as either helping or hindering the right balance

for local communities. We suspect that the community repair maps, discussed in The Oregon

Experiment, Chapter V (Volume 3 in this series), can play an especially useful role in helping to

encourage the growth of a balanced life cycle.

But this pattern can be no more than an indication of work that needs to be done. Each

community must find ways of taking stock of its own relative "balance" in this respect, and then define a

growth process which will move it in the right direction. This is a tremendously interesting and vital

problem; it needs a great deal of development, experiment, and theory.

x STAGE IMPORTANT SETTINGS RITES OF PASSAGE

1

INFANT

trust

Home, crib, nursery, garden Birth place, setting up the home . . . . out of

the crib, making a place

2

YOUNG

CHILD

autonomy

Own place, couple's realm, children's realm,

commons, connected play

Walking, making a place, special birthday

3

CHILD

Initiative

Play space, own place, common land,

neighborhood, animals

First ventures in town . . . joining

4 YOUNGSTER

Industry

Children's home, school, own place, adventure

play, club, community

Puberty rites, private entrance paying your

way

5 YOUTH

Identity

Cottage, teenage society, hostels, apprentice,

town and region

Commencement, marriage, work, building

6 YOUNG

ADULT

Intimacy

Household, couple's realm, small work group,

the family, network of learning

Birth of a child, creating social wealth . .

building

7 ADULT

Generativity

Work community, the family, town hall, a room

of one's own

Special birthday, gathering, change in work

8 OLD

PERSON

Integrity

Settled work, cottage, the family, independent

regions

Death, funeral, grave sites


88

The rites of passage are provided for, most concretely, by HOLY GROUND (66). Other specific patterns

which especially support the seven ages of man and the ceremonies of transition are HOUSEHUOLD MIX

(35), OLD PEOPLE EVERYWHERE (40), WORK COMMUNITY (41), LOCAL TOWN HALL (44), CHILDREN IN

THE CITY (57), BIRTH PLACES (65), GRAVE SITES (70), TEENAGE SOCIETY (84), CHILDREN'S HOME (86)

(…)”

This pattern is an example of the PL language scheme, which consists of links established

between the working pattern (26) with minor patterns (12), (14) of the language, as well as

with major patterns as (66), (35), (40), (41), (44), (57), (65), (70), (84), (86). Pattern (66)

denotes dominance over the others.

The following diagram shows the 26th PL ontology taxonomy.

30. An example of Pattern’s taxonomy (the created links between patterns)

Such description helps one to understand the role of each particular pattern within the

planning language.

PL fragilities

The work of Alexander stirred the field but had little practical impact. With a set of

comparative examples is possible to detect latent fragilities in the description of PL sorts. For

example, there are patterns that repeat analogous solutions (example: patterns 57 and 68)

due to the PL scale structure. There is also a lack of key patterns relevant for design such as

urban grids or network morphologies. Without them is very difficult to structure a plan. Some

other PL patterns are embedded in very particular cultural contexts which constrains the

possibility of a wider general use. Finally a large amount of PL patterns are applicable at a

larger scale (the city scale for example) than site planning scale, which is the focus of the

current.

major patterns related

working pattern

minor patterns related12

26

66 35 40 41 44 57 65 70 84 86

14


89

How then to select patterns?

To define a contemporary pattern language for the site planning scale, one may exclude

Alexander’s patterns based on three criteria: similar solutions, cultural context, and scale. In

general, patterns that can be excluded on scale refer to urban facilities that are normally

considered at a city scale (cemetery, sports facilities, etc). In the elaboration of a plan at a site

scale many of such patterns may have no applicability. However, if necessary, city scale

patterns can be inherited by site scale patterns in order to match SUP/SD criteria (PDP1). Such

patterns can then be included in the program. One possible way of managing large scale

patterns is to create a module for them, in order to prevent the creation of a pattern language

with a high number of exceptional patterns. To exclude patterns based on cultural criteria is

more difficult, in a simplified model, due to the complexity and the extension that involves

such descriptions. To surpass this difficulty one proposes a formulation “module”.

The idea is simple.

The basic structure of the formulation model is built on an agreed set of structural patterns,

where modules can be added or removed to adjust the language of the program to the context

of the plan and to the users of the urban program. The modules will include, among others,

different cultural contexts and theoretical models of the city. This means that, this way, the

formulation model is built in an extensible and combinatorial way, and it will be always a work

in progress. This flexibility sets one of the key concepts of the proposed model.

The assessment of related studies in order to revise and improve PL concepts and

patterns might be necessary in the development of a contemporary pattern language. Thus a

set of new patterns has to be introduced into the language to take into account new

knowledge, models, and theories. PL patterns might also have to be improved regarding

aspects such as data attributes, indicators and cross-references.

In summary:

1. It is necessary to bring-up-to-date and redefine Alexander’s “pattern language”,

having into account:

1.1 the advances in knowledge that occurred since the initial definition of the

“pattern language,” and

1.2 the evolution of the cultural context (communities do not live today as

before).

2. This redefinition can imply:


90

2.1 the elimination of patterns,

2.2 the inclusion of new patterns, and

2.3 the modification of patterns.

The entire redefinition of the pattern language, and the addition of new final patterns will be

the subject of future work, however, it is crucial to present a preliminary list of selected

patterns.

Urban patterns selected from Alexander’s PL patterns

This section is concerned with a tentative list of urban patterns that apply at the site planning

scale elaborated after Alexander’s list. In this a list, patterns that do not apply at the site

planning are out of the list or are written in red. Patterns that are usually applied at a larger

scale but occasionally also at the site planning scale are written green. Patterns that could be

applied at the site planning scale but were excluded are written in blue. The list is the

following:

TOWNS SCALE

10. MAGIC OF THE CITY

11. LOCAL TRANSPORT AREAS

12. COMMUNITY OF 7000

13. SUBCULTURE BOUNDARY

14. IDENTIFIABLE NEIGHBORHOOD

15. NEIGHBORHOOD BOUNDARY

16. WEB OF PUBLIC TRANSPORTATION

17. RING ROADS

18. NETWORK OF LEARNING

19. WEB OF SHOPPING

20. MINI-BUSES

21. FOUR-STORY LIMIT

22. NINE PER CENT PARKING

23. PARALLEL ROADS

24. SACRED SITES

25. ACCESS TO WATER

26. LIFE CYCLE

27. MEN AND WOMEN

28. ECCENTRIC NUCLEUS

29. DENSITY RINGS

30. ACTIVITY NODES

31. PROMENADE

32. SHOPPING STREET

33. NIGHT LIFE

34. INTERCHANGE

35. HOUSEHOLD MIX

36. DEGREES OF PUBLICNESS

37. HOUSE CLUSTER

38. ROW HOUSES

39. HOUSING HILL

40. OLD PEOPLE EVERYWHERE

41. WORK COMMUNITY

42. INDUSTRIAL RIBBON

43. UNIVERSITY AS A MARKETPLACE

44. LOCAL TOWN HALL

45. NECKLACE OF COMMUNITY PROJECTS

46. MARKET OF MANY SHOPS

47. HEALTH CENTER

48. HOUSING IN BETWEEN


91

49. LOOPED LOCAL ROADS

50. T JUNCTIONS

51. GREEN STREETS

52. NETWORK OF PATHS AND CARS

53. MAIN GATEWAYS

54. ROAD CROSSING

55. RAISED WALK

56. BIKE PATHS AND RACKS

57. CHILDREN IN THE CITY

58. CARNIVAL

59. QUIET BACKS

60. ACCESSIBLE GREEN

61. SMALL PUBLIC SQUARES

62. HIGH PLACES

63. DANCING IN THE STREET

64. POOLS AND STREAMS

65. BIRTH PLACES

66. HOLY GROUND

67. COMMON LAND

68. CONNECTED PLAY

69. PUBLIC OUTDOOR ROOM

70. GRAVE SITES

71. STILL WATER

72. LOCAL SPORTS

73. ADVENTURE PLAYGROUND

74. ANIMALS

75. THE FAMILY

76. HOUSE FOR A SMALL FAMILY

77. HOUSE FOR A COUPLE

78. HOUSE FOR ONE PERSON

79. YOUR OWN HOME

80. SELF-GOVERNING WORKSHOPS AND

OFFICES

81. SMALL SERVICES WITHOUT RED TAPE

82. OFFICE CONNECTIONS

83. MASTER AND APPRENTICES

84. TEENAGE SOCIETY

85. SHOPFRONT SCHOOLS

86. CHILDREN'S HOME

87. INDIVIDUALLY OWNED SHOPS

88. STREET CAFE

89. CORNER GROCERY

90. BEER HALL

91. TRAVELER'S INN

92. BUS STOP

93. FOOD STANDS

94. SLEEPING IN PUBLIC

BUILDINGS SCALE

95. BUILDING COMPLEX

96. NUMBER OF STORIES

97. SHIELDED PARKING

98. CIRCULATION REALMS

99. MAIN BUILDING

100. PEDESTRIAN STREET

101. BUILDING THOROUGHFARE

102. FAMILY OF ENTRANCES

103. SMALL PARKING LOTS

104. SITE REPAIR

105. SOUTH FACING OUTDOORS

Embedded concepts of patterns

Patterns are different because its concepts and descriptions depend on the point of view of its

developers. So to surpass this univocal picture it is important to look at related studies, first to

become aware of different visions, and then to ease the selection of the formulation language

structure.


92

Alexander (1977) and Pedro (2001b) are good examples of such dissimilarity. They

present two different visions regarding the development of patterns. Alexander has created a

sort of language for design - the Pattern Language book87 is ample on semantics. The work of

Pedro is largely framed by the universe of the quality standards - Pedro’s patterns deals

essentially with quality attributes.

While Alexander lacks main design actions as the creation of urban grids, focusing

extensively on political ideals, Pedro seems to lack flexibility and narrative88. Alexander

supports its vision upon a liberal philosophy. When defining patterns such as “Dancing on the

Streets” (PL 63) or “Sleeping on Public” (PL 94) it proposes ethics - often in opposition with

traditional conventions regarding the use of space. PL embraces thus a creative vision by

setting up desirable environments - however it seems too vague and too politically framed.

Pedro supports its vision on a more prescriptive philosophy focusing quality demands for a site

and a population. When defining a type of spatial quality he defines it following a constrained

path to guarantee accuracy according to predefined parameters.

A contemporary PL

In the update of the pattern language it must be taken into consideration these two different

visions concerning the formulation process. In the one hand, it is important to increase the

accuracy of the language, as Pedro did, and on the other hand, it is necessary to maintain

sufficient conceptual and visionary paths like Alexander.

A balance between such different conceptions can bring benefits to the formulation

model, because it amplifies the range of knowledge and manipulation.

87 . A Pattern Language: Towns, Buildings, Construction is a 1977 book on architecture. It was authored by Christopher Alexander , Sara Ishikawa and Murray Silverstein of the Center for Environmental Structure of the University of California at Berkeley, , with writing credits also to Max Jacobson, Ingrid Fiksdahl-King and Shlomo Angel. Twenty five years after its publication, it is still one of the best-selling books on architecture. The book is a substantive, illustrated discussion of a pattern language derived from traditional architecture, with 253 unitary patterns such as Main Gateways given a treatment over several pages. It is written as a set of rules that are invoked by circumstances. This is a form that a theoretical mathematician or computer scientist might call a generative grammar. The work originated from an observation that many medieval cities are attractive and harmonious. The authors said that this occurs because they were built to conform with local regulations that required specific features, but freed the architect to adapt them to particular situations. The book provides rules and pictures, and leaves decisions to be taken from the precise environment of the project. It describes exact methods for constructing practical, safe, and attractive designs at every scale, from entire regions, through cities, neighborhoods, gardens, buildings, rooms, built-in furniture, and fixtures down to the level of doorknobs. 88 . A narrative is a story that is created in a constructive format (as a work of writing, speech, poetry, prose, pictures, song, motion pictures, video games, theatre or dance) that describes a sequence of fictional or non-fictional events. It derives from the Latin verb narrare, which means "to recount" and is related to the adjective gnarus, meaning "knowing" or "skilled". (Ultimately derived from the Proto-Indo-European root gnō-, "to know"). The word "story" may be used as a synonym of "narrative", but can also be used to refer to the sequence of events described in a narrative. A narrative can also be told by a character within a larger narrative. An important part of narration is the narrative mode, the set of methods used to communicate the narrative through a process called narration.

http://en.wikipedia.org/wiki/Film

http://en.wikipedia.org/wiki/Theatre

http://en.wikipedia.org/wiki/Dance

http://en.wikipedia.org/wiki/Fictional

http://en.wikipedia.org/wiki/Non-fiction

http://en.wikipedia.org/wiki/Latin_language

http://en.wikipedia.org/wiki/Proto-Indo-European_language

http://en.wikipedia.org/wiki/Sequence

http://en.wikipedia.org/wiki/Character_%28arts%29

http://en.wikipedia.org/wiki/Narrative_mode


93

6.7. Urban Pattern Language Sketch (UPL)

Until now, the research was essentially focused on a variety of topics related with language

processing and planning methods. Now, it is time to build up the base (or the structure) of the

planner´s language. Facing the considerable variety of matters involving the description of

such a structure a question arises: Where to start?

First of all, it is important to mention that pre-design ontology (the core process of this

research) requires the sketch of an urban pattern language (UPL) to be used as a

contemporary flexible tool by planners. The axiom UPL is not an obsessive premise. It basically

drafts the name to distinguish it from other studies. So, herein, urban pattern language will be

written UPL in order to differentiate from Alexander’s PL (pattern language). Some overlapping

with PL is expected, though the word urban was chosen due to the exclusive field of

application, the word pattern because it concerns type and value within the urban

formulation, language due to the similarity with natural language’s structure.

How does UPL work in the proposed model?

The flow of information of UPL patterns acts at two different levels: one within the formulation

model and the other in association with the generation (design) and evaluation models. The

first level is simple to describe. Acquired data (PD1) acts as an input of the formulation model.

Such data is interpreted in order to be embedded into patterns to support and guide design

decisions. The second level consists of a data recursion process where data is in updating flow

(between formulation, generation and evaluation models) to compose more integrated

results. Evaluation is particularly important in such a process because it can act between

patterns and design correcting and updating the entire flow.

6.8. The UPL Ontology

The creation of a language’s ontology faces a set of difficulties. When one starts to handwrite a

diagram following the principles of “classic” ontology-building is faced with a set of

temptations (Gruber & others 1995). These concern mainly ambiguities prompted by the


94

manual manipulation of elements, groups, classes, and types, within its taxonomic description.

Such ambiguities drift from personal assumptions promoting a sense of vagueness in the

partitions of the model.

There is a critical problem between hand sketching and computational edition of

ontologies. The computational edition of ontologies allows the emergence of relational

structures, many of them without even having been previously imagined (Noy et al. 2000).

Basically, the software asks a set of questions to the agent (heuristically) asking for new

descriptions (domain, classes, and semantic attributes) (Sachs et al. 2006). Then the ontology-

building is led by a sequence of emerging semantic relations, fill-in the empty spaces of the

model.

The mission of congregating all semantic relational data into the ontology is massive.

One can initiate the process of organizing ingredients by a) describing a hierarchic tree of

elements that compose of urban space (a taxonomy), then by b) describing a list of semantics

that qualifies the urban elements (such as social, economic, and environmental features

among others), and finally c) describing a list of attributes for the specified elements.

6.9. The UPL Syntax and Core Components

Developing an ontology is usually an iterative process. As aforementioned, one can start with a

rough take at the ontology, and then revise and refine the evolving ontology by filling in the

details. As mentioned in the methodology chapter, developing an ontology includes the

following tasks:

1) definition of classes,

2) organization of the classes in a subclass-superclass hierarchy,

3) definition of slots by describing allowed values for such slots, and

4) the fill in the values for instances slots.

Furthermore an ontology allows one to act on two complementary levels of description: a top

level ontology on which are located the concepts and the relations of the model at a macro

scale; and an application ontology which specifies and details the concepts, thereby describing

the nature of its particular interactions.


95

The initial task concerns the description of the core features of the UPL top level

ontology. Such a selection requires a discovery of the crucial components of the urban

planning process, that is:

1) The nature of urban space (the field of its application),

2) The nature of design actions (the field of its proposals), and the interoperability of

these within,

3) A supporting computational system (the field of its administration).

These components are detailed as follows:

1) The nature of the urban space

Kevin Lynch (1960) wrote that users understood their surroundings in consistent and

predictable ways, by forming mental maps with defined elements: paths, streets, sidewalks,

trails, and other channels in which people travel (Networks); edges, perceived boundaries such

as walls, buildings (Blocks), and shorelines; districts, relatively large sections of the city

distinguished by some identity or character (Zones); nodes, focal points, intersections or loci;

and landmarks, readily identifiable objects which serve as reference points (Landmarks). Such

classes are hence defined by a) Networks, b) Zones, c) Blocks, and d) Landmarks.

2) The nature of the design actions

The urban design guidelines books surveyed for the current study recurrently presented similar

descriptions (as in the ByDesign CABE, the Urban Design Compendium, or in the Green

Dimensions books) (Gann et al. 2003)(Evans et al. 2007) (C. Moughtin & Shirley 2005). The core

element that occasionally appears separated from the Lynchian outline is the Landscape

element, somewhat denoting a tendency of planners to lead their actions based on this

additional feature. The elements can be thus described as a) Networks, b) Zones, c) Blocks, d)

Landmarks, and e) Landscape.

