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

2. Outlines Research Goal 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. Study Area and Problem Context

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

7. Study Area 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 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 ? Greenbelt

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

16. Population Trend

17. Urbanized Area Total Area: 727 km 2 ; Urbanized Area: 255 km 2 (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 Urban Growth

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

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 Logistic Regression Physical Variable (elevation, slope) Physical Variable (elevation, slope) Institutional Variable (regulation) Institutional Variable (regulation) Social Variable (ownership, accessibility, price) Social Variable (ownership, accessibility, price) Development Probability Surface

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

26. Development Toolkit (2) NetLogoRepast

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

33. Future Works

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

35. Thank you !