Modelling urban dynamics

Modelling urban dynamics Moving cities Presented by Rui Lima

Angers, France, February 27th 2013

Human actions are altering the terrestrial environment at unprecedented rates, magnitudes, and spatial scales. Land-cover change stemming from human land uses represents a major source and a major element of global environmental change.

The world population is increasing day by day, this increases the demand for food and housing.

The cities nowadays are growing to points where before were agricultural areas, agricultural areas are moving to forest areas, the space will end at some point. Avoiding the collapse starts with good planning.

To help planning one must be able to predict. Our crystal balls are complex computer models.

Introduction

Land use & Land cover changes

Land use, Land Cover Changes Land cover: attributes of the Earth’s land

surface and immediate subsurface, including biota, soil, topography, surface and groundwater, and human (mainly built-up) Structures

Land cover conversions: replacement of one cover type by another and are measured by a shift from one land-cover category to another, as is the case of agricultural expansion, deforestation, or change in urban extent.

[Cabral 2012]

Land use, Land Cover Changes Land cover and changes are visible in

remotely-sensed data or by generating evidence from secondary statistics, such as (agricultural) census data

Land-use as well as land-management information, in contrast, is mainly gained through detailed ground-based analysis, although land use can be inferred in remotely-sensed data under certain circumstances

[Cabral 2012]

The world changesMain tropical deforestation fronts in the 1980s and 1990s. The map is based on three data sets: (a) the deforestation hotspots in the humid tropics of

the Tropical Ecosystem Environment Observation by Satellite (TREES) project,

(b) a time series analysis of tree cover based on 8-km resolution data from the National Oceanic and Atmospheric Administration’s advanced very high resolution radiometer (AVHRR, and

(c) the Amazon Basin deforestation maps derived from time series of Landsat Thematic Mapper (LandsatTM) data.

These maps were overlaid and combined to identify areas where high rates of deforestation were measured by several of the datasets. Green areas are intact forests. The map indicates the number of times each 0.18grid was identified as being affected by rapid deforestation by the different datasets (orange, pixels detected as hotspot by one dataset, red, pixels detected as hotspot by two datasets, black, pixels detected as hotspot by three datasets)

[Lambin 2003]

The world changes

[Lambin 2003]

Legend of previous slide’s picture:

Population density in 1995 and most populated and changing cities from 1990 to 2000. The map is based on the 2001 revision of the “World Urbanization Prospects”, which provides population estimates in cities of more than 750,000 inhabitants for the years 1990 and 2000, and the “gridded population of the world”, which provides population estimates in 1995. The first dataset focuses on megacities whereas the second includes less populated areas. Green circles represent the most changing cities between 1990 and 2000, blue circles the most populated cities in 2000, and red circles the most changing and populated cities. The background color scale represents the population densities in 1995 (from less than five inhabitants in gray to more than 1750 inhabitants/km 2in dark orange)

The world changes

[Lambin 2003]

Recent changes in Urbanization

“In 2000, towns and cities housed more than 2.9 bilion people, nearly half of the world population.”

[Lambin & Geist 2006]

“Urban population has been growing more rapidly than rural population worldwide”

“the number of megacities, defined here as cities with more than 10 million inhabitants, has changed from one in 1950 (New York) to 17 in 2000, the majority of which are in developing countries”

“ It is estimated that 1 to 2 million ha of cropland are being takenout of production every year in developing countries to meet the land demand for housing, industry, infrastructure, and recreation ”

How about near you???? Did you know that...

The world changesGuia de Portugal in 1924:« Finalmente, no extremo N. dessa longa artéria, como remate da cidade moderna, abre-se o diadema da Rotunda (Praça Marquês de Pombal), de pavimento em empedrado lisboeta, com letreiros alusivos à acção reformadora do estadista, plantada de acácias do Japão (Sophora japonica), e de onde a vista se enfia através da Avenida e as ruas da Baixa até morrer nos montes azuis da outra banda. De aí irradiam em diferentes direcções uma série de avenidas: a de Brancaamp, que vai dar à praça do Brasil [Largo do Rato]; a de Fontes Pereira de Melo que comunica com a praça do Duque de Saldanha, servindo de ligação aos modernos bairros conhecidos por Avenidas Novas; e a do Duque de Loulé, que liga directamente a Rotunda com o largo do Matadouro, servindo de limite N. ao bairro de Camões.»

