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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
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An urban growth modeling shell to:
Explore and map the urban growth dynamics
Simulate Urban Expansion
Support Decision and Planning
CaFe
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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
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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
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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
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alternative scenarios population
population of inverse optima scenarios
scenarios may be based upon : • input data • suitability indexes
Decision Support
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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
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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
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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
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Structure of CaFe
1. Calculation of suitability per variable and overall suitability
2. Iterative CA-based urban cover allocation
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Case Study
the broader Mesogia area in east Attica
635 s.km
11+7
municipalities
> 100.000
population
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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
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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%
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Error Accumulation
Map Error
Model Error
Overestimation Underestimation Total
Overestimation Underestimation Total
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Results
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Results ΙΙ
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Results ΙΙI
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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
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stochastic KBE module
spatially sensitive Dempster-Shaffer operator
unbinding the over- and under-estimation errors
applications and further evaluation
Future Directions
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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 [email protected]. +30 2810 391736