3) The supporting computational system

A GIS (Geographic Information Systems) software platform will support the operability of the

full model due to its resourceful spatial descriptors. Its representation standards encompass a)

Points, b) Lines, and c) Polygons.

The correlations seems to be clear: Landmarks can be represented by Points, Networks

by Lines, and Blocks and Zones by Polygons. In summary, Lynch’s appraisal matches GIS core


96

description while the element Landscape (a design action component) seems to promote

fuzziness with the element Zones.

Therefore the Top Classes will be herein defined as:

a) Networks (Lines),

b) Zones (Polygons I),

c) Blocks (Polygons II), and

d) Focal points or Landmarks (Points).

However such a fuzziness limits the process understanding. To produce the ontology is thus

necessary to congregate three concepts:

1) the nature of urban space,

2) the nature of design actions, and

3) the interoperability of these with a supporting computational system.

The descriptions of patterns that will inform designs will follow the scheme: a) Networks

(Lines), b) Zones (Polygons I), c) Blocks (Polygons II), and d) Focal points (Points).

Now, describing the top ontological classes:

Networks

Networks appear here without having a hierarchical dominion over the other three core

components of the program (blocks, zones, and focal points or landmarks). It just sets one of

the possible four actions that planners can input into designs in a crucial phase of their work.

The Network component can be described by a framework of routes and spaces that connect

locally and more widely, and open spaces that are sequentially related to one another (Gann et

al. 2003). This component upholds a strong social impact once streets make a large part of

people’s experience of place. They are the main spaces where people interact, and they

combine their function as a place with a role as part of a movement network for vehicles and

pedestrians (Evans et al. 2007). There are various classes of Networks depending on form and

function. Networks involve mainly movement paths and infrastructure paths (Gann et al. 2003).

Other type of more abstract networks can be related with landscape networks or building

masses networks. Networks are divided into different class groups across different

taxonomies. According to Lynch (1960) there are three main metaphors which attempt to

explain city form through networks: the magical metaphor for the earliest ceremonial centres

of religious ritual to link the city to the cosmos and to the environment; the metaphor that

makes an analogy with a machine; and the metaphor that compares city form to an organism.


97

According to Moughtin and Shirley (2005) these three metaphors are related to five main

forms of urban grids: 1) hierarchy of boxes, each nesting another, 2) orthogonal geometrical

figure / grid-iron plan, 3) directional grid, 4) triangular grid, and 5) informal lacework of

paths. Marshall (2005) further describes the network system and details network movement

patterns by describing performance correlation between different morphologies.

Blocks

Blocks represent compositions of buildings or groups of buildings within an urban site. The

space in between the volumes of blocks generally implies movement grids. The mixture

between blocks and grids defines the main structure of urban settlements. This component of

the urban program involves street blocks arrangements with its plots and buildings in

settlements (buildings < plots < blocks) (Gann et al. 2003). Buildings can range from housing,

office areas, recreation, leisure, and sports to crèches, education, health, and training to

community workspaces. Buildings within blocks also provide a secure base for community

organisations to establish a presence by developing partnerships between locals and other

stakeholders. Blocks also configure the best local resources to generate income (Evans et al.

2007). There are few studies committed to the definition of block morphologies and its classes

within city descriptors. The existing ones seem to be vague or too classic. Once again,

Alexander’s “pattern language” (1977) lacks this core category of “city objects”. When

Alexander refers to blocks he avoids a physical or geometric recognition or description of it,

setting a group of social semantics upon a vague notion of neighbourhood. Post-modern

research (Krier & Porphyrios 1984) correlates blocks with types of classic archetypal

morphologies while building masses tend today to be more abstract or topologic, sometimes

inspired in natural forms, or even developed under conceptual art or technology. The range of

block designs is today quite wide and open. Mitchell (1993) talks about an absence of a

theoretical critic able to explain the origin of shapes. However the starting point to define

block in a somewhat formalized way can be found in Pedro (2001b), where blocks descriptions

comprise a set of flexible archetypal forms that can be parameterized.

Zones

The component Zones comprise areas within perimeters defining types of environments within

sites. Zones are here defined within boundaries containing groups of meanings involving

matters such as the range of services and facilities, including commercial, educational, health,

spiritual and civic services (Evans et al. 2007). Design under this component is similar to


98

“planning through portraits” (Lynch 1960) where concepts as neighbourhood and mixture

represent a design core. Zones also require caution in the design process to avoid any form of

segregation. It is therefore essential to promote diversity in terms of: a) development forms; b)

land use; c) density; d) tenure; and e) market segments. Zones are usually carved into

development parcels around a road system with a clear urban design structure in place (DETR

2000). This approach frequently involves routing the main road around the site rather than

across it and locating the traffic generating uses such as retail and employment areas close to

entrance junctions and along the main road. “The road is used as a boundary to segregate

uses. Such attempts to create a sense of place around a focal point often fail because the very

uses that generate activity are on the edge of the site or beyond, in a nearby business park or

out-of-town centre, and tend to be internalised in “big boxes”(Evans et al. 2007).

Focal Points or Landmarks

The development of a plan can start with the identification or definition of focal points and/or

landmarks in space (pre-existing or new). These marks act as structural nodes from which

planners can define networks and masses of the plan. The relevance of landmarks and focal

points is simple to portray (Gann et al. 2003); “People find it easier to orient themselves and

recognise where they are when new development safeguards important views between places

or creates new ones, whilst respecting or adding new local landmarks. To ensure that a

particular place is legible, assess the relationship between existing elements and, in consulting

local people, determine how proposals contribute to a linked series of spaces and markers that

make it easy to get from A to B and to C” (Evans et al. 2007).

The following diagram (31) shows the main classes of the pre-design phase. The core

ontology (Blocks, Focal Points, Networks, and Zones) is located in the purple boxes of the

diagram entities. Its shared super-class is Design Core (light purple and dark green boxes).

Diagram 31 is also presented in Annex 5 at a different scale.


99

31. Syntax class hierarchical tree (above left), exported piece of XML Schema (above centre), CPL diagram

classes, subclass-superclass hierarchy, and slots (middle right), CPL diagram zoom - the design core -

Networks, Zones, Blocks, and Landmarks (at the bottom).

note: the CPL was built in the Protégé-Frames editor, in accordance with the Open Knowledge Base

Connectivity protocol (OKBC).


100

6.10. The UPL lexicon

A considerable part of the information related with urban space comprises geographic data,

that is, information that describes territorial features. Such information type is currently

shared by some communities and managed by central governments or controlled by other

administrative organizations. These specific databases are internationally known as geographic

information systems (GIS). The main purpose of such systems is the assemblage of relevant

geographic data to produce comprehensive descriptions of spatial configurations. One of the

advantages of GIS is related with its system of coordinates known as geo-reference, which

means that geographic information is related to a precise location on the planet, within a

continent, a region, or a site, and can be correlated with other geo-referenced information.

Due to strong investments by state and private corporations with interests on property and

territorial management, GIS software constitutes a well developed platform today. GIS thus

represents an important resource to be used in the formulation model to quickly retrieve and

store geographic data in the process of developing urban proposals. A GIS database can be

used to feed the formulation model with data about the context. It also can be used to store

data about designs produced by the generation module. Compatibility amongst both

contextual and design data requires the definition of a common ontology. Such ontology will

allow an easy flow of information within the formulation model and between this and the

other two models of the planning process - the generation model and the evaluation model. At

present there is an absence of an urban planning ontology covering the core matters of the

urban planning process. One way of surpassing the difficulty of defining a totally new ontology

is to use partial existing ones adapted to the needs of the formulation model. This creates the

possibility of using ontological descriptions within existing GIS databases as the Spatial Data

Transfer Standard (SDTS)89 approved by the US Federal Information Processing Standard (FIPS)

89 . Purpose of SDTS -- The purpose of the SDTS is to promote and facilitate the transfer of digital spatial data between dissimilar computer systems, while preserving information meaning and minimizing the need for information external to the transfer. Implementation of SDTS is of significant interest to users and producers of digital spatial data because of the potential for increased access to and sharing of spatial data, the reduction of information loss in data exchange, the elimination of the duplication of data acquisition, and the increase in the quality and integrity of spatial data. SDTS is neutral, modular, growth-oriented, extensible, and flexible--all characteristics of an "open systems" standard. (http://data.geocomm.com/sdts/) accessed January 2009.


101

(Rugg 1995), or the OpenGIS City Geography Markup Language90 (Kolbe, Gröger et al. 2005).

Those GIS “standards” were built to support operations between different computational

systems within a data transfer philosophy based on geographic and cartographic information

as well as other relevant metadata. This relevance demands a closer look at both systems:

The SDTS – GIS

The SDTS has a concept model consisting of detailed specifications such as content, structure,

and format. The spatial data model structure is based on three types of object description: a)

The “Spatial entities model”, which describes real spatial entities such as buildings, roads,

rivers, and so on, through rate attributes, b) the “Spatial objects model”, which describes

spatial objects such as lines, polygons and dots, used to represent real entities in digital

systems and, c) the “Spatial descriptions model”, which describes entities related to the real

world, objects related to the digital world, as well as spatial descriptions and connections that

exist between them. To guarantee data homogeneity and compatibility between data transfer,

SDTS describes a formal list of entities. The actual list includes more than 200 entity types, 244

attributes and more than 1200 alternative terms. The standard definition of entities includes

the following data structure: a) “Entity type”, a definition of a set of similar entities, b) “Entity

instance”, an example of a specific formalization within a type; “Attribute Entity”, a feature

that describes a type, c) “Attribute value”, a specific quality of an attribute, d) “Standard

expression”, a stereotyped name for an entity or an attribute, and e) “Integrated expression”,

a synonym used to refer to an entity or an attribute, defined by SDTS rules (Rugg 1995).

The OpenGIS – CityGML

The OpenGIS City Geography Markup Language (Kolbe, Gröger et al. 2005) has a similar

functional structure, with entities and classes definition that can be applied to the current

study model. One of the relevant aspects of GIS within geographic data gathering is the focus

on the creation of spatial ontologies (GIS-O). GIS spatial ontologies comprise today one of the

most advanced fields of ontology research and implementation. GIS software applications are

90 . . OpenGIS® Encoding Standard is for representation, storage, and exchange of virtual 3D city and landscape models. CityGML is implemented as an application schema of the Geography Markup Language version 3.1.1 (GML3). CityGML models both complex and georeferenced 3D vector data along with the semantics associated with the data. In contrast to other 3D vector formats, CityGML is based on a rich, general purpose information model in addition to geometry and appearance information. For specific domain areas, CityGML also provides an extension mechanism to enrich the data with identifiable features under preservation of semantic interoperability.


102

also widely implemented facilitating the access to classified data managed by institutions

ranging from governmental institutes to private corporations as mentioned above. However,

one of the recurrent problems involving GIS databases is the data sharing knowledge. This

difficulty occurs particularly in Europe where GIS is largely administrated according to an

individual management philosophy. In the U.S., GIS databases have a large public shared

character although this seems to lead to lower quality. In this region GIS databases are

financed and promoted by governments.

The UPL lexicon could correspond to ontological sub-classes mainly taken from the

CityGML GIS standards, which consists in vast class definitions for the most important types of

objects within 3D city models. Its basic definitions representing the spatial objects and their

aggregations are defined by ISO 19109 and GML3 standards, and it comprises different types

of interrelationships between Feature Classes like aggregations, generalizations and

associations. An important outcome of such descriptions is a high degree of semantic

interoperability between different applications along their UML mapping, defining feature

types, attributes, and data types with a standardised meaning or interpretation. Towards an

ontological integration of descriptions the data is encoded by UML - Unified Modeling

Language within its static structure diagram. The structure is simple. The base class of all

thematic classes is CityObject. “CityObject is a subclass of the GML class Feature, thus it

inherits its metadata (e.g. information about the lineage, quality aspects, and accuracy). The

subclasses of CityObject comprise the different thematic fields of a city model: the terrain, the

coverage by land use objects, transportation, vegetation, water bodies and sites, in particular

buildings” (Kolbe, Gröger et al. 2005). CityFurniture is another GML class, and is used to

represent traffic lights, traffic signs, flower buckets, or similar objects. The features that are

not covered explicitly by them are modelled by the class GenericCityObject. However CityGML

schema still lacks some descriptions of subclasses like tunnel, bridge, excavation, wall or

embankment. At present, these objects have to be represented by GenericObjects, or will be

defined herein by new classes.

There is a normative to apply to Feature attributes: unless it is stated otherwise each

feature has attributes classes, function, and usage. The class attribute can occur only once,

while the attributes usage and function can be repeated within the ontology. The class

attribute describes the classification of objects, e.g. road, track, railway, or square. For the

purpose of an object, like national highway or county road, it is used the attribute function,


103

while the attribute usage may define if an object is e.g. navigable or usable for pedestrians.

The most relevant Top level classes are: Land Use, Building Model, City Furniture,

Transportation Object, Vegetation Object, and Water Bodies (Kolbe, Gröger et al. 2005).

The following diagram presents the top level class hierarchy of the lexicon.

32. UML diagram of the top level class hierarchy (CityGML)

The figure below shows some of the CityGML object listed codes, that will be used in the

edition of the language ontology, and which composes part of the vocabulary used in the

above UML diagrams.

6. An example of a CityGML code list for city objects


104

6.11. The UPL Semantics

UPL semantics are key attributes of the formulation model enclosing performance91. These

attributes bring up recursive data to fill in slots such as meaning and substance within the

ontology. Such data is therefore crucial to compose the ontology of an urban program. The

field of its application is mainly focused on:

a) Events upholding the human quotidian life (social/safety patterns),

b) Features as energy and environment control (bioclimatic patterns), and

c) Features with a value sight (technology and economy patterns).

Where is located this semantic field in the ontology?

The Alexander’s patterns are part of this semantic field and represent the core instrument of the

ontology-building. In fact, the patterns that are here portrayed represent an addition to the Alexander

patterns. These are the patterns that will be conjugated at the final pattern language redesign. The final

organization of all these components of the planning language will be concluded in future research.

Now, a closer look at the item a) events upholding the human quotidian life (social/safety

patterns).

a) Social core. CPL semantics are guided by the social nature of a site as described by the

following:

1. Character – which is a place with its own identity. The idea is to “promote character

in townscape and landscape by responding to and reinforcing locally distinctive

patterns of development, landscape and culture”,

91 . . A performance indicator or key performance indicator (KPI) is a measure of performance. Such measures are commonly used to help an organization define and evaluate how successful it is, typically in terms of making progress towards its long-term organizational goals. KPIs can be specified by answering the question, "What is really important to different stakeholders?". KPIs may be monitored using Business Intelligence techniques to assess the present state of the business and to assist in prescrib ing a course of action. The act of monitoring KPIs in real-time is known as business activity monitoring (BAM). KPIs are frequently used to "value" difficult to measure activities such as the benefits of leadership development, engagement, service, and satisfact ion. KPIs are typically tied to an organization's strategy using concepts or techniques such as the Balanced Scorecard. The KPIs differ depending on the nature of the organization and the organization's strategy. They help to evaluate the progress of an organization towards its vision and long-term goals, especially toward difficult to quantify knowledge-based goals. A KPI is a key part of a measurable objective, which is made up of a direction, KPI, benchmark, target, and time frame.


105

2. Continuity and enclosure – which is a place where private and public spaces are

distinguished. Here the concept is to promote the continuity of street frontages and

the enclosure of space by development which defines private and public areas,

3. Quality of the public realm – which is a place with attractive and successful outdoor

areas. The idea is to promote public spaces and routes that are attractive, that are

safe, and work efficiently for all in society, including disabled and elderly people,

4. Ease of movement – which is a place that is easy to get to and move through. The

idea is to promote accessibility by making places that are easy to move through and

connect with each other, putting people before traffic and incorporating land uses

and transport,

5. Legibility – which is a place that is easy to understand. The concept is to promote

legibility through development that provides recognisable routes, intersections and

landmarks to help people find their way around,

6. Adaptability – which is a place that can change easily. The idea is to promote

adaptability through development that can respond to changing social,

technological and economic conditions, and g) diversity – which is a place with

variety and choice” (Gann et al. 2003).

The social quality needs to be evaluated through requirements such as security, spatial and

functional potential, personalization and economy; which are applicable within the space

relations of a particular site (Pereira 1996). Those are held to reinforce links between

populations within their surrounding environment. Moreover further studies deepen the

notion of social space comprising new urban research outcomes. This seems to be the case of

the “The City Joust” of Guterres (2004) that consists of an innovative social study regarding

urban planning. In “The City Joust”, social metrics are enclosed in the “experience of the city”,

in a phenomenon that gauges the universe of the urban space relations, its intrinsic empathies

and the social behaviours. The study presents an extensive study on social space measuring

consequences implicit on public and private areas (as well as hybrid). It describes the impacts

on the quality of a population’s everyday life. The concepts are supported by different theories

and indicators, namely from Maslow (1954), Jacobs (1961), Hall (1973), Newman (1972), Hillier

& Hanson (1984), and Coleman (1990).


