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Modeling Urban Growth using the CaFe Modeling Shell Mantelas A. Eleftherios Regional Analysis Division Institute of Applied and Computational Mathematics.

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Presentation on theme: "Modeling Urban Growth using the CaFe Modeling Shell Mantelas A. Eleftherios Regional Analysis Division Institute of Applied and Computational Mathematics."— Presentation transcript:

1 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

2 An urban growth modeling shell to: Explore and map the urban growth dynamics Simulate Urban Expansion Support Decision and Planning CaFe

3 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

4 no pre-defined formulas or functions it does not exclude/require certain input calculates mean values of each variables conditional frequency distribution function the extracted patterns are space sensitive scale free knowledge base in natural language Exploring & Mapping

5 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

6 alternative scenarios population population of inverse optima scenarios scenarios may be based upon : input data suitability indexes Decision Support

7 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

8 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

9 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

10 Structure of CaFe 1. Calculation of suitability per variable and overall suitability 2. Iterative CA-based urban cover allocation

11 Case Study the broader Mesogia area in east Attica 635 s.km 11+7 municipalities > population

12 Available Data: Corine land cover for 1994, 2000, 2004 road network for 1994, 2000, 2004 DEM the period was used for knowledge extraction and model calibration the was used for model evaluation Application

13 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%

14 Error Accumulation Map Error Model Error Overestimation Underestimation Total

15 Results

16 Results ΙΙ

17 Results ΙΙI

18 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 CaFes output refers to housing activities rather than the whole of the artificial surface Conclusions

19 stochastic KBE module spatially sensitive Dempster-Shaffer operator unbinding the over- and under-estimation errors applications and further evaluation Future Directions

20 Modeling Urban Growth using the CaFe Modeling Shell Regional Analysis Division Institute of Applied and Computational Mathematics Foundation for Research and Technology - Hellas CaFe: Cellular Automata – Fuzzy Engine Mantelas A. Eleftherios tel


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