Modeling Urban Growth using the CaFe Modeling Shell

Mantelas A. Eleftherios

Regional Analysis Division Institute of Applied and Computational Mathematics

Foundation for Research and Technology - Hellas

An urban growth modeling shell to:

Explore and map the urban growth dynamics

Simulate Urban Expansion

Support Decision and Planning

CaFe

simple, open, with visible mechanisms

extracts and reproduces spatial patterns of change

retains a extendible/reducible knowledge base

combines various knowledge sources

expresses extracted knowledge in a comprehensible way

little data limitations

tranferable

Design

no pre-defined formulas or functions

it does not exclude/require certain input

calculates mean values of each variable’s conditional frequency distribution function

the extracted patterns are space sensitive

scale free

knowledge base in natural language

Exploring & Mapping

parallel connection of each variable and calculation of suitability indexes for urbanization

combines statistical, empirical and theoretical knowledge

allocation of an urban “amount”

the growth is an exogenous parameter

Simulation

alternative scenarios population

population of inverse optima scenarios

scenarios may be based upon : • input data • suitability indexes

Decision Support

Stand alone C code supporting:

information management through Fuzzy Logic

application of Cellular Automata Techniques

basic raster file managements

a GIS is necessary for data pre-processing and results’ visualization

Cellular Automata – Fuzzy Engine

explicit space

implicit time through terms of urban growth

variables are described as fuzzy sets

location is given by a 2D fuzzy variable

Information Management

knowledge in IF – THEN rules

each rule has a certainty factor

each certainty factor is spatially sensitive

suitability rules have simple hypotheses and are accumulated using the Dempster-Shaffer theory of evidence:

Knowledge Management

n

i 1i )CF -1 ( - 1CCF

Structure of CaFe

1. Calculation of suitability per variable and overall suitability

2. Iterative CA-based urban cover allocation

Case Study

the broader Mesogia area in east Attica

635 s.km

11+7

municipalities

> 100.000

population

Available Data: Corine land cover for 1994, 2000, 2004 road network for 1994, 2000, 2004 DEM

the 1994-2000 period was used for knowledge extraction and model calibration the 2000-2004 was used for model evaluation

Application

Evaluation

Error Indexes: Model Map

overestimation error 0,11 0,023

underestimation error 0,08 0,015

total error 0,19 0,039

total error for results with 0,05 0,009

Certainty >70%

Error Accumulation

Map Error

Model Error

Overestimation Underestimation Total

Overestimation Underestimation Total

Results

Results ΙΙ

Results ΙΙI

Fuzzy Logic and Cellular Automata consist an advisable framework to describe and simulate urban growth

CaFe is capable to simulate is a satisfactory way the short term urban growth using little data

CaFe’s output refers to housing activities rather than the whole of the artificial surface

Conclusions

stochastic KBE module

spatially sensitive Dempster-Shaffer operator

unbinding the over- and under-estimation errors

applications and further evaluation

Future Directions

Modeling Urban Growth using the CaFe Modeling Shell

Regional Analysis Division Institute of Applied and Computational MathematicsFoundation for Research and Technology - Hellas

CaFe: Cellular Automata – Fuzzy Engine

Mantelas A. Eleftherios

e-mail eamantel@iacm.forth.grtel. +30 2810 391736