106

Those social patterns were encoded into new patterns, and transform into indicators as

shown in the table 7.

Some of the created patterns are described as follows:

1. Empathy Distance (ea),

2. Public Green (pg),

3. Total Area With Sense of Sociability (tass),

4. Private Areas With or Without Social Interaction (pawwsi),

5. Public Area With Everyday Existence (pgee),

6. Sidewalks With Social Interaction (ssi),

7. Private Areas, or with Diaphragms, with Social Monitoring (padm),

8. Pedestrian Areas (pa) and,

9. Private Areas without Social Interaction (pawsi).

codes calculations site values indicators

ea p1 (%) 12,0% 0 > desirable

ea (m2/hab) 79,56 0 > desirable

pgee p3 (%) 0,0% > 3,5%

pgee (m2/hab) 0,00

tass p4 (%) 18,0% > 25%

tass (m2/hab)

119,62

ssi p7 (%) 13,0% > 20%

ssi (m2/hab)

86,03 > 10

pg p8 (%) 1,1% 10%

pg (m2/hab) 7,11 5

pawsi p10 (%) 0,0% < 6%

pawsi (m2/hab)

0,00 < 3

7. The above table presents some indicators of Guterres social patterns (2004).


107

The provisory codes and names for this sample are described by:

GP ea1

GP pg2,

GP tass3,

GP pawwsi4,

GP pgee5,

GP ssi6,

GP padm7,

GP pa8,

GP pawsi9.

b) Sustainability core (environmental domain). UPL is also informed by sustainability factors.

The definition of patterns encompassing such factors follows a basic set of concepts. In

summary they require urban space to be:

a) active, inclusive, and safe – which means that urban settlements needs to be fair,

tolerant and cohesive with a strong local culture and other shared community activities;

b) well run – this is related with an effective and inclusive participation, representation,

and also leadership;

c) environmentally sensitive – because is important to provide places for people to live

that are considerate of the environment;

d) well designed and built – which concerns the quality of the built and the natural

environment;

e) well connected – with good transport services and communication linking people to

jobs, schools, health and other services;

f) thriving – with a flourishing and diverse local economy;

g) well served – with public, private, community and voluntary services that are

appropriate to people’s needs and accessible to all; and

h) fair for everyone – because is crucial to include minorities in communities, now and in

the future (Evans et al. 2007).

Further research will give depth to concepts and indicators defining the corpus of sustainability

patterns. Despite such a framework some examples of sustainable patterns are presented as

follows.


108

b1) Bioclimatic. These patterns consist of a group of instruments inspired in the “Bioclimatic

Urbanism” domain (Higueras 1997). These studies are mainly focus on climatic features and

urban environmental conditions. The main outcome is a set of design guidelines for green and

open areas, buildings, volume orientations, etc., supported on energy and environmental

sustainability criteria.

Olgyay (1963)charts both architecture and climate attributing different urban forms for

buildings in the four main world regions, establishing for each region design strategies that

serve as guidelines to architects as much as to city planners. In this context building forms are

shaped by temperature, sun exposure and humidity.

8. Form and proportions of buildings in different regions (Olgyay 1963)

The formal descriptions of the climatic patterns (Higueras, 1997) traduce a set of standards

towards the definition of form, setting itself as an essential support to develop climate-based

formal features. The following example of a cold region reveals the relevance of climate for the

definition of design solutions. In fact, each description of a climatic recommendation seems to

enclose a relevant pattern for design. As an example I describe below patterns for the Cold

Region (Higueras, 1997).

General considerations

1. Selection of a location. For sun exposure, slopes S and SE are most favourable. Locations on a

mid slope are beneficial to prevent excessive effect of winds and avoid cold air. 2. Urban

structure. Planning management has to provide protection against winds. Sets of constructions

of a larger scale can be grouped, although maintaining free space between them to take

advantage of the solar effect. Houses tend to be united to decrease the exposed surface as much

as possible and avoid heat loss. 3. Public Spaces. These should be protected from the wind,


109

open, and have with periodic shaded areas. 4. Landscape. Topography, generally rough,

influences the definition of the forms of streets and the use of the space, granting it an irregular

character. 5. Vegetation. The most favourable vegetal protecting barriers are those constituted

by perennial vegetation, oriented according to the NE direction, and located within a distance of

twenty times the height of its hoist. Near the houses should stand deciduous trees. Is should be

avoided to locate vegetation near houses because it can produce humidity.

The design of the house

1. House Type. In residential arrangements, houses of one or two levels must favour

compactness. Terraced houses offer the advantage of loosing less heat. In larger apartment

buildings the compact volume is the better choice. 2. General distribution. Heating saving is

three times more important than the provision of comfort in summer. Extreme conditions in

summer and in winter suggest the creation of two separated zones that play the double roles in

building. The location of steps in the outside and the presence of ramps for cars must be avoided.

3. Distribution Plan. Design will be governed by the predominant conditions in cold months. The

period of “stay inside the house” represents 70% of annual hours. Although the plan will have to

satisfy both conditions through compactness, it is essential to include additional zones of activity

or use in deeper spaces for summer comfort. 4. Form and volume. The constructions must be

compact and display a minimum exposed outer surface. A proportion of 1:1,1 or 1:1,3, along the

East-West axis will give the finest results. 5. Orientation. The most favourable solar orientation is

located to 12° SE. The predominant wind pattern NW-SE can influence the location of isolated

buildings. 6. Colour. Surfaces exposed to the sun must have average tonalities. Surfaces can be

made of absorbent dark colours, assuring that they will always be in the shade during the

summer.”

These guidelines will be patterns or design rules only if they will be applied to generate

patterns, that is, recurrent solutions for designs. The deference for these guidelines will

generate descriptive patterns of properties of urban objects.

In summary, the set of cold region patterns can be used to reinforce UPL semantics by

complementing Alexander’s patterns. The provisory codes and names for these patterns are:

HP cr1. Selection of the location,

HP cr2. Urban structure,

HP cr3. Public Spaces,

HP cr4. Landscape,

HP cr5. Vegetation,

HP cr10. House Type,


110

HP cr20. General distribution,

HP cr30. Distribution Plan,

HP cr40. Form and volume,

HP cr50. Orientation, and

HP cr60. Colour.

All these patterns will be tentatively added to the previously selected Alexander’s patterns,

following the mentioned criteria. The list of Alexander’s patterns complemented with the

added patterns is, therefore, organized as follows;

AP lta11. LOCAL TRANSPORT AREAS

AP cm12. COMMUNITY OF 7000

AP in14. IDENTIFIABLE NEIGHBORHOOD

AP nb15. NEIGHBORHOOD BOUNDARY

AP wpt16. WEB OF PUBLIC

TRANSPORTATION

AP rr17. RING ROADS

AP fls21. FOUR-STORY LIMIT

AP pr23. PARALLEL ROADS

AP ss24. SACRED SITES

AP aw25. ACCESS TO WATER

AP lc26. LIFE CYCLE

AP en28. ECCENTRIC NUCLEUS

AP dr29. DENSITY RINGS

AP an30. ACTIVITY NODES

AP p31. PROMENADE

AP ss32. SHOPPING STREET

AP nl33. NIGHT LIFE

APhm35. HOUSEHOLD MIX

AP dp36. DEGREES OF PUBLICNESS

AP hc37. HOUSE CLUSTER

AP rh38. ROW HOUSES

AP ope40. OLD PEOPLE EVERYWHERE

AP wc41. WORK COMMUNITY

AP nc45. NECKLACE OF COMMUNITY

PROJECTS

AP mms46. MARKET OF MANY SHOPS

AP hb48. HOUSING IN BETWEEN

AP llr49. LOOPED LOCAL ROADS

AP tj50. T JUNCTIONS

AP gs51. GREEN STREETS

AP npc52. NETWORK OF PATHS AND

CARS

AP mg53. MAIN GATEWAYS

AP rc54. ROAD CROSSING

AP bpr56. BIKE PATHS AND RACKS

AP cc57. CHILDREN IN THE CITY

AP qb59. QUIET BACKS

AP ag60. ACCESSIBLE GREEN

AP sps61. SMALL PUBLIC SQUARES

AP ps64. POOLS AND STREAMS

AP hg66. HOLY GROUND

AP por69. PUBLIC OUTDOOR ROOM

AP sw71. STILL WATER

AP ls72. LOCAL SPORTS

AP ap73. ADVENTURE PLAYGROUND

AP sgwo80. SELF-GOVERNING WORKSHOPS AND

OFFICES

AP sswrt81. SMALL SERVICES WITHOUT RED

TAPE

AP oc82. OFFICE CONNECTIONS

AP ts84. TEENAGE SOCIETY

AP ch86. CHILDREN'S HOME

AP iws87. INDIVIDUALLY OWNED SHOPS


111

AP sc88. STREET CAFE

AP bh90. BEER HALL

AP ti91. TRAVELER'S INN

AP bs92. BUS STOP

AP fs93. FOOD STANDS

AP bc95. BUILDING COMPLEX

AP ns96. NUMBER OF STORIES

AP cr98. CIRCULATION REALMS

AP mb99. MAIN BUILDING

AP ps100. PEDESTRIAN STREET

AP bs101. BUILDING THOROUGHFARE

AP fe102. FAMILY OF ENTRANCES

AP spl103. SMALL PARKING LOTS

AP sr104. SITE REPAIR

AP sfo105. SOUTH FACING OUTDOORS

And more occasionally by:

AP i34. INTERCHANGE

AP lth44. LOCAL TOWN HALL

AP hc47. HEALTH CENTER

AP bp65. BIRTH PLACES

AP gs70. GRAVE SITES

All patterns are described by its codes as shown in the following list. The code is defined by:

two capital letters, the first identifying the pattern’s author and the second confirming it as

pattern (P) (example: AP – Alexander pattern), then, two or three small letters representing

the particular axiom of the pattern (example: Birth Places – bp), finally, the relative number of

each pattern. The following list congregates all provisional patterns discussed in this

document.

AP lta11

AP cm12

AP in14

AP nb15

AP wpt16

AP rr17

AP fls21

AP pr23

AP ss24

AP aw25

AP lc26

AP en28

AP dr29

AP an30

AP p31

AP ss32

AP nl33

APhm35

AP dp36

AP hc37

AP rh38

AP ope40

AP wc41

AP nc45

AP mms46

AP hb48

AP llr49

AP tj50

AP gs51

AP npc52

AP mg53

AP rc54

AP bpr56

AP cc57

AP qb59

AP ag60

AP sps61

AP ps64

AP hg66

AP por69

AP sw71

AP ls72

AP ap73

AP sgwo80

AP sswrt81

AP oc82

AP ts84

AP ch86

AP iws87

AP sc88

AP bh90

AP ti91

AP bs92

AP fs93

AP bc95

AP ns96

AP cr98

AP mb99

AP ps100

AP bs101

AP fe102

AP spl103

AP sr104

AP sfo105

AP i34

AP lth44

AP hc47

AP bp65

AP gs70

HP cr1

HP cr2

HP cr3

HP cr4

HP cr5

HP cr10


112

HP cr20

HP cr30

HP cr40

HP cr50

HP cr60

GP ea1

GP pg2

GP tass3

GP pawwsi4

GP pgee5

GP ssi6

GP padm7

GP pa8

GP pawsi9

6.12. Conclusion

Following Christopher Alexander’s conceptual pathway it is straightforward to elaborate a

relatively balanced and functional selection of patterns in order to provide for an efficient and

effective urban pattern language. However, problems emerge after such selection, more

precisely, when the task is to relate the selected patterns with each other (from so different

domains)92 to build-up the syntax of the language.

Up to this point, the focus was on organizing patterns according to scale. Alexander

classified defined patterns based on scale because in a way it corresponds to a model of

rational planning. As a model it seems as clear as it can be. However, city planners might select

thematic fields of interest when planning and designing urban space. In this context, organizing

patterns by scale seems to restrict some of the ideological facets of traditional planning

activity.

One of the future missions of our research is, therefore, to find an alternative taxonomy that

can first be based on thematic urban problems, and only then controlled by scale

considerations.

92 . Economic, climatic, social, regulatory, cultural, etc.


113


pattern language


(IV)

This chapter explores the most important category of the formulation model: the systemic language that is used by planners to formulate urban solutions, in short, the planner’s language.

In Diagram 33 is depicted the design core of the language.

33. The diagram shows the core structure of the CPL, first described in a hierarchical tree (above left), then

in an exported piece of XML Schema (above centre), and finally in a diagram (ontology core - Networks,

Zones, Blocks, and Landmarks (at the bottom)). See annex 5.

05

• planners have their own language to plan - one will call it urban pattern language (UPL)


114

Chapter 7

7. Conclusion


115

7.1. The Context

This thesis is part of a larger research which is concerned with the development and

implementation of a computer model for urban design. The ultimate goal of this model is the

development of a computer tool to assist in the conception and implementation of urban

design plans at the site planning scale. This larger model includes three partial models and

tools for formulating, generating, and evaluating such plans.

This thesis is concerned with the formulation model, which aims at creating the

programs of urban interventions from contextual data, including, regulations, site, and

population data. It represents one step towards the development of this model and the

corresponding tool. The thesis sketches an ontology and a pattern language for describing

urban space and composing urban programs. The tool will consist of an interactive computer

system that codifies a pattern language that can be used for describing urban solutions for

predefined contexts, according to the ontology. The computer implementation of the ontology

is the immediate following step in the research. Other future steps will be the inference of the

rules that link particular contextual features to specific patterns and the computer

implementation of the urban pattern language (UPL) defined in this way.

7.2. The Problem

One of the main concerns related with the development of the formulation model and tool is

the selection and definition of its core components and the efficiency of the embodied

methodology.

This first problem was solved to a certain extent in this thesis.

A knowledge model in any domain requires an initial listing of the base concepts of that

domain. The proposed ontology allowed one to indentify the main classes of such concepts, in

order to structure the taxonomy of urban space that is manipulated in the planning activity.

These classes are networks, blocks, zones, focal points, and landscape and they were compiled

based on several previous studies. The ontology allows one to map the relationships among

the entities and components of the model and to understand how they work.

The methodology embodied in the formulation model concerns the series of steps that

are necessary to follow in a certain order to arrive at the urban program departing from the


116

context. So far, research permitted to link such a methodology to a large data model formed

by key concepts, called patterns, and to the relations among them.

Still, the relation between ontological objects and urban patterns is not completely clear

yet. Are they the same type of entities? This is an important aspect to clarify in further

research. It is our hope that in fact, the exhausting description of these concepts will allow one

to understand the overall process as a vast relational database constituted of independent but

interrelated parts (patterns and sub-patterns). So, future research will be concerned with using

an ontology editor to describe the concepts and the relations among them exhaustively so as

to arrive at the rules for generating programs from the context.

7.3. The Goal

As said before, by planning space, one can prevent the waste of resources allowing, at

the same time, to maximize the satisfaction of population needs. Planning plays, therefore, a

key role in spatial and social organization (D. Harvey 2009). First, because it defines objectives

that clarify the mission of the territory, and second because it establishes levels of

effectiveness and efficiency by implementing measures to attain defined goals (Drucker 2007).

A computational platform, here expressed by an ontology, facilitates the creation and

the management of such a more complex planning instrument. With the ontology sketch was

thus possible to classify the core entities of the planning domain knowledge, as well as set up a

strategy to assess to its inconsistencies as well as reasoning the model.

By undertake this first step towards the ontology building it was possible to define the

strategy to implement the planning instrument in a more accurate, flexible and open process.

7.4. The Model

According to what was said above, it is important to guarantee that the theories on which the

proposed model is based are theoretically sound and meet to the desired goal. The academic

theories used as a basis for this study are threefold: are well-known.

The first one is Duarte’s model for the mass customization of housing (2001), upon

which the formulation conceptual model was developed. Its premises are focused on context,

interpretation, and formulation. The contribution of this model is to provide the technical


117

apparatus, which is based on Stiny’s description and shape grammars and permits to link

contextual descriptions to programmatic descriptions and then these to design outcomes.

The second theory is the notion of pattern language proposed by Christopher Alexander

(1977), which launched the bases for a planning language theory. Alexander’s premises depart

from linguistics theory, which defines language as a combinatorial and creative method of

communication in different contexts, with clear instructions, and within a defined vocabulary.

The aim for this language is to allow users to manipulate creatively its ingredients to develop

an appropriate speech that is a design adequate to a given context. In planning, such

characteristics are particularly useful for enabling a creative and flexible process.

The third theoretical framework consists in the use of a computational data model – a

computational ontology (Gruber & others 1995) – to describe the semantic relational data of a

given domain in a systematic way, as well as the relations among such entities and their

instances. This framework will be used to describe both urban space and the urban planning

process, thereby defining the formulation model.

7.5. The Outcome

This research has provided the elaboration of the skeleton of the pre-design framework. This

means that its components were developed and combined, and the methods to define its

structure had also been implemented.

The product of this research is therefore the development of the basic concepts of the

formulation model, its methodology, its components, and the way data is manage to harness

good results upon its future implementation.

Other relevant and complimentary outcomes are the development of an ontology to

support urban descriptions and the development of a tentative pattern language, adequate to

the Western cultural context, that revises and complements Alexander’s pattern language.

Together, the proposed ontology and pattern language will constitute the basis for future

research, concerned with the inference of rules to link contextual features to programmatic

descriptions.