Praça Marquês de Pombal, Lisbon, 192...Source: unkown

The world changes

Praça Marquês de Pombal, Lisbon, 195...Photo by: António Passaporte, in Postais de Lisboa, [Lisbon], C.M.L., [1998].

http://arquivomunicipal.cm-lisboa.pt/default.asp?s=12082&l=5&u=8&cat=5

The world changes

Praça Marquês de Pombal, Lisbon, 196... 197...Source: unkown

The world changes

Praça Marquês de Pombal, Lisbon, nowadays...in Bing maps bird’s eye view

Rome was not built in a day.

Europeans still travel on Roman roads.

Land use changes reversibility

Planning for the best use of land and its resources should take fully into consideration the long-term consequences of each type of use in order to stretch out most beneficially the well-being of society in the future, and to protect the integrity of the land and its biota.

Reversible land-use: leaves the land, after use, essentially as it was before; little or no man-induced modification remains.

Terminal land-use: commits the land to a chosen particular use, and any attempt at reversal requires either time-scales that are long compared with the expected lifespan of the social and political institution, or a commitment of resources that is too high for society to consider worth bearing. Examples of terminal land-use are location of metropolises and sites of toxic and/or radioactive waste disposals; by its nature the list grows monotonically.

In between these two extremes of reversible and terminal land-use, the bulk of land-use is sequential, in which each use of land changes its potentials and configurations, and these changes are mainly irreversible.

Major transport investments tend to be the most durable and also involve the longest time lags between planning and completion.

The same is true for the appropriation of open space for human settlements.

The common feature of these changes is their virtual irreversibility.

Land use changes reversibility

Provision and lack of food, feed, fiber and timber

Disease risk and human health Atmospheric Chemistry, Climate Regulation

and life support functions Agro diversity and biodiversity loss Soil quality Fresh water hydrology, agricultural water

use and coastal zones

Impacts of land use and cover change

[Lambin & Geist 2006]

Growing cities

Models for predicting the growth

Why do cities grow?! Several factors can be used to explain a land use change in

general:◦ Biophysical factors

Climate, relief, hydrology, and vegetation ...

◦ Economic and technological factors input and output prices, taxes, subsidies, production and transportation

costs, capital flows and investments, credit access, trade …

◦ Demographic factors population composition and distribution, namely changes in urbanization

and in household size acknowledge the importance of indirect or consumptive demands on the land by an increasingly urbanized population

◦ Institutional factors Institutions (political, legal, economic, and traditional) and their

interactions with individual decision-making. In particular, government policy plays a ubiquitous role in land change, either directly causative or in mediating fashion

[Cabral 2012]

Predicting the growth...

“ A model is a an abstraction of an object, system or process that permits knowledge to be gained about reality by conducting experiments on the model ”

[Clarke 2003]

“Modeling is essential for the analysis, and especially for the prediction, of the dynamics of urban growth.

Yet the successful application of a model in one particular geographical area does not necessarily imply its successful use in another setting where local characteristics, territorial constraints and the classic site and situation properties of economic geography ensure that different development paths have been followed.”

Predicting the growth...

[Silva 2001]

In the last decades different modeling techniques have been developed for better understanding and predicting urban expansion, such as Cellular Automata (CA).

CA-based models have been commonly used in exploring various urban phenomena, such as urbanization, urban form change, urban growth effect, etc.

CA is individual-based models designed to simulate systems in which states, time, and space are discrete.

Cellular Automata Modelling

[MadhanMohan 2012]

Simply stated, an automaton (plural: automata) is a self-operating machine.

CA is a discrete dynamical system that is composed of an array of cells, each of which behaves like a finite-state automaton.


The data requirements for parameterization, calibration and validation of urban models are intense due to the complexity of the models and their objectives.

A simple and well-known example of a cellular automata is John Conway's Game of Life.


These models are not concern on the dynamics of multiple categories of urban land that leads to simulation errors.

To overcome this drawback we use Agent Based Models (ABM). It is an effective and process based model which is used for modeling the real world applications like urban growth.


