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 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

Study Area and Problem Context

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

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

Land Cover 1985

Land Cover 1990

Land Cover 1995

Land Cover 2000

Land Cover 2003

Land Cover 2006 Conurbanisation down to south

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

Leapfrog Development Individual manufacturing firms Small scale or individual housing development

Population Trend

Urbanized Area Total Area: 727 km 2 ; Urbanized Area: 255 km 2 (35.14%), As of 2008

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

Modelling Urban Growth

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)

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!

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

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

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

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

Development Toolkit (2) NetLogoRepast

Visualizing Model Output and Reasoning Together

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

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

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

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

Display a series of KML data in time sequence Applicable to cell changes and agent movement

Future Works

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

Thank you !