118

7.6. Reflections for future research

A large part of this study is dedicated to the discovery of the most appropriate methodologies

and tools to intervene in urban space with the objective of producing better living conditions

for populations. However one cannot disregard that formulation in its primordial concept is

far ahead from the definition of methods and techniques. An important concern pointed out

in several chapters of this research is the necessity of taking into account places’ particularities

and citizen’s aspirations (inhabitants and visitors) as they are the users of cities. Emphasizing

this concern site interventions are increasingly more global and homogeneous both in terms of

program and design proposal. She refers that in addition to local and global marketing

strategies there is today an array of mandatory regulations and communitarian directives

concerning patrimonial values, accessibilities, security and public sanitation, among other

aspects, that are leading towards a gradual standardization of public spaces. One can also

argue that portions of cities are drawn as finished objects, places without a future anima, in a

sort of a perfect city scale model. In contrast, our research aims at urban planning strategies

that can incorporate the desires and expectations of citizens.

Quite often today, spaces are created where the urgency of consumption and the hyper-

programming constrain free choice. Urban programs will thus have to leave room for natural

processes of urban evolution to take place in order to avoid the excesses of prescriptive or

mandatory policies. They should be formulated in ways that define flexible rather than rigid

specifications to enable unexpected while creative transformations along time.

This goal constitutes a relevant focus of the developed model. Flexibility is considered,

since the beginning of this study, as a core feature of urban programs and design proposals

alike.

One fact seems to be clear. The ontological framework is essential to build the data

model. However crucial questions still require further clarification, namely: what quantity or

quality of information is necessary to embed in the model? Furthermore where does it come

from? In operational terms one can argue that the ontology editor will solve a large part of the

data structure. However, how does one detail the ontology93? Will one simply seat in front of

93. The development of the model.


119

the computer and develop the model’s concepts and relations based on personal experience?

If such happens, where will one search for the information that one does not possess yet?

To answer to this question is, in summary, to explain the methodology of future

research. In fact, it is possible to collect a great amount of relevant data through literature

reviews or by undertaking additional methods and experiences.

The brief conclusion is that the use of an ontology editor seems to be insufficient to

eliminate all the expectable inconsistencies. The model has, thus, to be built using additional

sources and methods.

7.7. The Opportunity

There is one concept concerned with the description of building components and associated

information that can provide a useful reference of the development of the urban formulation

model.

This concept is known as BIM.

In summary, Building Information Models (BIM)94 comprise a system that aims at incorporating

all aspects of design from geographic information, to building geometry, to component

relationships, and finally, to the quantities and properties of the building components. BIM

requires a purpose-built foundation to manage the amount of data generated. Such a

description closely corresponds to the Urban Pattern Language (UPL) framework. The idea is

depart from this correspondence to build a similar relational model, comprising a wide range

of data to describe urban space and its properties. Such a City Information Model (CIM),

94 . Building Information Modeling is the process of generating and managing building data during its life cycle. Typically it uses three-dimensional, real-time, dynamic building modeling software to increase productivity in building design and construction. The process produces the Building Information Model (also abbreviated BIM), which encompasses building geometry, spatial relationships, geographic information, and quantities and properties of building components. Building information modeling covers geometry, spatial relationships, light analysis, geographic information, quantities and properties of building components (for example manufacturers' details). BIM can be used to demonstrate the entire building life cycle, including the processes of construction and facility operation. Quantities and shared properties of materials can be extracted easily. Scopes of work can be isolated and defined. Systems, assemblies and sequences can be shown in a relative scale with the entire facility or group of facilities. BIM can be seen as a companion to PLM as in the Product Development domain, since it goes beyond geometry and addresses issues such as Cost Management, Project Management and provides a way to work concurrent on most aspects of building life cycle processes. BIM goes far beyond switching to a new software. It requires changes to the definition of traditional architectural phases and more data sharing than most architects and engineers are used to. BIM is able to achieve such improvements by modeling representations of the actual parts and pieces being used to build a building. This is a substantial shift from the traditional computer aided drafting method of drawing with vector file-based lines that combine to represent objects.


120

however, will have a wider semantic core and model hundreds of thousands of components

more, when compared to the BIM and so it will represent a significantly more complex

challenge. The goal of future research is to overcome such hurdles and develop such model.

Glossary


121

Introduction

Towards building an ontology is crucial the access to a common and shared lexicon (part of the

aim of the ontology theory). The description of technical terms related with this specific field

facilitates the engagement of such framework.

This glossary presents hence an urban planning data synopsis (urban management, design,

language, and methods) based on sorted written documents, namely; ESRI's Dictionary of GIS

Terminology (Kennedy 2001), (Gann et al. 2003), (Evans et al. 2007), (Moughtin et al. 2003), among

other references.

This glossary is intended to provide general assistance, not authoritative definitions of terms

which are sometimes controversial or used with different meanings in different contexts, in

order to facilitate an application of specific technical terms.

Terms

ACCESSIBILITY: The ability of people to move round an area and to reach places and facilities, including elderly and disabled people, those with young children and those encumbered with luggage or shopping.

ACCESSORY USE (a) (GIS): The use of a building, structure or land that is subordinate to, customarily incidental to, and ordinarily found in association with, a principal use, which it serves.

ACCESSORY USE (b): A building or a usage of land that is additional to primary use. A garage apartment or granny flat located behind the main house is an example of an accessory use.

ACTIVITY CENTER (a) (GIS): A community focal point providing for the combination, rather than scatteration, of general retail, service commercial, professional office, higher density housing, and appropriate public/quasi-public uses.

ACTIVITY CENTER (b): A central area within a neighborhood or at the intersection of several neighborhoods, that serves as a formal and/or informal gathering place. An activity center can be a commercial area with a variety of different types of retail establishments, often with public open space, a formal park, or any area that promotes interaction with other people on a personal and impersonal level and is pedestrian-oriented.

ACTIVE FRONTAGE: This refers to ground floors with windows and doors onto the street which create interest and activity. This normally means shopfronts but can include atriums and foyers.

ACTION PLANNING: Participation techniques, including community planning weekends and Urban Design Action Teams (UDATs), which enable local people and invited teams of professionals to explore design ideas for particular areas over one or several days.

ACTIVITY SPINE: Street or streets along which activity is concentrated.

Activity Node: Concentration of activity at a particular point.

ACRE (GIS): 43,560 square feet (about the size of a football field).

ADAPTABILITY: The capacity of a building or space to be changed so as to respond to changing social, technological and economic conditions.


122

AGRICULTURAL ASSESSMENT (GIS): A state program in which land used for agricultural purposes is assessed based on its value as agricultural land as opposed to a higher valuation.

AREA APPRAISAL: An assessment of an area’s land uses, built and natural environment, and social and physical characteristics.

AREA MASTER PLAN OR AREA PLAN (GIS): Area Master Plans: Area master plans consist of a plan map along with supporting data, text and other maps. They provide specific recommendations on a planning area or sub-region basis on the environment, historic preservation, living areas, housing, commercial areas, employment areas, urban design, circulation, and transportation.

ARCHITECTURE AND PLANNING CENTRE (UK): An institution which provides a focus for a range of activities and services (such as discussions, information, exhibitions, collaboration and professional services) relating to architecture and planning.

ARTERIAL (GIS): A highway, usually within a 120-foot right-of-way, for through traffic with access controlled to minimize direct connections, usually divided and on a continuous route.

AT-GRADE (GIS): Level for a road, building or other structure at the same grade or level as the adjoining property (as opposed to a depressed or elevated road, building or other facility).

ATRIUM: A circulation space, normally in the centre of an office building. This is often a high space with a glass roof that is the reception space for the building and the vertical circulation

AVERAGE DAILY TRAFFIC (ADT): The average number of vehicles passing a specified point on a highway during a 24-hour period.

BASE DISTRICT: A zoning district that establishes regulations governing land use and site development in a specific geographic area. Example: - A minimum lot size of 10,000 square feet, - A minimum lot width of 60 feet, - That the house covers no more than 35% of the lot, - That all of the improvements (the house, driveway, sidewalk, etc.) cover no more than 40% of the lot, - That the house be no taller than 35 feet, - That the house be at least 25 feet from the street front.

BASIC PLAN: Phase 1 of the Comprehensive Design Zone process. It sets forth general land use relationships,

including the approximate number of dwelling units and building intensity. Proposed land uses are also described.

BERM: An earthen mound designed to provide visual interest on a site, screening of undesirable views, noise reduction, etc.

BEST MANAGEMENT PRACTICES (BMPs): Conservation practices or systems of practices and management measures that control soil loss and reduce water quality degradation caused by nutrients, animal waste, toxins and sediment.

BIKEWAY: A lane, path or other surface reserved exclusively for bikers.

BRIEF: This guide refers to site-specific briefs as development briefs. Site-specific briefs are also called a variety of other names, including urban programs, planning briefs and development frameworks.

BUILDING LINE: The primary front face of buildings along a street. Where all of the buildings share a common building line (which can be curved) there is continuous enclosure along the street.

BUILDING ELEMENTS: Doors, windows, cornices and other features which contribute to the overall design of a building.

BUILDING ENVELOPE GUIDELINES: Diagram(s) with dimensions showing the possible site and massing of a building.

BUILDING EXPLORATORY: A centre for explaining, interpreting and providing information on the built environment.

BUILDING LINE: The line formed by the frontages of buildings along a street. The building line can be shown on a plan or section.


123

BUFFER (a): An area of land designed or managed for the purpose of separating and insulating two or more land areas whose uses conflict or are incompatible (trees separating homes from an expressway). BUFFER (b) OR BUFFER STRIP: Landscaped areas, open spaces, fences, walls, berms, or any combination of these, used to physically separate or screen one land use or piece of property from another. Buffers are often used to block light or noise.

BUFFERYARD: One of several specific combinations of minimum building setbacks, landscaped yard widths, and plant material requirements set forth in the Landscape Manual for use in buffering incompatible land uses.

BUILT ENVIRONMENT: The urban environment consisting of buildings, roads, fixtures, parks, and all other improvements that form the physical character of a city.

BULK: The combined effect of the arrangement, volume and shape of a building or group of buildings. Also called massing.

BUS RAPID TRANSIT (BRT) (GIS): A fixed guideway transit (FGT) system in which transit buses operate on rights-of-way that are physically or otherwise off-limits to regular vehicular traffic. These systems are often constructed so that they can be upgraded to light-rail vehicle operations when ridership grows beyond the operational capacity of transit buses.

CHARACTER: The image and perception of a community as defined by its built environment, landscaping, natural features and open space, types and style of housing, and number and size of roads and sidewalks.

CHARACTER ASSESSMENT: An area appraisal identifying distinguishing physical features and emphasising historical and cultural associations.

COMPATIBILITY STANDARDS: Development regulations established to minimize the effects of commercial, industrial, or intense residential development on nearby residential property. These standards usually include: - Regulation of building height - Minimum and maximum building setbacks - Buffers - Building design - Controls to limit the impact of lighting on adjacent properties

COMPREHENSIVE PLAN: A document, or series of documents, that serves as a guide for making land use changes, preparation of capital improvement programs, and the rate, timing, and location of future growth. It is based upon establishing long-term goals and objectives to guide the future growth of a city. It is also known as a Master or General Plan. Elements of a Comprehensive Plan include: - Economic Development - Environment - Housing - Land Use - Recreation and Open Space - Transportation.

CONTEXT: The setting of a site or area, including factors such as traffic, activities and land uses as well as landscape and built form.

CONTEXT CONDITION (Language): Constrains the syntax; it describes the set of wellformed expressions of a language.

CONTEXT (or site and area) APPRAISAL: A detailed analysis of the features of a site or area (including land uses, built and natural environment, and social and physical characteristics) which serves as the basis for an urban design framework, development brief, design guide or other policy or guidance.

COUNTRYSIDE DESIGN SUMMARY: Supplementary planning guidance prepared by a local authority to encourage a more regionally and locally based approach to design and planning.

CLUSTER DEVELOPMENT (GIS): An alternative development technique under zoning and subdivision regulations. A cluster subdivision is basically one in which a number of residential lots are grouped or clustered, leaving some land undivided for common use. Generally the same number of lots or dwelling units permitted under conventional subdivision procedures are clustered on smaller-than-usual lots. The land remaining from lot reduction is left undivided and is available as common area or open space.

COMMUNITY ACTIVITY CENTER (GIS) (as defined in Master Plans): A commercial center containing 10-25 acres of commercial development on a site area of 20-30 acres, serving a population of at least 50,000 and anchored by a


124

general merchandise store and may also include a supermarket. A community activity center should also include other commercial, public/quasi-public and residential uses.

COMMUNITY CENTERS (GIS): Concentration of activities, services and land uses that serve, and are focal points for, the immediate neighborhoods.

COMMUNITY (as defined in some master plans): A grouping of neighborhoods and villages, the population of which may range from 23,000 to 30,000 in suburban areas and up to 40,000 in corridor communities. Most communities should have as their centers of focal points a Community Activity Center.

CONSTRAINED LONG-RANGE PLAN (CLRP) (GIS): The approved regional plan for highway, transit, and bikeway projects, as well as major jurisdictional and regional studies.

CORRIDOR(S) (GIS): An uninterrupted path or channel of developed or undeveloped land paralleling the route of a street or highway. b. The land within one-quarter mile of both sides of designated high-volume transportation facilities, such as arterial roads. If the designated transportation facility is a limited access highway, the corridor extends one-quarter mile from the interchanges.

CRIME PATTERN ANALYSIS: Carried out by the Police and is available through liaison with the Architectural Liaison Officer/Crime Prevention Design Adviser. It comprises four components: crime series identification, trend identification, “hot-spot” analysis and general profile analysis. This last aspect includes an examination of demographic and social change and its impact on criminality and law enforcement.

DEFENSIBLE SPACE: Public and semi-public space that is “defensible” in the sense that it is surveyed, demarcated or maintained by somebody. Derived from Oscar Newman’s 1973 study of the same name, and an important concept in securing public safety in urban areas, defensible space is also dependent upon the existence of escape routes and the level of anonymity which can be anticipated by the users of the space.

DENSITY (a): dph - Dwellings per hectare.

DENSITY (b): The floorspace of a building or buildings or some other unit measure in relation to a given area of land. Built density can be expressed in terms of plot ratio (for commercial development); number of units or habitable rooms per hectare (for residential development); site coverage plus the number of floors or a maximum building height; or a combination of these.

DENSITY (c): A measure of the amount of housing in a particular area (acre or a hectare). The simplest measure of density is the number of residential units per hectare which ranges for 30u/ha in a suburban area to 200u/ha or more in a city centre. Density can also be measured using habitable rooms or bed spaces which takes account of the type of units.

DENSITY (d) (GIS): The number of dwelling units or persons per acre of land, usually expressed in units per gross acre. Single-family detached dwellings (range from less than 1 to 6 per acre) on a single lot. Townhouses (range from 6 to 12 per acre) attached in a row. Multifamily Apartments (range from 12 to 48 per acre) in one structure.

DESIGN ADVISORY PANEL: A group of people (often architects) with specialist knowledge, which advises a local authority on the design merits of planning applications or other design issues. Also known as an architect’s panel.

DESIGN ASSESSMENT: An independent assessment of a design usually carried out for a local authority by consultants, another local authority or some other agency.

DESIGN GUIDE: A document providing guidance on how development can be carried out in accordance with the design policies of a local authority or other organisation often with a view to retaining local distinctiveness.

DESIGN PRINCIPLE: An expression of one of the basic design ideas at the heart of an urban design framework, design guide, development brief or a development.

DESIGN STANDARDS: Specific, usually quantifiable measures of amenity and safety in residential areas.


125

DESIGN STATEMENT (a): A pre-application design statement is made by a developer to indicate the design principles on which a development proposal in progress is based. It enables the local authority to give an initial response to the main issues raised by the proposal. (b) A planning application design statement sets out the design principles that the planning applicant has adopted in relation to the site and its wider context, as required by PPG1.

DESIRE LINE: An imaginary line linking facilities or places which people would find it convenient to travel between easily.

DEVELOPMENT BRIEF: A document, prepared by a local planning authority, a developer, or jointly, providing guidance on how a site of significant size or sensitivity should be developed. Site-specific briefs are sometimes known as planning briefs, urban programs and development frameworks.

DEVELOPMENT (as defined in Zoning Ordinance) (GIS): Any activity that materially affects the condition or use of dry land, land under water, or any structure.

DOWNZONING (GIS): A popular term for an action that changes a property to a lower density, in effect limiting development to less-intense uses than previously permitted.

DWELLING UNIT (GIS): A room or group of rooms, occupied or intended for occupancy as separate living quarters.

ELEVATION (a): The facade of a building, or the drawing of a facade.

ELEVATION (b): The front, back or side face of a building.

ENCLOSURE: The use of buildings to create a sense of defined space.

ENCLOSURE RATIO: A measure of the shape of a street expressed as a ratio in which the first number relates to the height of the buildings and the second to the width of the street. A street with an enclosure ratio of 1:2 is therefore twice as wide as the height of the buildings.

ENERGY EFFICIENCY: The extent to which the use of energy is reduced through the way in which buildings are constructed and arranged on site.