[MadhanMohan 2012]

SLEUTH◦ Stands for the input needs for driving the model:

Slope Land cover Exclusion Urbanization Transportation Hillshade

Uses historical geospatial data for calibration of its parameters, when running SLEUTH the rules of the CAs are calibrated to historical urban spatial data.

Actually SLEUTH incorporates two different models such as Clarke Urban Growth Model (UGM) and Deltatron Land Use/Land Cover Model (DLM).


SLEUTH – Advantages◦ SLEUTH is a self-organizing CA model and, therefore, the

coefficients that control growth may vary according to numerous factors.

◦ Due to its independent scalability, transportability, and transparency which has become a popular tool in modeling, the increase of urban extent over time and recreating the past or forecasting growth into the future.

SLEUTH – Drawbacks◦ The simulations performed with SLEUTH underestimate the emergence

of urban. ◦ SLEUTH produces some of the errors, because this model does not

consider the dynamics of multiple categories of urban land. ◦ The number of patches generated by SLEUTH is much lower and

patches are larger and more clustered. This model produces an excess underestimation of new growth and an overestimation of infill growth.


FCAUGM◦ Stands for Fuzzy Cellular Automata Urban Growth

Model◦ Fuzzy logic combined to CA provides a proper

framework for expressing and mapping the urban growth dynamics.

◦ FCAUGM is generally capable of simulating and predicting the complexities of urban growth.


FCAUGM - Advantages◦ The fuzzy logic gives the evaluation closer to the

complex reality of regional planning. ◦ Assignment of the weights to the different indicators can

be taken into consideration in an environmental impact using fuzzy logic, in order to obtain a significant homogeneity and objectivity.

FCAUGM – Drawbacks◦ Direct employment of fuzzy logic lies in the way

knowledge is captured, i.e. by employing man-made rules.

◦ The construction of a manual, expertly guided rule-base is a complex task due to the presence of a high number of inter-dependent variables.


MOLAND◦ The MOLAND model represents processes at three spatial levels: Global, Regional

and Local. Greater Dublin Area example.


[Lavalle 2004]

The macro-model consists of 4 strongly linked sub-models representing the Economic, Demographic, Land use and Transportation sub-systems. ◦ The economic sectors are aggregated into four main categories:

Industry, Services, Commerce, and Port activities

The population is assigned to four residential categories:◦ Residential continuous dense,◦ Residential continuous medium,◦ Residential discontinuous urban,◦ Residential discontinuous sparse


Cellular Automata ModellingThe MOLAND macro-model consists of 4 sub-systems:

Economy, Demography, Land use and Transportation

[Lavalle 2004]

MOLAND – Advantages◦ MOLAND is the model that comes nearest to real

percentages of each type of growth and it has produced growth patterns closer to the real ones than the other models.

◦ Indeed, it produced more realistic urban patterns near the road network than the model of White et al. since it considered various types of roads ( consequently, various coefficients) in the calculation of the effects of road type on the transition potential, instead of a single type of road and, consequently, a single coefficient.


[MadhanMohan 2012]

MOLAND – Drawbacks◦ The cell-to-cell correspondence between the real

and the map simulated using MOLAND is low. Yet, establishing the correct location of each simulated land use cell is very difficult because of path dependence and stochastic uncertainty.


[MadhanMohan 2012]

There are some common limitations for all CA-based models:◦ They focused on the reproduction of spatial patterns

and this is the obscurity to model socioeconomic dynamics and decision making processes regarding land use.

◦ None of these models consider the dynamics of multiple categories of urban land and they may produce simulation errors such as those found in the visual inspection of results, caused by the lack of differentiation between a single-family detached house with a large garden and a group of single-family detached houses.

◦ These shortcomings are overcome by Agent Based Models (ABM), which are process based.


An agent-based model is a generalization of cellular automata in which agents are able to move around the space, rather than being confined to the cells of a raster.

Agent based modeling refers to a modeling concept which is closely linked to the modeling techniques of object orientation (OO).

Agent Based Modelling

What is an agent?


Agents are a representation and a simplification of complex (including human) behavior, this representation is established by defining rules used by the agents to pursue a goal (or goals).

Agents must be able to communicate among each other, dependent on the behavior-rules one applies in the model; also agents communicate with the simulated environment.



