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Urban Growth Simulation and Geospatial Web for Planning Support

Urban Growth Simulation and Geospatial Web for Planning Support. PhD Researcher, Dong Han Kim Centre for Advanced Spatial Analysis. Outlines. Research Goal Study Area and Problem Context Modelling Urban Growth Visualizing Model Output Future Works. Research Goal. Research Goal and Method.

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Urban Growth Simulation and Geospatial Web for Planning Support

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  1. Urban Growth Simulation and Geospatial Web for Planning Support PhD Researcher, Dong Han Kim Centre for Advanced Spatial Analysis

  2. Outlines • ResearchGoal • Study Area and Problem Context • Modelling Urban Growth • Visualizing Model Output • Future Works

  3. Research Goal

  4. Research Goal and Method • Developing a urban growth simulation model and disseminating it by geospatial Web technologies to support planning policy making • Explorative and descriptive • Literature review, modelling, and experimental case study

  5. StudyArea and Problem Context

  6. StudyArea South Korea Seoul Metropolitan Area Seoul Hwaseoung: Study area

  7. StudyArea Characteristics • Undeveloped rural area in Seoul Metropolitan Area until 2000 • Industrial and residential development began to occur afterward • One of the fastest urbanizing area in Korea • One of the most concerned area for sprawl

  8. Land Cover 1985

  9. Land Cover 1990

  10. Land Cover 1995

  11. Land Cover 2000

  12. Land Cover 2003

  13. Land Cover 2006 Conurbanisation down to south

  14. Suitability Analysis Greenbelt • Developable lands in SMA • Excluding physically and legally undevelopable land, significant amount of developable land are located in Hwaseoung • What is happening and what can happen in future ?

  15. Leapfrog Development • Individual manufacturing firms • Small scale or individual housing development

  16. Population Trend

  17. Urbanized Area Total Area: 727 km2; Urbanized Area: 255 km2 (35.14%), As of 2008

  18. Major Planning Problem/Agenda • Sprawl of small scale(but lots of) industrial and residential developments • Rapid loss of forest and agricultural land • Development oriented policy without long term vision and citizen consensus • Need for “centres” or “compact cores” for sustainable development

  19. Modelling UrbanGrowth

  20. Why Agent Based Modelling(ABM) ? • Dynamic • Driving force of urban growth : Spatial behaviour of individual agents • Bottom up approach can be joined with top down intervention • Possible “hot spots” during growth simulation (Emergence, Knowledge discovery)

  21. Overall Model Building Process Conceptual model Agent Decision rule Environment Understanding Problem Context Implementation Toolkit: NetLogo, Repast Data Analysis (Explanatory/Descriptive) Simulation Time matching, Parameter setting Feedback Output Analysis Calibration, Verification, Validation I am here now! Policy Evaluation

  22. Model Outline (1) • Simulating urban growth • Non-urban to residential use • Non-urban to service use • Non-urban to industrial use • To support planning policy by simulating future urban growth under different policy scenarios

  23. Model Outline (2) • Hybrid approach (Combination of ABM and CA : Cells state affected by not only neighbourhood characteristics but also agent behaviour) • Cell: 30m * 30m grid • Agent: Household, manufacturing industry, retail

  24. Model Outline (3) Agent Location Decision Development Probability Surface Logistic Regression Physical Variable(elevation, slope) Institutional Variable (regulation) Social Variable(ownership, accessibility, price)

  25. Development Toolkit (1) Functionality Mason Swarm Repast J/Phyton/.Net NetLogo Programming difficulty

  26. Development Toolkit (2) • Repast • NetLogo

  27. Visualizing Model Output and Reasoning Together

  28. Disseminating Model Outcomes • Knowledge, especially one about future, is of importance in making planning policy • However, contemporary planning not only relies on knowledge but also requires a broader consensus among stakeholders • Thus, sharing model outcome is a necessary step to support planning decision making and action

  29. Java Applet • Simple development • User interactions on model parameter • Inflexibility of data overlay

  30. WebGIS Server • Web 2.0, mesh up • Spatial analysis on Web • Requires heavy duty hardware

  31. Google Earth • Ease of use, wide availability • Mesh up (Spatially explicit communication) • OGC standard • Dynamic KML

  32. Dynamic KML • Display a series of KML data in time sequence • Applicable to cell changes and agent movement http://puff.images.alaska.edu/dynamic_kml.shtml

  33. Future Works

  34. Next Step • Conceptual model building • Agents behavior • Environment • Decision rules • Bridging model and planning policy • Storytelling ?

  35. Thank you !

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