ENVIRONMENTAL IMPACT STATEMENT (EIS) (GIS): A document, prepared by a federal agency, on the environmental impact of its proposals for legislation and other major actions that significantly affect the quality of the human environment. Environmental Impact Statements are used as tools for decision making and are required by the National Environmental Policy Act. Similar environmental analyses are undertaken by state and local agencies.

EYES OF THE STREET: Refers to views out of building that provide surveillance of public areas.

EXPRESSION (Language): Is a meaningful, wellformed element of a language; the following are synonyms: word, statement, sentence, document, diagram, model, term, piece of data, clause, and module.

FAÇADE: The front wall of a building.

FEASIBILITY: The viability of development in relation to economic and market conditions.

FENESTRATION: The arrangement of windows on a facade.

DIAGRAM: A plan showing the relationship between built form and publicly accessible space (including streets) by presenting the former in black and the latter as a white background (or the other way round).

FIXED GUIDEWAY TRANSIT (FGT): Transit service provided on its own right-of-way: a rail track, physically restricted vehicle lanes, or a dedicated roadway in the road and highway system. Both the Metrorail regional rapid transit and MARC commuter rail systems that serve Prince George’s County are FGT systems.

FLAG LOT (GIS): A flag-shaped lot, created under the Optional Residential Design Approach provisions of Subtitle 24, which has a street frontage smaller than that other required for the zone in which it is located.


126

FLOATING ZONE (GIS): A zone that is more flexible than euclidean zones in terms of permissible densities, intensities and land uses and overall development design opportunities. Most floating zones require the following findings by the District Council to be granted: 1) The proposed zone is in conformance with the Master Plan; 2) Is compatible with the surrounding community; and 3) Meets the purposes of the zone. Findings of change or mistake, required for granting a euclidean zone, are not required for floating zones. Some floating zones require Master Plan recommendation.

FLOODPLAIN (GIS): A relatively flat or lowland area adjoining a river, stream, or watercourse, which is subject to periodic, partial or complete inundation.

FLOOR AREA RATIO (FAR): The ratio of the gross floor area of a building to the area of the lot on wich it is located.

FORECAST (GIS): As defined for use in the Council of Governments (COG) Cooperative Forecasting Program, a projection tempered by stated policy considerations, including the reconciliation of past and current trends with current and future policies. Ideally, forecasts reflect the best professional judgment concerning the impact of trends and present conditions on the future trend of development and the likely effectiveness of policies to alter this trend. Therefore, forecasts should represent the most realistic assessment of the future.

FORM: The layout (structure and urban grain), density, scale (height and massing), appearance (materials and details) and landscape of development.

Frontage: Similar to facade - the front face of a building where it has its main door windows.

FRUIN ANALYSIS: A method of analysing pedestrian movement devised by Bernard Fruin. It applies a “level of service” concept to pedestrian flows. Fruin defined capacity and speeds of movement in various forms of corridors, pavements and other pedestrian routes.

FUTURE SEARCH: A participation technique enabling groups of people to identify common interests, discuss ideas and share information and experience. “Open space” is a similar technique.

GEOGRAPHIC INFORMATION SYSTEM (GIS): An organized collection of computer hardware, software and geographic data designed to efficiently capture, store, update, manipulate, analyze and display all forms of geographically referenced information.

GREEN AREA (GIS): An area of land associated with, and located on the same parcel of land as, a building for which it serves to provide light and air, or scenic, recreational, or similar purposes.

GREEN BUILDING: Practices that consider the impacts of buildings on the local, regional, and global environment, energy and water efficiency, reduction of operation and maintenance costs, minimization of construction waste, and eliminating the use of harmful building materials.

GREENWAYS: Areas of protected open space that follow natural and manmade linear features for recreation, transportation and conservation purposes and link ecological, cultural and recreational amenities. GRAIN (b): The complexity and coarseness of an urban area. Fine grained areas have a large number of different buildings and closely spaces streets. Course grained areas have large blocks and building and little architectural variety.

GROSS FLOOR AREA (GFA): The total number of square feet of floor area in a building.

HEIGHT: The height of a building can be expressed in terms of a maximum number of floors; a maximum height of parapet or ridge; a maximum overall height; any of these maximum heights in combination with a maximum number of floors; a ratio of building height to street or space width; height relative to particular landmarks or background buildings; or strategic views.

HIGH-OCCUPANCY VEHICLE (HOV): A passenger vehicle containing more than one person. HOV facilities—such as John Hanson Highway (US 50) in Prince George’s County—generally require a minimum number of occupants for a vehicle to be granted access to HOV lanes.


127

HIGH STREET: Traditionally a high street is a road through the heart of an urban area that carries all of the through traffic and is also where the greatest number and most important shops are sited together with civic functions. These streets would once have been the “shopfront” of the town or city. Now bypasses often mean that they no longer carry traffic but they do still tend to be the focus for the shopping area.

HUMAN SCALE: The use within development of elements which relate well in size to an individual human being and their assembly in a way which makes people feel comfortable rather than overwhelmed.

IDENTITY: The memorability or sense of place on an urban area. An area with identity is recognisable and has a distinctive character created by the size, shape or design of the buildings.

IN-CURTILAGE PARKING: Parking within a building’s site boundary, rather than on a public street or space.

INDEPENDENT DESIGN AUDIT: An assessment of a design, carried out for a local authority by consultants, another local authority or some other agency.

INDICATIVE SKETCH: A drawing of building forms and spaces which is intended to convey the basic elements of a possible design.

INTERPRETATION (Language): Extracts the information from a piece of data; it is a mapping of data to a semantic domain.

LANDMARK: A building or structure that stands out from its background by virtue of height, size or some other aspect of design.

LANDSCAPE: The character and appearance of land, including its shape, form, ecology, natural features, colours and elements and the way these components combine. Landscape character can be expressed through landscape appraisal, and maps or plans. In towns “townscape” describes the same concept.

LAND USE (OR USE) (GIS): The types of buildings and activities existing in an area or on a specific site. Land use is to be distinguished from zoning, the latter being the regulation of existing and future land uses. LANGUAGE: Is a possibly infinite set of expressions used to communicate; it is a synonym to notation; a language allows us to syntactically represent information.

LAYOUT: The way buildings, routes and open spaces are placed in relation to each other.

LAYOUT STRUCTURE: The framework or hierarchy of routes that connect in the local area and at wider scales.

LEGIBILITY: The degree to which a place can be easily understood and traversed.

LIVE EDGE: Provided by a building or other feature whose use is directly accessible from the street or space which it faces; the opposite effect to a blank wall.

LOCAL DISTINCTIVENESS: The positive features of a place and its communities which contribute to its special character and sense of place.

LYNCHIAN ANALYSIS: The widely used method of context appraisal devised by the urban designer Kevin Lynch. It focuses on gateways to an area, nodes, landmarks, views and vistas, and edges and barriers.

MAJOR COMMUNITY ACTIVITY CENTER (as defined in master plans): A commercial center containing 20-50 acres of commercial development on a site area of 30-60 acres, serving a population of at least 150,000. A major community activity center typically includes uses listed under community activity center plus one or more general merchandise anchor stores. Can also be defined as a community focal point providing for a concentration of activities such as general retail, service commercial, professional office, higher-density housing, and appropriate public and open space uses easily accessible by pedestrians.

MANDATORY (LAND) DEDICATION (GIS): Land excluded from subdivision approved for residential development. The land is dedicated to M-NCPPC (or held in private ownership) for the purpose of providing suitable and adequate open space, light, and air to serve the recreational needs of the future occupants of the subdivision.


128

MASTER PLAN: A document that guides the way an area should be developed. It includes a compilation of policy statements, goals, standards, maps and pertinent data relative to the past, present, and future trends of a particular area of the County including, but not limited to, its population, housing, economics, social patterns, land use, water resources and their use, transportation facilities, and public facilities.

MASSING (a): The size and height of a building.

MASSING (b): The combined effect of the height, bulk and silhouette of a building or group of buildings.

MIXED USE (MU): A type of development that combines residential, commercial, and/or office uses, within a commercial or office zoning district, into one development or building. For example, a mixed-use building could have several floors. On the bottom floor, the space could be dedicated to retail or offices. The remaining two or three floors could be for apartments or condominiums. A Mixed Use Combining District allows residential, commercial, retail, and office uses to be combined in a single development. Under the Smart Growth Infill Ordinance two types of Mixed Use development are now possible with adopted neighborhood plans that include these uses as part of their plans: - Neighborhood Urban Center allows a variety of residential types (condos, apartments, townhouses) and commercial, office, and retail uses clustered together in a development of less than forty acres. - A Neighborhood Mixed Use Building allows residential uses above ground floor commercial uses. - Multi-Family: A building that is designed to house more than one family.

MIXED USES: A mix of uses within a building, on a site or within a particular area. “Horizontal” mixed uses are side by side, usually in different buildings. “Vertical” mixed uses are on different floors of the same building.

MIXED-USE ZONING (GIS): Zoning that permits a combination of uses within a single development. Many zoning districts specify permitted combinations of, for example, residential and office/commercial uses. The term has also been applied to major developments, often with several high-rise buildings, that may contain offices, shops, hotels, apartments and related uses.

MODAL SPLIT: How the total number of journeys in an area or to a destination is split between different means of transport, such as train, bus, car, walking and cycling.

MODELLING LANGUAGE (Language): Is used for specifying and documenting properties of a system in different abstractions, and from different points of view.

MOVEMENT: People and vehicles going to and passing through buildings, places and spaces. The movement network can be shown on plans, by space syntax analysis, by highway designations, by figure and ground diagrams, through data on origins and destinations or pedestrian flows, by desire lines, by details of public transport services, by walk bands or by details of cycle routes.

NATURAL SURVEILLANCE (or supervision): The discouragement to wrong-doing by the presence of passers-by or the ability of people to be seen out of surrounding windows. Also known as passive surveillance (or supervision).

NEIGHBORHOOD: (As defined in some master plans) The smallest unit of community structure. Neighborhood population ranges from 3,000 to 6,000, depending on the ratio of single-family to multifamily housing. NEIGHBORHOOD CONVENIENCE CENTER (as defined in master plans): A commercial center containing 2-6 acres of commercial development on a site of 4-10 acres, serving a population of approximately 8,000 and anchored by a small grocery or drug store. It should also include a limited range of other commercial and residential uses.

NET LOT AREA (GIS): The total contiguous area included within a lot, excluding public ways (i.e., streets, alleys) and land with 100-year floodplain.

NODE (GIS): A location along a corridor at a major intersection or major transit stop (bus or rail) that consists of a concentration of high-intensity, mixed-use residential and commercial development. Nodes should be interspersed with stretches of lower intensity land uses or open space.

NODE: A place where activity and routes are concentrated often used as a synonym for junction.


129

NOTATION (Language): Is a syntactic representation of information; a synonym to language; what the user deals with.

OPEN SPACE (a) (land use, not zoning) (GIS): Areas of land not covered by structures, driveways, or parking lots. Open space may include homeowners association common areas, parks, lakes, streams and ponds, etc.

OPEN SPACE (b): An area set aside or reserved for public or private use with very few improvements. Types of open space include include:- Golf Courses- Agricultural Land- Parks- Greenbelts- Nature Preserves. In many cases, land designated as open space lies within the 100-year flood zone, has sensitive environmental features such as wetlands or aquifer recharge features such as caves and fault lines, or has unstable slopes.

PASSIVE SURVEILLANCE: See “natural surveillance”.

PEDESTRIAN-ORIENTED DESIGN (GIS): Land use activities that are designed and arranged in a way that emphasizes travel on foot rather than by car. The factors that encourage people to walk are often subtle, but they most regularly focus upon the creation of a pleasant environment for the pedestrian. Elements include compact, mixed-use development patterns with facilities and design that enhance the environment for pedestrians in terms of safety, walking distances, comfort, and the visual appeal of the surroundings. Pedestrian-friendly environments can be created by locating buildings close to the sidewalk, by lining the street with trees, and by buffering the sidewalk with planting strips or parked cars, small shops, street-level lighting and signs, and public art or displays.

PERFORMANCE CRITERION (pl. criteria): A means of assessing the extent to which a development achieves a particular functional requirement (such as maintaining privacy). This contrasts with a standard, which specifies how a development is to be designed (by setting out minimum distances between buildings, for example). The art of urban design lies in balancing principles which may conflict. Standards may be too inflexible to be of use in achieving a balance. Performance criteria, on the other hand, make no prior assumptions about the means of achieving a balance.

PERMEABILITY (a): The degree to which an area has a variety of pleasant, convenient and safe routes through it.

PERMEABILITY (b): The ease with which people can move around an urban area. A permeable neighbourhood has plenty of streets and it is possible to move through the area by a variety of routes.

PERSPECTIVE: Illustration showing the view from a particular point as it would be seen by the human eye.

PLACECHECK: A type of urban design audit advocated by the Urban Design Alliance, based on the Connected City approach. A local collaborative alliance or partnership uses checklists to investigate the connections in the built environment, in its movement network and among the people who shape it. The Placecheck becomes the first step in a continuing collaborative process of urban design.

PLANNING: The process of setting development goals and policy, gathering and evaluating information, and developing alternatives for future actions based on the evaluation of the information.

PLANNING BRIEF: This guide refers to site-specific briefs as development briefs. Other names, including planning briefs, urban programs and development frameworks are also used.

PLANNING FOR REAL: A participation technique (pioneered by the Neighbourhood Initiatives Foundation) that involves residents and others with an interest coming together to make a model of their area and using it to help them determine their priorities for the future.

PLANNING POLICY GUIDANCE NOTES (PPGs): Documents embodying Government guidance on general and specific aspects of planning policy to be taken into account in formulating development plan policies and in making planning decisions.

PLOT RATIO (a): A measurement of density generally expressed as gross floor area divided by the net site area.

PLOT RATIO (b): A measure of density for non-residential used. This is expressed as a ratio in which the first number relates to the floor area of the building and the second to the area of the site. A 2:1 ratio therefore denotes a


130

building that has two times the floor area of the site. This could be a two storey building covering the entire site or a four storey building covering half of the site.

PRIVACY DISTANCE: The distance between the habitable windows of a dwelling necessary to ensure privacy. This is normally 20-23m but can be reduced to 15m in city centres. Where a dwelling has a front on a back the privacy distance relates to the back. On double-loaded flats (see above) it relates to the front.

PROACTIVE DEVELOPMENT CONTROL: Any process by which a local authority works with potential planning applicants to improve the quality of development proposals as early as possible before a planning application is submitted.

PROGRAMMING LANGUAGE (Language): Is used for programming software systems.

PUBLIC ART: Permanent or temporary physical works of art visible to the general public, whether part of the building or free-standing: can include sculpture, lighting effects, street furniture, paving, railings and signs.

PUBLIC DOMAIN: The parts of a village, town or city (whether publicly or privately owned) that are available, without charge, for everyone to use or see, including streets, squares and parks. Also called public realm.

PUBLIC/PRIVATE INTERFACE: The point at which public areas

PUBLIC REALM: The public spaces of an urban area. This includes streets, squares and parks where people are free to walk. It does not include private gardens or courtyards or shopping malls.

SEMANTICS (Language): Defines the meaning of a notation; what information do the expressions in the notation describe.

SEMANTIC DOMAIN (Language): Is a well understood domain of elements. Elements of the semantic domain describe the important properties of what we are trying to define using a language in our context; this means software and hardware systems, and components of such systems.

SEMANTIC MAPPING (Language): Is a mapping that relates each syntactic construct to a construct of the semantic domain; it usually explains new constructs in terms of known constructs.

SETBACK: The distance between a building or structure (not including ground-level parking lots or other paved surfaces) from property lines or from other buildings.

SEVERE SLOPES: Those slopes that are greater than 25 percent. (Example: a 25-foot change in elevation in a 100-foot horizontal distance.)

SITING: The positioning of a building on the ground.

SMART GROWTH: A perspective, method, and goal for managing the growth of a community. It focuses on the long-term implications of growth and how it may affect the community, instead of viewing growth as an end in itself. The community can vary in size; it may be as small as a city block or a neighborhood, or as large as a city, a metropolitan area, or even a region. Smart Growth promotes cooperation between often diverse groups to arrive at sustainable long-term strategies for managing growth. It is designed to create livable cities, promote economic development, and protect open spaces, environmentally sensitive areas, and agricultural lands.

SMART HOUSING: An initiative of the City of Austin to promote sustainable and equitable housing development for low- to moderate-income households. Housing developed under this program would serve the needs of a variety of income levels and be accessible to people with disabilities. The SMART Housing Initiative also requires that housing developed under the program have ready access to transit. SMART stands for:

S afe

M ixed-Incom


131

A accessible

R easonably Priced

T ransit-oriented

SPECIFIC DESIGN PLAN (SDP): Phase III of the Comprehensive Design Zone process. It is a precise site plan that includes exact locations of lots, buildings and streets, etc., architectural plans, exterior building elevations and detailed landscaping plans.

SPRAWL: A haphazard and disorderly form of urban development. There are several elements that characterize sprawl: - Residences far removed from stores, parks, and other activity centers, - Scattered or “leapfrog” development that leaves large tracts of undeveloped land between developments, - Commercial strip development along major streets, - Large expanses of low-density or single use development such as commercial centers with no office or residential uses, or residential areas with no nearby commercial centers, - Major form of transportation is the automobile, - Uninterrupted and contiguous low- to medium-density (one to six du/ac) urban development, - Walled residential subdivisions that do not connect to adjacent residential development.