A first notion of an agent

[Huigen 2003]

An agent is ‘alive’ in its environment. An agent has direct interactions with its world.

The agent may act on the environment, which in turn provides perceptions to the agent.

The complexity of these interactions is primarily constructed in the agent, and not in the environment. An agent observes and interprets the world. The environment may change, but the agent will have to observe these changes.


Agent-based models are useful in conceptualizing land use changes and urban growth.

Each agent, in such models, acquires its momentum from factors like the configuration of the land use of its neighbors, the cost of living, cost of transportation, accessibility and other factors determining the quality of life.

The spatial environment in the model includes land use attributes (slope, land use, excluded, urban, transportation, hill shade), land price distribution, surrounding environment.

A spatial environment includes a virtual real estate market, social network, government policies and casualty problems


Components in an agent based LUCC model[Huigen 2003]

ABMS Software Packages ◦ AgentSheets ◦ AndroMeta ◦ AnyLogic ◦ Ascape ◦ Breve ◦ Cormas ◦ DEVS: Discrete Event System Specification ◦ EcoLab ◦ FLAME: FLexible Agent Modelling Environment ◦ GAMA: Gis& Agent-based Modelling Architecture◦ JAS: Java Agent Based Simulation Library ◦ LSD: Laboratory for Simulation Development ◦ MAML: Multi-Agent Modelling Language ◦ MATSim ◦ MASON: Multi-Agent Simulation of Neighbourhoods ◦ MASS: Multi-Agent Simulation Suite ◦ MetaABM ◦ MIMOSE ◦ MobiDyc: Modélisation Basée sur les Individus pour la Dynamique des Communautés ◦ Modelling4all ◦ NetLogo ◦ Open StarLogo ◦ RePast: Recursive Porous Agent Simulation Toolkit ◦ Repast Simphony ◦ SimPack ◦ SimPy ◦ SOARS: Spot Oriented Agent Role Simulator ◦ StarLogo ◦ SugarScape ◦ Swarm ◦ VisualBots ◦ Xholon ◦ ...


Cellular automata (CA）modelling is one of the recent advances in spatial–temporal modelling techniques in the field of urban growth dynamics. It is commonly used in exploring various urban phenomena, such as urbanization, urban form change, urban growth effect, etc.

The limitations of the existing CA models are overcome by using Agent based Modelling (ABM).

ABMs would certainly provide a more realistic representation of complex urban organization, as well as provide us the flexibility to vary urban quantities and population characteristics.

ABM can be used as an effective model for modeling the urban growth dynamics.

Conclusion

[Cabral 2012]Cabral P. (2012) - Land use and cover changes: general issues and modelling approaches - Erasmus Intensive Programm 2012 .

[Lambin 2003] Lambin E. et al. – Dynamics of land-use and land-cover, change in tropical regions.

[Lambin & Geist 2006] Lambin E. and Geist H. (Eds) (2006). Land-Use and Land-Cover Change: local Processes and Global Impacts. Springer

References

[Silva 2001] Silva, E.A & Clarke, K.C. 2001- Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal, in Computers, Environment and Urban Systems #26 (2002), pg. 525–552

[MadhanMohan 2012] MadhanMohan, S. et al. 2012 – Analysis of various urban growth models based on cellular automata, in [IJESAT] International Journal of Engineering Science & Advanced Techonology volume 2, issue 3, p.453-460

[Lavalle 2004] Lavalle, C. et al. 2004-The MOLAND model for urban and regional growth forecast - A tool for the definition of sustainable development paths, European Commission - Joint Research Centre

[Huigen 2003] Huigen, M. G.A. 2003 - Agent Based Modelling in Land Use and Land Cover Change Studies – Interim Report IR-03-044, International Institute for Applied Systems Analysis Schlossplatz 1 A-2361 Laxenburg, Austria

[Mathur 2007] Mathur, P. et al. 2007- Agent-based Modeling of Urban Phenomena in GIS, University of Pennsylvania

Erasmus Intensive Programme (IP)Agents-based modeling for processes and dynamics in landscape geography 17 February until 2 March 2013At AGROCAMPUS OUEST ANGERS - France

Modelling urban dynamics

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