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 MetropolitanArea

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

Legend

Study area

Barren

Argriculture

Forest

Water

Wet land

Built up

Grass land

Land Cover 1990

Legend

Study area

Barren

Argriculture

Forest

Water

Wet land

Built up

Grass land

Land Cover 1995

Legend

Study area

Barren

Argriculture

Forest

Water

Wet land

Built up

Grass land

Land Cover 2000

Legend

Study area

Barren

Argriculture

Forest

Water

Wet land

Built up

Grass land

Land Cover 2003

Legend

Study area

Barren

Argriculture

Forest

Water

Wet land

Built up

Grass land

Land Cover 2006

Legend

Study area

Barren

Argriculture

Forest

Water

Wet land

Built up

Grass land

Conurbanisationdown 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 km2; Urbanized Area: 255 km2 (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 ProblemContext

Data Analysis(Explanatory/Descriptive)

Simulation

Time matching, Parameter setting

Output Analysis

Calibration, Verification, Validation

Implementation

Toolkit: NetLogo, Repast

Conceptual model

EnvironmentDecision

ruleAgent

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

PhysicalVariable

(elevation, slope)

PhysicalVariable

(elevation, slope)

InstitutionalVariable

(regulation)

InstitutionalVariable

(regulation)

SocialVariable

(ownership, accessibility,

price)

SocialVariable

(ownership, accessibility,

price)

Development Probability Surface

Development Toolkit (1)

Functionality

Programming difficulty

NetLogo

Mason

Repast J/Phyton/.Net

Swarm

Development Toolkit (2)

• NetLogo • Repast

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

Dynamic KML

http://puff.images.alaska.edu/dynamic_kml.shtml

• 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 !