STAR BUILDING: This relates to a building that is special by virtue of its role. Traditionally this would include churches, town halls and other public institutions. These buildings should be commissioned by public competition but are not subject to the same rules as other buildings.

STREET HIERARCHY: The relative importance of different streets. This traditionally includes high streets that carry most through traffic and have the greatest number of shops, secondary streets that take traffic into each neighbourhood and have fewer shops and local streets that give access to each of the buildings. Today high streets are often pedestrianised and through traffic is carried on a new level of the hierarchy - the boulevard.

STREETSCAPE: The space between the buildings on either side of a street that defines its character. The elements of a streetscape include: - Building Frontage/Façade, - Landscaping (trees, yards, bushes, plantings, etc.), - Sidewalks, - Street Paving, - Street Furniture (benches, kiosks, trash receptacles, fountains, etc), - Signs, - Awnings, - Street Lighting. SUB-LANGUAGE (Language): Is a subset of the syntactic elements, together with an appropriate adaptation projection.

SUSTAINABILITY: A concept and strategy by which communities seek economic development approaches that benefit the local environment and quality of life. Sustainable development provides a framework under which communities can use resources efficiently, create efficient infrastructures, protect and enhance the quality of life, and create new businesses to strengthen their economies. A sustainable community is achieved by a long-term and integrated approach to developing and achieving a healthy community by addressing economic, environmental, and social issues. Fostering a strong sense of community and building partnerships and consensus among key stakeholders are also important elements.

SUPPORTING CAST BUILDING: This relates to the majority of buildings in an urban area - all of the housing, shops and offices. These create the urban form of an urban area and should be subject to urban design rules.


132

TEXTUAL LANGUAGE (Language): Is a language consisting of linear strings of characters and symbols; words, sentences, etc.

TRADITIONAL NEIGHBORHOOD CORRIDOR: The combination of an activity center and the transportation connections linking it to the rest of city. These links may be made by frequent public transit service, walking, cycling, or by car. The major throughway into a traditional neighborhood corridor should be wide enough to accommodate all modes of vehicular transportation, on-street parking, as well as provide space for safe and inviting sidewalks for pedestrians. A Traditional Neighborhood Corridor is characterized by a mixture of various uses and densities such as stores, offices, and different types of housing.

TRANSIT-ORIENTED DEVELOPMENT (TOD): A form of development that emphasizes alternative forms of transportation other than the automobile - such as walking, cycling, and mass transit - as part of its design. Transit-Oriented Development locates retail and office space around a transit stop. This activity center is located adjacent to a residential area with a variety of housing options such as apartments, townhouses, duplexes, and single family houses. Similar to a Traditional Neighborhood Development.

TRANSIT NODES: Stops along a public transportation route where people board and disembark, often where one or more routes intersect with each other. These sites can provide ideal locations for mixed use development as well as transit-oriented development.

URBAN BLOCK: This is an area bounded by streets and occupied by buildings. Sometimes called a perimeter block, the buildings face outwards onto the streets often with a private courtyard in the centre. For housing development this courtyard is often used by residents (sometimes for gardens) for shops it is where servicing takes place and of offices it is often an atrium.

VISUAL/DIAGRAMMATIC LANGUAGE (Language): Is a language based mainly on graphic (topological geometric) elements; it can employ textual elements too.

VISUAL FORMALISM (Language): Is a diagrammatic language that has formal syntax and semantics.

ZONING (a) (GIS): The classification of land by types of uses permitted and prohibited in a district and by densities and intensities permitted and prohibited, including regulations regarding building location on lots. ZONING (b): The method used by cities to promote the compatibility of land uses by dividing tracts of land within the city into different districts or zones. Zoning ensures that a factory is not located in the middle of a residential neighborhood or that a bar is not located next to an elementary school.

ZONING CATEGORY or DISTRICT (GIS): An area designated (zoned) for a type of land use and for a certain density or intensity of development within that type.

ZONING MAP (GIS): The official 1"=200' scale map showing the location of all zoning categories in a given area.

Conventions

Some Conventions of Abbreviated terms in GIS, in Computer Aided Design, and in other languages.

2D: Two Dimensional

3D: Three Dimensional

AEC: Architecture, Engineering, Construction

ALKIS: German National Standard for Cadastral Information

ATKIS: German National Standard for Topographic and Cartographic Information

B-Rep: Boundary Representation


133

CAD: Computer Aided Design

CAAD: Computer Aided Architectural Design

DTM: Digital Terrain Model

DXF: Drawing Exchange Format

FM: Facility Management

GDF: Geographic Data Files

GML: Geography Markup Language

IAI: International Alliance for Interoperability

IETF: Internet Engineering Task Force

IFC: Industry Foundation Classes

ISO: International Organization for Standardisation

LOD: Level of Detail

NBIMS: National Building Information Model Standard

OASIS: Organisation for the Advancement of Structured Information Standards

OGC: Open Geospatial Consortium

OSCRE: Open Standards Consortium for Real Estate

SIG 3D: Special Interest Group 3D of the GDI NRW

TC211: ISO Technical Committee 211

TIC: Terrain Intersection Curve

TIN: Triangulated Irregular Network

UML: Unified Modeling Language

URI: Uniform Resource Identifier

VRML: Virtual Reality Modeling Language

W3C: World Wide Web Consortium

XML Extensible Markup Language


134

References


135

About Emergent Urbanism « Emergent Urbanism. Available at: http://emergenturbanism.com/about/ [Accessed December 10, 2009].

eCAADe 2005. Available at:

http://www.ecaade.org/conference/archive/ecaade2005/site/ecaade05program.htm [Accessed December 10, 2009].

FCT : Resumo do Projecto. Available at:

http://www.fct.mctes.pt/projectos/pub/2006/Painel_Result/vglobal_projecto.asp?idProjecto=64384&idElemConcurso=909 [Accessed July 22, 2009].

From Here To Modernity Architects - CIAM. Available at: http://www.open2.net/modernity/4_2.htm

[Accessed November 23, 2009]. IEEE. Available at: http://www.patternlanguage.com/archive/ieee/ieeetext.htm#6 [Accessed

December 10, 2009]. Notes on "Quality of Life". Available at: http://www.gdrc.org/uem/qol-define.html [Accessed

December 10, 2009]. Recipes FRAME. Available at:

http://www.patternlanguage.com/leveltwo/recipesframe.htm?/leveltwo/../sequencetheory/sequenceopener.htm [Accessed December 10, 2009].

(UNESCO), U.N.E.S.A.C.O., 2009. Education for All Global Monitoring Report 2009: Overcoming

inequality- why governance matters, Oxford University Press, USA. Alexander, C., Neis, H. & Anninou, A., 1987. A new theory of urban design, Oxford University Press,

USA. Alexander, C., 1979. The Timeless Way of Building, Oxford University Press. Alexander, C., Ishikawa, S. & Silverstein, M., 1977. A Pattern Language: Towns, Buildings,

Construction Later printing., Oxford University Press. Bafna, S., 2003. Space syntax: A brief introduction to its logic and analytical techniques. Environment

and Behavior, 35(1), 17. Balachandran, M., 1980. Regional Statistics: A Guide to Information Sources, Gale Group. Batty, M., 2004. Distance in space syntax. Beirão, J.N.Duarte J.P., 2005. Urban Grammars: Towards Flexible Urban Design. Available at:

http://cumincad.scix.net/cgi-bin/works/Show?_id=2005_491&sort=DEFAULT&search=jos%e9%20duarte&hits=99 [Accessed December 10, 2009].

Beirão, J.N.Montenegro N.J.Gil.J.P.Duarte.R.Stoufs., 2009. The city as a street system: A street

description for a city ontology. Available at: http://cumincad.scix.net/cgi-


136

bin/works/Show?_id=sigradi2009_821&sort=DEFAULT&search=nuno%20montenegro&hits=7 [Accessed November 23, 2009].

Best, R. & De Valence, G., 1999. Building in value: pre-design issues, Butterworth-Heinemann. Blass, &. & Gurevich, &., 2003. Algorithms: A Quest for Absolute Definitions. Bulletin of European

Association for Theoretical Computer Science, 81. Available at: http://research.microsoft.com/~gurevich/Opera/164.pdf [Accessed December 10, 2009].

Borja, J. & Castells, M., 1998. Local y global. La gestión de las ciudades en la era de la información.

Madrid, Editorial Santillana. Britannica, E. & inc, 2002. Encyclopedia Britannica, Encyclopaedia Britannica. Burgess, E.W. & Park, R.E., 2002. The growth of the city. The Blackwell City Reader. Oxford. Buyya, R., Abramson, D. & Venugopal, S., 2005. The grid economy. Proceedings of the IEEE, 93(3),

698–714. Caneparo, L. et al., 2007. URBAN GENERATOR. In Computer-Aided Architectural Design Futures 2007:

Proceedings of the 12th International CAAD Futures Conference. p. 347. Carmona, M., Marshall, S. & Stevens, Q., 2006. Design codes: their use and potential. Progress in

Planning, 65(4), 209-289. Available at: http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VC7-4K18VY9-1&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1084944631&_rerunOrigin=scholar.google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=af0a5578941ca0982007efecee9944fd#implicit0 [Accessed November 13, 2009].

Castells, M., 2005. Grassrooting the space of flows. Internationalizing Cultural Studies: An Anthology,

627. Castells, M., 2004. Space of flows, space of places: Materials for a theory of urbanism in the

information age. The cybercities reader, 82–93. Castells, M., 2005. The network society: A cross-cultural perspective, Edward Elgar Publishing,

Incorporated. Castells, M., 2003. The Internet Galaxy, Oxford University Press. Castells, M., 2000. The rise of the network society, Wiley-Blackwell. Chenery, H.B. & Taylor, L., 1968. Development patterns: among countries and over time. The Review

of Economics and Statistics, 391–416. Chomsky, N., 1965. Aspects of the Theory of Syntax, MIT press.

Chomsky, N., 1957. Syntactic structures, The Hague: Mouton.


137

Christensen, K.S. & Bang, H.K., 2003. Knowledge management in a project-oriented organization: three perspectives. Journal of Knowledge Management, 7(3), 116–128.

Clark, T.N., 2000. Old and new paradigms for urban research: Globalization and the fiscal austerity

and urban innovation project. Urban Affairs Review, 36(1), 3. Coleman, J.S., 1990. Equality and achievement in education, Westview press. Cormen, T.H. et al., 2001. Introduction to algorithms, The MIT press. Croft, W. & Cruse, D.A., 2004. Cognitive linguistics, Cambridge Univ Pr. Deacon, T.W. & Deacon, Terrence William, 1998. The Symbolic Species: The Co-Evolution of Language

and the Brain, New York: W. W. Norton & Company. Dechter, R. & Pearl, J., 1987. Network-based heuristics for constraint-satisfaction problems. Artificial

Intelligence, 34(1), 1–38. Dempsey, N., 2008. Quality of the built environment in urban neighbourhoods. Planning Practice and

Research, 23(2), 249–264. DETR, C., 2000. By Design, Urban Design in the Planning System: Towards Better Practice, Thomas

Telford Publishing, London. Drucker, P.F., 2007. Innovation and entrepreneurship: Practice and principles, Butterworth

Heinemann. Duarte, J.P., 2003. A Discursive Grammar for Customizing Mass Housing - The case of Siza´s houses at

Malagueira. Available at: http://cumincad.scix.net/cgi-bin/works/Show?_id=ecaade03_665_181_duarte&sort=DEFAULT&search=jos%e9%20duarte&hits=99 [Accessed December 10, 2009].

Duarte, J.P. & Castro, 2007. Personalizar a habitação em série, Fundação Calouste Gulbenkian,

Fundação para a Ciência e a Tecnologia. Dudley, J.M., Creating descriptive metadata for patron browsing and selection on the Bryant &

Stratton College Virtual Library. Evans, R. et al., 2007. Urban Design Compendium 2 (UDC 2): Delivering Quality Places. English

Partnerships and the Housing Corporation, London, 172. Evitts, P., 2000. A UML pattern language, New Riders Publishing Thousand Oaks, CA, USA. Farquhar, A., 1997. Ontolingua tutorial, Knowledge Systems Lab Stanford University. Fayyad, U.M. et al., 1996. Advances in knowledge discovery and data mining, MIT press. Fonseca, F.T. & Egenhofer, M.J., 1999. Ontology-driven geographic information systems. In

Proceedings of the 7th ACM international symposium on Advances in geographic information systems. pp. 14–19.


138

Fonseca, F.T. et al., 2002. Using ontologies for integrated geographic information systems. Transactions in GIS, 6(3), 231–257.

Formaggio, D. & Ivars, J.F., 1976. Arte, Forsythe, D. & Hess, D.J., 2001. Studying those who study us, Stanford University Press. Fritsch, M., 2004. Cooperation and the efficiency of regional R&D activities. Cambridge Journal of

Economics, 28(6), 829. Gandon, F., 2002. Distributed Artificial Intelligence and Knowledge Management: ontologies and

multi-agent systems for a corporate semantic web. Doctoral school of sciences and technologies of information and communication, University of Nice Sophia Antipolis.

Gann, D., Salter, A. & Whyte, J., 2003. Design Quality Indicator as a tool for thinking. Building

Research and Information, 31(5), 318–333. Garreau, J., 1998. Edge cities. Landscape Architecture, 78(8), 51–52. Gil, J.Montenegro N Beirao J.N Duarte J.P., 2009. On the Discovery of Urban Typologies: Data Mining

the Multi-dimensional Character of Neighbourhoods. Available at: http://cumincad.scix.net/cgi-bin/works/Show?_id=ecaade2009_148&sort=DEFAULT&search=nuno%20montenegro&hits=7 [Accessed November 23, 2009].

Gil, J.Duarte J.P., 2008. Towards an Urban Design Evaluation Framework. Available at:

http://cumincad.scix.net/cgi-bin/works/Show?_id=ecaade2008_147&sort=DEFAULT&search=gil&hits=6 [Accessed January 19, 2010].

Gleick, J., 1987. Chaos: Making a new science. New York: Viking Penguin, 8. Goldstein, F.C. & Levin, H.S., 1987. Disorders of reasoning and problem-solving ability.

Neuropsychological rehabilitation, 327–354. Graham, S. & Marvin, S., 2001a. Splintering urbanism, Routledge. Graham, S. & Marvin, S., 2001b. Splintering urbanism: networked infrastructures, technological

mobilities and the urban condition, Routledge. Gruber, T., 2005. What is an Ontology, Gruber, T.R., 1991. The role of common ontology in achieving sharable, reusable knowledge bases. In

Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference, Cambridge, MA. pp. 601–602.

Gruber, T.R. & others, 1993. A translation approach to portable ontology specifications. Knowledge

acquisition, 5, 199–199. Gruber, T.R. & others, 1995. Toward principles for the design of ontologies used for knowledge

sharing. International Journal of Human Computer Studies, 43(5), 907–928.


139

Guarino, N., 1998. Formal Ontology in Information Systems., IOS press Amsterdam. Guarino, N., 1997. Understanding, building and using ontologies. International Journal of Human

Computer Studies, 46, 293–310. Guterres, j., (2004), Da Cidade Justa e Bem Ordenada, inovação no Planeamento Urbano – Métricas

Urbanísticas e de Vivência na Cidade (aplicação a Lisboa). Hall, E.T., 1973. The hidden dimension. Leonardo, 6(1), 94–94. Harper, D. & Harper, Douglas, [[Online Etymology Dictionary]]. In Dystopia. Available at:

http://www.etymonline.com/index.php?term=Dystopia [Accessed December 10, 2009]. Harvey, D., 1995. Globalization in question, Department of Development and Planning, Aalborg

University. Harvey, D., 2009. Social justice and the city, Univ of Georgia Pr. Harvey, D., 2006. Spaces of global capitalism, Verso. Hayes, P.J., 1979. The naive physics manifesto. Expert Systems. Hayes-Roth, B., 1985. A blackboard architecture for control. Artificial intelligence, 26(3), 251–321. Hayes-Roth, F. et al., 1983. Building expert systems, Addison-Wesley Reading, MA. Healey, P., 1997. Making strategic spatial plans: innovation in Europe, Routledge. Healey, P., 2007. Urban complexity and spatial strategies: towards a relational planning for our

times, Routledge. Herrschel, T. & Newman, P., 2002a. Governance of Europe's city regions: Planning, policy and politics,

Routledge. Herrschel, T. & Newman, P., 2002b. Governance of Europe's city regions: planning, policy and politics,

Routledge. Higueras, E., 1997. Urbanismo bioclimático. Criterios medioambientales en la ordenación de

asentamientos. Resumen de Tesis Doctoral. ETS de arquitectura Dirección General de la Vivienda, Arquitectura y Urbanismo.

Hillier, B. & Hanson, J., 1984. The social logic of space, Cambridge University Press Cambridge. Hillier, B. et al., 1976. Space syntax. Environment and Planning B, 3(2), 147–185. Hirst, P., Thompson, G. & Bromley, S., 1996. Globalization in question, Polity Press Cambridge. Hix, S., 1999. The political system of the European Union, Macmillan. Hoel, M., 1991. Efficient international agreements for reducing emissions of CO2. The Energy Journal,

12(2), 93–108. Houghton, J., 2005. Global warming. Reports on Progress in Physics, 68, 1343–1403.


140

Huberman, A.M. & Miles, M.B., 1994. Data management and analysis methods. Handbook of

qualitative research, 42, 8–444. Hurley, A.L. et al., ‘Measuring’sustainable living agendas. Jabareen, Y.R., 2006. Sustainable urban forms: their typologies, models, and concepts. Journal of

Planning Education and Research, 26(1), 38. Jacobs, J., 1961. The life and death of great American cities. New York. Jacobs, J., 1961. The Death and Life of Great American Cities 1st ed., Random House. Jori, A., 2003. Aristotele, B. Mondadori. Katz, J.J., 1964. Mentalism in linguistics. Language, 124–137. Kay, M., 1984. Functional unification grammar: A formalism for machine translation. In Proceedings

of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics. pp. 75–78.

Kennedy, H., 2001. The ESRI Press dictionary of GIS terminology, ESRI Press. Kleene C. , &. & Kleene C., S., 1936. General Recursive Functions of Natural Numbers.

Mathematische Annalen, 112(5), 727–742. Kolbe, T.H., Gr\öger, G. & Pl\ümer, L., 2005. CityGML–Interoperable access to 3D city models. In First

International Symposium on Geo-Information for Disaster Management GI4DM. Kolbe, T.H., Gröger, G. & Plümer, L., 2005. CityGML–interoperable access to 3d city models. In

Proceedings of the first International Symposium on Geo-Information for Disaster Management, Springer Verlag. Springer.

Krier, L. & Porphyrios, D., 1984. Leon Krier, Architectural Design. Lawson, B., 2006. How designers think: the design process demystified, Architectural press. Lefebvre, G. & Evanson, E.M., 1962. The French Revolution 0th ed., Columbia University Press. Lin, J. & Mele, C., 2005. The Urban Sociology Reader, Routledge. Lindekens, J.A.A.H., 2004. Re-Using Re-Design Knowledge. Available at: http://cumincad.scix.net/cgi-

bin/works/Show?_id=ddss2004_d%2d209&sort=DEFAULT&search=lindekens&hits=2 [Accessed January 20, 2010].

Luca, C., 2007. Generative platform for urban and regional design. Automation in Construction, 16(1),

70–77. Luger, G.F., 2005. Artificial intelligence: Structures and strategies for complex problem solving,

Addison-Wesley Longman. Lynch, K., 1960. The image of the city, MIT press.


141

Mabert, V.A., Soni, A. & Venkataramanan, M.A., 2003. Enterprise resource planning: Managing the

implementation process. European Journal of Operational Research, 146(2), 302–314. Maclaren, V.W., 2004. " Urban Sustainability SE Reporting" il. The sustainable urban development

reader, 46. Manning, C.D. & Sch\ütze, H., 2002. Foundations of statistical natural language processing, MIT

Press. Mark, D.M., Smith, B. & Tversky, B., 1999. Ontology and geographic objects: An empirical study of

cognitive categorizaton. Lecture Notes in Computer Science, 283–298. Marshall, S., 2005. Streets & patterns, Spon Pr. Maslow, A.H., Motivation and personality, 1954. New York: Harper&Brothers. Mason, R.O. & Mitroff, I.I., 1981. Challenging strategic planning assumptions: Theory, cases, and

techniques, John Wiley & Sons Inc. McCallum, M.H., Ellickson, D.E. & Goldberg, H.G., 1996. 1996 Editions of AIA Design/Build Standard

Forum Agreements, The. Constr. Law., 16, 38. McCarthy, J., 1980. Circumscription - A Form of Non-Monotonic Reasoning. McGuinness, D.L. et al., 2000. An environment for merging and testing large ontologies. In

PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING-INTERNATIONAL CONFERENCE-. pp. 483–493.

Mintzberg, H., 2000a. The rise and fall of strategic planning, Pearson Education. Mintzberg, H., 2000b. The rise and fall of strategic planning, Pearson Education. Mitchell, W.J., 1993. A computational view of design creativity. Modeling Creativity and Knowledge-

Base Creative Design, 25–42. Montenegro, N.C.;.Duarte J.P., 2009. Computational Ontology of Urban Design: Towards a City

Information Model. Available at: http://cumincad.scix.net/cgi-bin/works/Show?_id=ecaade2009_133&sort=DEFAULT&search=nuno%20montenegro&hits=7 [Accessed November 23, 2009].

Montenegro, N.C.;.Duarte J.P., 2008. Towards a Computational Description of Urban Patterns.

Available at: http://cumincad.scix.net/cgi-bin/works/Show?_id=ecaade2008_113&sort=DEFAULT&search=nuno%20montenegro&hits=7 [Accessed November 23, 2009].

Montgomery, M.R., 2008. The urban transformation of the developing world. science, 319(5864),

761. Moughtin, C. & Shirley, P., 2005. Urban design: green dimensions, Architectural Pr.


142

Moughtin, J.C. et al., 2003. Urban Design: Method and Techniques, Second Edition 2nd ed., Architectural Press.

Mumford, L., 1968. The City in History: Its Origins, Its Transformations, and Its Prospects Later

Printing., Harvest Books. Navigli, R. & Velardi, P., 2005. Structural semantic interconnections: a knowledge-based approach to

word sense disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(7), 1075–1086.

Neches, R. et al., 1991. Enabling technology for knowledge sharing. AI magazine, 12(3), 36. Newman, O., 1972. Defensible space: Crime prevention through urban design. New York. Novello, T.C. et al., An approach for systematic interface design from knowledge models. Noy, N.F., Fergerson, R.W. & Musen, M.A., 2000. The knowledge model of Protege-2000: Combining

interoperability and flexibility. Lecture Notes in Computer Science, 17–32. Noy, N.F. et al., 2001. Creating semantic web contents with protege-2000. IEEE Intelligent Systems,

16(2), 60–71. Noy, N.F., Fergerson, R.W. & Musen, M.A., 2000. The knowledge model of Protege-2000: Combining

interoperability and flexibility. Lecture Notes in Computer Science, 17-32. O'Brien, J. & Marakas, G., 2006. Management Information Systems with MISource 2007 8th ed.,

McGraw-Hill/Irwin. Olgyay, V. & Olgyay, A., 1963. Design with climate: bioclimatic approach to architectural regionalism,

Princeton University Press Princeton, New Jersey, USA. Pedro, J.B., 2001a. Programa Habitacional: espaços e compartimentos. Lisboa: Laboratório Nacional

de Engenharia Civil, 1. Pedro, J.B., 2001b. Programa habitacional: vizinhança próxima. Lisboa: Laboratório Nacional de

Engenharia Civil. Pereira, M.L., A leitura da imagem de uma área urbana como preparaç\ ao para o planeamento-acç\

ao da sua reabilitaç\ ao, LNEC. Pickering, A., 1992. Science as practice and culture, University of Chicago Press. Pieterse, J.N., 2009. Globalization and culture: global mélange, Rowman & Littlefield Publishers. de Pinho, C.M. & Goltz, E., Construção ao de ontologias espaciais: O lote urbano. Podgorski, E., 2008. Word Foration Types: Compounding, GRIN Verlag. Portas, N., Domingues, Á. & CabraL, J., 2003. Políticas Urbanas: tendências, estratégias e

oportunidades, Fundação ao Calouste Gulbenkian.


143

Rodrıguez, A., Moulaert, F. & Swyngedouw, E., 2001. Nuevas politicas urbanas para la revitalización de las ciudades en Europa. Ciudad y Territorio. Estudios Territoriales, 33(129), 409–424.

Rogers, A. & Vertovec, S., 1995. The urban context: ethnicity, social networks, and situational

analysis, Berg Pub Ltd. Rosen, C., 2000. World resources 2000-2001, Elsevier. Rossi, A., 1984. The Architecture of the City, The MIT Press. Rueda, S., 1998. La ciudad compacta y diversa frente a la conurbación difusa. Dirección Genral de la

Vivienda, Arquitectura y Urbanismo. Rugg, R.D., 1995. Defining Standard Features for Land Use Applications. Cartography and Geographic

Information Science, 22(3), 195–204. Sachs, E., Contact, C. & Modified, L., 2006. Getting started with Protégé-frames. Standford University. Sanchirico, J.N. & Wilen, J.E., 1999. Bioeconomics of spatial exploitation in a patchy environment.

Journal of Environmental Economics and Management, 37(2), 129–150. Santorini, B. & Kroch, A., 2000. The syntax of natural language: An online introduction using the

Trees program. University of Pennsylvania.(http://www. ling. upenn. edu/\ beatrice/syntax-textbook).

Sowa, J.F., 2000. Knowledge representation: logical, philosophical, and computational foundations,

MIT Press. Stanlake, G.F. & Grant, S.J., 1994. Introductory economics, Longman. Stiny, G., 1981. A note on the description of designs. Environment and Planning B, 8(3), 257–267. Stiny, G., 1980. Kindergarten grammars: designing with Froebel's building gifts. Environment and

Planning B, 7(4), 409-462. Stiny, G. & Gips, J., 1972. Shape grammars and the generative specification of painting and sculpture.

Information processing, 71, 1460–1465. Sugita, Y. & Tani, J., 2005. Learning semantic combinatoriality from the interaction between linguistic

and behavioral processes. Adaptive Behavior, 13(1), 33. Terrados, J., Almonacid, G. & Hontoria, L., 2007. Regional energy planning through SWOT analysis

and strategic planning tools. Impact on renewables development. Renewable and Sustainable Energy Reviews, 11(6), 1275–1287.

Thomson, D.S. et al., 2003. Managing value and quality in design. Trento, A.L., 2009. Participative Technologies: an Internet-based Environment to Access a Plural

Design Experience: Knowledge Modeling to Support User’s Requirements Formalization. Available at: http://cumincad.scix.net/cgi-bin/works/Show?_id=ecaade2009_186&sort=DEFAULT&search=trento&hits=5 [Accessed December 15, 2009].


144

Ulrich, K.T. & Eppinger, S.D., 2003. Product design and development, Irwin Professional Pub. Vazirgiannis, M. & Wolfson, O., 2001. A spatiotemporal model and language for moving objects on

road networks. Lecture Notes in Computer Science, 20–35. Wastiels, L. et al., 2007. Material knowledge for Design: The architect’s vocabulary. In Emerging

trends in Design Research, International Association of Societies of Design Research (IASDR) Conference, Hong Kong, Hong Kong.

Yule, G., 2006. The study of language, Cambridge Univ Pr.


145

Annexes


146

Annex 1 A rule case-study edited by the Protégé rules editor Distance between buildings (Trento 2009)

“Using an existing ontology and rules editor (Protegé2000 + PAL Constraints), authors

implemented a design rule which states that each single family house (denominated

“building”) must not be closer than 15 meters to another building (Figure). (...) By means of the

purposed Knowledge Modelling level, this rule can be linked to the building entities involved in

the design process and formalized in order to support the designers with some inferred

suggestions. (...) The Goals and Constraints editing, through the described mechanism, allow

the coherence of the design to be verified vis-à-vis the objective sets. The research in progress

is revealing the potential of the approach adopted for the preliminary design phase

representing a first-step validation of the illustrated software system implementation.”

9. Design rule formalization on Protégé Axiom Language

Annex 2


147

An example of an ontological class edition and its exported XML format

Protégé ontologies can be exported into a XML Schema. In the bottom is shown the class

hierarchy of climatic patterns ontology. At its bottom is shown the the XLM related Shema.

Ontology Class Hierarchy

10. Class edition on Protégé 2000

Ontology XML Schema

<?xml version="1.0" ?>

- <knowledge_base xmlns="http://protege.stanford.edu/xml" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://protege.stanford.edu/xml http://protege.stanford.edu/xml/schema/protege.xsd">

- <class> <name>:THING</name> <type>:STANDARD-CLASS</type> - <own_slot_value> <slot_reference>:ROLE</slot_reference> <value value_type="string">Abstract</value>

</own_slot_value> </class>

- <class> <name>:STANDARD-CLASS</name> <type>:STANDARD-CLASS</type> - <own_slot_value> <slot_reference>:ROLE</slot_reference> <value value_type="string">Concrete</value>

</own_slot_value> <superclass>:CLASS</superclass> <template_slot>:ROLE</template_slot> <template_slot>:DOCUMENTATION</template_slot>


148

<template_slot>:SLOT-CONSTRAINTS</template_slot> <template_slot>:ROLE</template_slot> <template_slot>:DOCUMENTATION</template_slot> <template_slot>:SLOT-CONSTRAINTS</template_slot>

</class> - <class> <name>:STANDARD-SLOT</name> <type>:STANDARD-CLASS</type> - <own_slot_value> <slot_reference>:ROLE</slot_reference> <value value_type="string">Concrete</value>

</own_slot_value> <superclass>:SLOT</superclass> <template_slot>:DOCUMENTATION</template_slot> <template_slot>:SLOT-CONSTRAINTS</template_slot> <template_slot>:SLOT-MAXIMUM-CARDINALITY</template_slot> <template_slot>:SLOT-MINIMUM-CARDINALITY</template_slot> <template_slot>:SLOT-NUMERIC-MAXIMUM</template_slot> <template_slot>:SLOT-NUMERIC-MINIMUM</template_slot> <template_slot>:SLOT-INVERSE</template_slot> <template_slot>:SLOT-DEFAULTS</template_slot> <template_slot>:SLOT-VALUES</template_slot> <template_slot>:ASSOCIATED-FACET</template_slot> <template_slot>:DIRECT-SUBSLOTS</template_slot> <template_slot>:DIRECT-SUPERSLOTS</template_slot> <template_slot>:DOCUMENTATION</template_slot> <template_slot>:SLOT-CONSTRAINTS</template_slot> <template_slot>:SLOT-MAXIMUM-CARDINALITY</template_slot> <template_slot>:SLOT-MINIMUM-CARDINALITY</template_slot> <template_slot>:SLOT-NUMERIC-MAXIMUM</template_slot> <template_slot>:SLOT-NUMERIC-MINIMUM</template_slot> <template_slot>:SLOT-INVERSE</template_slot> <template_slot>:SLOT-DEFAULTS</template_slot> <template_slot>:SLOT-VALUES</template_slot> <template_slot>:ASSOCIATED-FACET</template_slot> <template_slot>:DIRECT-SUBSLOTS</template_slot> <template_slot>:DIRECT-SUPERSLOTS</template_slot>

</class> - <class> <name>Cold Region</name> <type>:STANDARD-CLASS</type> - <own_slot_value> <slot_reference>:ROLE</slot_reference> <value value_type="string">Concrete</value>

</own_slot_value> <superclass>:THING</superclass>

</class> - <class> <name>Election of the location</name> <type>:STANDARD-CLASS</type> - <own_slot_value> <slot_reference>:ROLE</slot_reference> <value value_type="string">Concrete</value>

</own_slot_value> <superclass>Cold Region</superclass>

</class> - <class> <name>Slopes</name> <type>:STANDARD-CLASS</type> - <own_slot_value> <slot_reference>:ROLE</slot_reference> <value value_type="string">Abstract</value>

</own_slot_value> <superclass>Election of the location</superclass>

</class>


149

- <class> <name>Urban structure</name> <type>:STANDARD-CLASS</type> - <own_slot_value> <slot_reference>:ROLE</slot_reference> <value value_type="string">Concrete</value>


</class> - <class> <name>Public Spaces</name> <type>:STANDARD-CLASS</type> - <own_slot_value> <slot_reference>:ROLE</slot_reference> <value value_type="string">Concrete</value>


</class> - <class> <name>Landscape</name> <type>:STANDARD-CLASS</type> - <own_slot_value> <slot_reference>:ROLE</slot_reference> <value value_type="string">Concrete</value>


</class> - <class> <name>Vegetation</name> <type>:STANDARD-CLASS</type> - <own_slot_value> <slot_reference>:ROLE</slot_reference> <value value_type="string">Concrete</value>


</class> - <class> <name>Perish vegetation</name> <type>:STANDARD-CLASS</type> - <own_slot_value> <slot_reference>:ROLE</slot_reference> <value value_type="string">Abstract</value>

</own_slot_value> <superclass>Vegetation</superclass>

</class> - <class> <name>Persistent vegetation</name> <type>:STANDARD-CLASS</type> - <own_slot_value> <slot_reference>:ROLE</slot_reference> <value value_type="string">Abstract</value>

</own_slot_value> <superclass>Vegetation</superclass>

</class> - <class> <name>House Type</name> <type>:STANDARD-CLASS</type> - <own_slot_value> <slot_reference>:ROLE</slot_reference> <value value_type="string">Concrete</value>


</class> - <class> <name>General distribution</name>


150

<type>:STANDARD-CLASS</type> - <own_slot_value> <slot_reference>:ROLE</slot_reference> <value value_type="string">Concrete</value>


</class> - <class> <name>Distribution Plan</name> <type>:STANDARD-CLASS</type> - <own_slot_value> <slot_reference>:ROLE</slot_reference> <value value_type="string">Concrete</value>


</class> - <class> <name>Form and volume</name> <type>:STANDARD-CLASS</type> - <own_slot_value> <slot_reference>:ROLE</slot_reference> <value value_type="string">Concrete</value>

</own_slot_value> <superclass>Cold Region</superclass> <template_slot>Bioclimatic Pattern_Slot_0</template_slot>

</class> - <class> <name>Orientation</name> <type>:STANDARD-CLASS</type> - <own_slot_value> <slot_reference>:ROLE</slot_reference> <value value_type="string">Concrete</value>


</class> - <class> <name>Color</name> <type>:STANDARD-CLASS</type> - <own_slot_value> <slot_reference>:ROLE</slot_reference> <value value_type="string">Concrete</value>


</class> - <slot> <name>Bioclimatic Pattern_Slot_0</name> <type>:STANDARD-SLOT</type> - <own_slot_value> <slot_reference>:SLOT-MAXIMUM-CARDINALITY</slot_reference> <value value_type="integer">1</value>

</own_slot_value> - <own_slot_value> <slot_reference>:SLOT-VALUE-TYPE</slot_reference> <value value_type="string">String</value>

</own_slot_value> </slot>

- <slot> <name>:ASSOCIATED-FACET</name> <type>:STANDARD-SLOT</type> - <own_slot_value> <slot_reference>:SLOT-MAXIMUM-CARDINALITY</slot_reference> <value value_type="integer">1</value>

</own_slot_value> - <own_slot_value> <slot_reference>:SLOT-INVERSE</slot_reference>


151

<value value_type="slot">:ASSOCIATED-SLOT</value> </own_slot_value>

- <own_slot_value> <slot_reference>:SLOT-VALUE-TYPE</slot_reference> <value value_type="string">Instance</value> <value value_type="class">:FACET</value>


- <slot> <name>:DIRECT-SUBSLOTS</name> <type>:STANDARD-SLOT</type> - <own_slot_value> <slot_reference>:SLOT-INVERSE</slot_reference> <value value_type="slot">:DIRECT-SUPERSLOTS</value>

</own_slot_value> - <own_slot_value> <slot_reference>:SLOT-VALUE-TYPE</slot_reference> <value value_type="string">Instance</value> <value value_type="class">:SLOT</value>


- <slot> <name>:DIRECT-SUPERSLOTS</name> <type>:STANDARD-SLOT</type> - <own_slot_value> <slot_reference>:SLOT-INVERSE</slot_reference> <value value_type="slot">:DIRECT-SUBSLOTS</value>



- <slot> <name>:DOCUMENTATION</name> <type>:STANDARD-SLOT</type> - <own_slot_value> <slot_reference>:ASSOCIATED-FACET</slot_reference> <value value_type="facet">:DOCUMENTATION-IN-FRAME</value>

</own_slot_value> - <own_slot_value> <slot_reference>:SLOT-VALUE-TYPE</slot_reference> <value value_type="string">String</value>


- <slot> <name>:ROLE</name> <type>:STANDARD-SLOT</type> - <own_slot_value> <slot_reference>:SLOT-MAXIMUM-CARDINALITY</slot_reference> <value value_type="integer">1</value>

</own_slot_value> - <own_slot_value> <slot_reference>:SLOT-DEFAULTS</slot_reference> <value value_type="string">Concrete</value>

</own_slot_value> - <own_slot_value> <slot_reference>:SLOT-VALUE-TYPE</slot_reference> <value value_type="string">Symbol</value> <value value_type="string">Abstract</value> <value value_type="string">Concrete</value>


- <slot>


152

<name>:SLOT-CONSTRAINTS</name> <type>:STANDARD-SLOT</type> - <own_slot_value> <slot_reference>:ASSOCIATED-FACET</slot_reference> <value value_type="facet">:CONSTRAINTS</value>

</own_slot_value> - <own_slot_value> <slot_reference>:SLOT-VALUE-TYPE</slot_reference> <value value_type="string">Instance</value> <value value_type="class">:CONSTRAINT</value>


- <slot> <name>:SLOT-DEFAULTS</name> <type>:STANDARD-SLOT</type> - <own_slot_value> <slot_reference>:ASSOCIATED-FACET</slot_reference> <value value_type="facet">:DEFAULTS</value>


- <slot> <name>:SLOT-INVERSE</name> <type>:STANDARD-SLOT</type> - <own_slot_value> <slot_reference>:SLOT-MAXIMUM-CARDINALITY</slot_reference> <value value_type="integer">1</value>

</own_slot_value> - <own_slot_value> <slot_reference>:SLOT-INVERSE</slot_reference> <value value_type="slot">:SLOT-INVERSE</value>



- <slot> <name>:SLOT-MAXIMUM-CARDINALITY</name> <type>:STANDARD-SLOT</type> - <own_slot_value> <slot_reference>:SLOT-MAXIMUM-CARDINALITY</slot_reference> <value value_type="integer">1</value>

</own_slot_value> - <own_slot_value> <slot_reference>:SLOT-DEFAULTS</slot_reference> <value value_type="integer">1</value>

</own_slot_value> - <own_slot_value> <slot_reference>:ASSOCIATED-FACET</slot_reference> <value value_type="facet">:MAXIMUM-CARDINALITY</value>

</own_slot_value> - <own_slot_value> <slot_reference>:SLOT-VALUE-TYPE</slot_reference> <value value_type="string">Integer</value>


- <slot> <name>:SLOT-MINIMUM-CARDINALITY</name> <type>:STANDARD-SLOT</type> - <own_slot_value> <slot_reference>:SLOT-MAXIMUM-CARDINALITY</slot_reference> <value value_type="integer">1</value>

</own_slot_value> - <own_slot_value>


153

<slot_reference>:ASSOCIATED-FACET</slot_reference> <value value_type="facet">:MINIMUM-CARDINALITY</value>

</own_slot_value> - <own_slot_value> <slot_reference>:SLOT-VALUE-TYPE</slot_reference> <value value_type="string">Integer</value>


- <slot> <name>:SLOT-NUMERIC-MAXIMUM</name> <type>:STANDARD-SLOT</type> - <own_slot_value> <slot_reference>:SLOT-MAXIMUM-CARDINALITY</slot_reference> <value value_type="integer">1</value>

</own_slot_value> - <own_slot_value> <slot_reference>:ASSOCIATED-FACET</slot_reference> <value value_type="facet">:NUMERIC-MAXIMUM</value>

</own_slot_value> - <own_slot_value> <slot_reference>:SLOT-VALUE-TYPE</slot_reference> <value value_type="string">Float</value>


- <slot> <name>:SLOT-NUMERIC-MINIMUM</name> <type>:STANDARD-SLOT</type> - <own_slot_value> <slot_reference>:SLOT-MAXIMUM-CARDINALITY</slot_reference> <value value_type="integer">1</value>

</own_slot_value> - <own_slot_value> <slot_reference>:ASSOCIATED-FACET</slot_reference> <value value_type="facet">:NUMERIC-MINIMUM</value>

</own_slot_value> - <own_slot_value> <slot_reference>:SLOT-VALUE-TYPE</slot_reference> <value value_type="string">Float</value>


- <slot> <name>:SLOT-VALUES</name> <type>:STANDARD-SLOT</type> - <own_slot_value> <slot_reference>:ASSOCIATED-FACET</slot_reference> <value value_type="facet">:VALUES</value>

</own_slot_value> </slot> </knowledge_base>


154

Annex 3

PEST Analysis Template

Urban plan analyzed: ___________________________________________________________________

PEST analysis (political, economical, social, and technological) assesses a standpoint of a particular plan’s

proposition.

criteria examples

a) Ecological/environmental

current legislation b) Future legislation c) International legislation d) Regulatory bodies and

processes e) Government policies f) Government term and

change g) Urban codes h) Initiatives i) Home pressure- groups j) International pressure-

groups k) Conflicts

political economical criteria examples

a) Home economy b) Economy trends c) Overseas related

economies d) General taxation e) Seasonality issues f) Market cycles g) Specific industry factors h) Interest

criteria examples

a) Lifestyle trends b) Demographics c) Social attitudes and

opinions d) Media views e) Law changes affecting

social factors f) Brand, company,

technology image g) Fashion and role models h) Major events and

influences i) Buying access j) Ethnic/religious factors k) Ethical issues

social technological criteria examples

a) Competing technology

development b) Research c) Associated/dependent

technologies d) Replacement

technology/solutions e) Maturity of technology f) Information and

communications g) Technology legislation h) Innovation potential i) Technology access,

licencing j) Intellectual property issues k) Global communications

11. PEST analysis

Note: PEST analysis can be useful before SWOT analysis because PEST helps to identify SWOT factors. PEST and

SWOT are two different perspectives but can contain common factors. SWOT stands for strengths, weaknesses,

opportunities, threats.


155

SWOT Analysis Template

Urban plan analyzed: ___________________________________________________________________

This SWOT example is for a new urban plan. Many criteria can apply to more than one quadrant. Identify criteria

appropriate to each specific SWOT situation.

criteria examples

Advantages of proposition? Capabilities? Competitive advantages? Resources, Assets? Knowledge, data? Innovative aspects? Location and geographical? Value, quality? Processes, systems, IT? Cultural, attitudinal, behavioural? Management? Philosophy and values?

strengths weaknesses criteria examples

Disadvantages of proposition? Gaps in capabilities? Lack of competitive strength? Reputation, presence and reach? Financials? Own known vulnerabilities? Timescales, deadlines and pressures? Continuity, supply chain robustness? Effects on core activities, distraction? Reliability of data, plan predictability? Processes and systems? Management?

criteria examples

Territory developments? Social cohesion? Industry and lifestyle trends? Technology development and innovation? Economic development? Environmental concerns? Global influences? Competitors' vulnerabilities? Tactics: e.g., surprise? Information and research? Partnerships? Seasonal, weather, fashion influences?

opportunities threats criteria examples

Social unsteadiness? Political frame? Legislative effects? Environmental effects? Competitor intentions? IT developments? Market demand? New technologies, services, ideas? Obstacles faced? Insurmountable weaknesses? Sustainable financial backing? Economy? Seasonality, weather effects?

12. PEST analysis

Initially develop by Alan Chapman 2005-09 (www.businessballs.com/swotanalysisfreetemplate.htm)

http://www.businessballs.com/swotanalysisfreetemplate.htm


156

Annex 4

Regulations and contextual data attributes, proposed codes, types, reference values, data sources, and related calculations (Gil 2009).

Table of Building related attributes

Attributes Codes Types Refs Sources Calculations

Number of inhabitants HABN Socio-economic

1 Census/Plan integer

Gross Floor Area GFA Socio-economic

5 Composite BA * F

Built-up area BA General 1,5 Geometry m2

Number of Floors F General 1,5 Survey/Plan integer

Dwellings area DWEA Socio-economic

7 Survey/Plan m2

Number of Dwellings DWEN Socio-economic

1 Survey/Plan integer

Retail units area RETA Socio-economic

7 Survey/Plan m2

Number of Retail units RETN Socio-economic

4,7 Survey/Plan integer

Construction date COND Cultural 3,4 Census year

Population Ethnic origin POPE Cultural 4 Census % share

Population age POPA Socio-economic

4 Census % share

Population Socio-economic status POPS Socio-economic

4,6 Census % share

Exposure to prevailing winds WNDE Bioclimatic 2,3 Composite m2

Shaded area SHDA Bioclimatic 2 Composite m2

Solar Exposure SOLE Bioclimatic 2,3 Composite SOLO - DIR

Dwellings proximity DWEP Socio-economic

7 Composite SUM DWEN (or DWEA)

Number of parking spaces per dwelling

PRKD Socio-economic

1 Composite PRKN/DWEN

Retail units proximity RETP Socio-economic

7 Composite SUM RETN (or RETA)

Compactness COM Bioclimatic 2 Geometry A/LEN

Length LEN General 2,4,6 Geometry m

Proportion PROP Bioclimatic 2 Geometry LEN/W

Orientation DIR General 2,4 Geometry degrees

Exposure EXP General 2 Geometry integer

Width W General 1,2,4 Geometry m


157

State of Conservation CONS Cultural 3,4 Survey/Plan Attribute

Construction type CONT Cultural 3,4 Survey/Plan Attribute

Typology TYP General 2 Survey/Plan Attribute

Ground Floor use GFU Socio-economic

4,6,7 Survey/Plan %share

Upper floors use UFU Socio-economic

6,7 Survey/Plan %share

Surface quality SURF Bioclimatic 2 Survey/Plan Attribute

Solar Orientation SOLO Bioclimatic 2,3 Weather Data

N,S,E,W

Table of Plot related attributes


Green area GREA Bioclimatic 2 Attribute/Geometry m2

Common space area COMA Socio-economic 1,4 Attribute/Geometry m2

Private space area PRVA Socio-economic 1,4 Attribute/Geometry m2

Public space area PUBA Socio-economic 1,4 Attribute/Geometry m2


Dwellings density DWED Socio-economic 1 Composite DWEN/A

Floor Area Ratio or Floor Space Index

FAR or FSI

Socio-economic 5 Composite GFA / TA

Mean number of floors FAVG Socio-economic 1 Composite FSUM/BLDN

Mode number of floors FMOD Socio-economic 1 Composite Mode F

Ground Space Index GSI Socio-economic 1,5 Composite BA / TA

Layers L Socio-economic 5 Composite GFA/BA

Open Space Ratio OSR Socio-economic 5 Composite (TA - BA) / GFA

Retail units density RETD Socio-economic 7 Composite RETN/A

Exposure EXP General 2 Geometry integer


Area TA General 1,5 Geometry m2

Table of Block related attributes





Area TA General 1,5 Geometry m2


158

Floor Area Ratio or Floor Space Index

FAR or FSI

Socio-economic 5 Composite GFA / TA

Mean number of floors FAVG Socio-economic 1 Composite FSUM/BLDN

Mode number of floors FMOD Socio-economic 1 Composite Mode F

Ground Space Index GSI Socio-economic 1,5 Composite BA / TA

Layers L Socio-economic 5 Composite GFA/BA

Open Space Ratio OSR Socio-economic 5 Composite (TA - BA) / GFA

Number of Buildings BLDN Socio-economic 1 Geometry integer

Built-up area BA General 1,5 Geometry m2

Proportion PROP Bioclimatic 2 Geometry LEN/W

Solar Orientation SOLO Bioclimatic 2,3 Geometry N,S,E,W

Green area GREA Bioclimatic 2 Attribute/Geometry m2




Dwellings total RETT Socio-economic 6 Composite SUM DWEN (or DWEA)

Retail units total RETT Socio-economic 6 Composite SUM RETN (or RETA)

Dwellings density DWED Socio-economic 1 Composite DWEN/A

Retail units density RETD Socio-economic 7 Composite RETN/A

Number of inhabitants HABN Socio-economic 1 Census/Plan integer



Pavement width PAVW Socio-economic 1 Geometry m

Vegetation type VEG Bioclimatic 2 Survey/Plan %share

Number of parking spaces PRKN Mobility 1 Survey/Plan integer

Table of Street related attributes





Solar Orientation SOLO Bioclimatic 2,3 Weather Data N,S,E,W

Solar Exposure SOLE Bioclimatic 2,3 Composite SOLO - DIR

Number of Buildings BLDN Socio-economic 1 Geometry integer

Pavement width PAVW Socio-economic 1 Geometry m


159

Proportion PROP General 2 Geometry LEN/W

Pedestrian area PEDA Mobility 1 Attribute/Geometry m2




Vehicular area VEHA Mobility 1 Attribute/Geometry m2

Dwellings total RETT Socio-economic 6 Composite SUM DWEN (or DWEA)

Retail units total RETT Socio-economic 6 Composite SUM RETN (or RETA)

Number of parking spaces per dwelling

PRKD Socio-economic 1 Composite PRKN/DWEN

Slope S Bioclimatic 2,3 Geometry degrees

Global accessibility rank ACCG Mobility 4,6 Network Closeness

Local accessibility rank ACCL Mobility 6 Network Closeness

Global movement flow rank MOVG Mobility 4,6 Network Betweenness

Local movement flow rank MOVL Mobility 6 Network Betweenness

Dwellings proximity DWEP Socio-economic 7 Composite SUM DWEN (or DWEA)

Pedestrian surface share PEDR Socio-economic 1 Composite ?

Retail units proximity RETP Socio-economic 7 Composite SUM RETN (or RETA)



Viewshed area VWA General 3 Isovist m2

Viewshed compactness VWC General 3 Isovist L/A

Number of parking spaces PRKN Mobility 1 Survey/Plan integer

Surface quality SURF Bioclimatic 2 Survey/Plan Attribute

Vegetation type VEG Bioclimatic 2 Survey/Plan %share

13. Attributes applicable to urban formulation (Gil et al 2009)


160

Annex 4

The diagram shows the main classes of pre-design phase. The core ontology (Blocks, Focal Points,

Networks, and Zones) is located in the purple boxes of the diagram entities. Its shared super-

class is Design Core (light purple and dark green boxes).

34. Syntax class hierarchical tree (above left), exported piece of XML Schema (above centre), CPL diagram

classes, subclass-superclass hierarchy, and slots (middle right), CPL diagram zoom - the design core -

Networks, Zones, Blocks, and Landmarks (at the bottom).

Building a Pre-Design Ontology

Documents

Transcript of Building a Pre-Design Ontology