urban growth modelling in jakarta metropolitan area: the research proposal

Case study: Jakarta Metropolitan Area, Indonesia

URBAN GROWTH MODELING

IN A DEVELOPING COUNTRY

AGUNG WAHYUDI

S 42817150

Supervisor: Dr. YAN LIU

OUTLINE

• Introduction

• Methodologies

• Management

Agung Wahyudi S42817150

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INTRODUCTION


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BACKGROUND• Urban area covers 5% of overall land in the world1.

• In 2030, it is expected 80% world population lives in urban areas2.

• Occurs largely in developing countries in Asia.

• Jakarta, Indonesia, is experiencing rapid urban growth.


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Jakarta, 70’s Jakarta, 90’s

STUDY AREA


• Covers 7500 km2

• Population: 28 million people (~10% of Indonesian)

• Comprises of 3 provincial governments

• Jakarta (the capital of Indonesia) as the economic core

• Uncertain future of urban growth

Land

sat,

US

GS

199

4. T

rue

colo

ur

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Agung Wahyudi S42817150 Wallpapersfor.me

RESEARCH GAP

Urban

systems

Proximity to

facilitiesHUMAN

Developed countries

Developing countries

Geomorphology

e.g. slope

Transportation

system

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AIM AND OBJECTIVES (1)


• AIM:

To develop an urban growth model by taking into accounts human

behaviour factors, emphasizing their interactions with other actors,

and the spatial pattern of urban growth towards aiding policy

makers in regulating the growth of urban areas using Jakarta

Metropolitan Area (JMA) as case study.

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AIM AND OBJECTIVES (2)


• OBJECTIVES:

1. To review and evaluate the selection of urban growth factors in cellular automata (CA) urban models.

2. To identify the spatio-temporal pattern of urban growths in JMA using Landsat imaging

3. To develop a prototype of ABM featuring three agents in a separated model

4. To evaluate the impact of policies on the urban spatial pattern by simulating three policy scenarios in three types of interrelated agent’s behaviours

5. To disseminate the results of urban ABM to improve the effectiveness of urban decision-making towards a well-coordinated management among municipalities in JMA

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METHODOLOGIES


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METHODOLOGIES

Objective 1

OBJECTIVE 1:


• Select articles from web of knowledge

• Extract and record the factors in CA urban models

• Analyse the time (year) and geographic location

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To review and evaluate the selection of urban growth

factors in cellular automata (CA) urban models.

1st STAGE

Search on “Cellular automata

AND urban*” from Web of

Knowledge (n=470)

2nd STAGE

Check relevance of study

– title & abstract

(n=165)

3rd STAGE

Manual assessment,

check introduction and

conclusion (n=107)

OBJECTIVE 1:


• Suggest key factors influencing urban growth for Objective 3

• Conference paper in CUPUM, The Netherlands, July 2013

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To review and evaluate the selection of urban growth

factors in cellular automata (CA) urban models.


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METHODOLOGIES

Objective 2

OBJECTIVE 2:


• Where the changes happen?

• How fast the changes occur?

• What are the main driving factors?

• Spatial characteristics of the

changes?

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To identify the spatio-temporal pattern of urban growths

in JMA using Landsat imaging

Jakarta, 70’s Jakarta, 80’s Jakarta, 90’s

OBJECTIVE 2:


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Landsat Year Resol

L1-2L4L5L8

1978198919942013

60x6030x3030x3030x30

Pre-analysis

Geometric correction Atmospheric calibration

Supervised classification

NDVI NDBI Band 5 and Band 3

Validation

Visual calibration on GCPs Confusion matrix

Secondary data Administrative boundary Land use map 2005 Google earth Previous reports on JMA

Descriptive analysis Histogram Land use proportion Percentage of changes

Spatial analysis

Urban cross-section maps Variogram on selected angle

OBJECTIVE 2:


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NDVI =NIR−red

NIR+red

NDBI = Bu − NDVI

Bu =NIR−MidIR

NIR+MidIR

NDVI

+ -

ND

BI - Forest Water

+ Grass Urban

TASK 2.A SUPERVISED CLASSIFICATION

Source: Jensen (2007)3

OBJECTIVE 2:


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2)(

1

)()()(2

1)(

hN

i

ii huzuzhN

h

Sem

i-va

rio

gram

(γ

)Distance (h)

sill

Nugget fx

range

z(ui)

z(ui+h)

TASK 2.D SPATIAL ANALYSIS


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METHODOLOGIES

Objective 3

OBJECTIVE 3:


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To develop a prototype of ABM featuring

three agents in a separated model

• Develop a robust ABM

• Which agent?

• How to define the agents?

• How to represent agents’ behavior into the

model?

OBJECTIVE 3:


Primary data

Expert: academician Practitioner: developer Government: Min.Pub.Work

Secondary data

Previous JMA reports Urban growth case studies in JMA

and developing countries

Cellular Automata (CA)

Agents Agents

A-A interactionA-E interaction

Translate to ABM script

verification

Conceptual frameworkfeedback

Static data layerThe environment, represents the space where agents are roaming spatially

Dynamic data layerThe agent, represents the actors that involve in developing the lands

Module 1:The environment

Module 2:The Agents

Module 3:The A-E interaction

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OBJECTIVE 3:



TASK 3.A DEFINE THE AGENTS

Agent Who Which Behaviour SourcesData acquisition

Method

Developers • Occupy >500 ha land in

JMA

• Searching land

• Price negotiation

• House credit impact

• Real Estate Indonesia

• School of Property

• Bandung Inst of Tech

• Semi-open interview

Residents • Middle-income

• 25-40 years of age

• Location preferences

• Price negotiation

• Which developers

• Thesis on resident

behaviours

• Indonesia resident

association

• Literature review

• Triangulation with

expert interview

Government • Each municipalities in

JMA

• Indonesia Ministry of

Public work, section

JMA (DPU ID)

• Urban policies

• Land allocation in

master plan

• Master plans of each

municipalities

• Planning agencies in

each municipalities

• Experts from DPU ID

• Semi-open interview


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OBJECTIVE 3:



TASK 3.B CONCEPTUAL FRAMEWORK

Hou

sin

g

Searching Found land

developers

Permission granted?

National Land Agency

yes

BUY

No

Develop Idle

Land owner

expelled

negotiate

High income Med incomeLow house

(CSR)

Spatial pattern Econm instinct

Very high, fast High, fast slow

Incr

easi

ng

Land

Val

ue

Source: Adopted from Firman (2004a)4


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METHODOLOGIES

Objective 4

OBJECTIVE 4:


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To evaluate the impact of policies on the urban spatial pattern by simulating

three policy scenarios in three types of interrelated agent’s behaviours

• What-if scenario

• To simulate the impact of urban policy on the

urban spatial pattern

• Foreseen the likely future development of an

area

OBJECTIVE 4:


NO Interaction

No protection on the threatened agriculture lands. Permission on new lands will be given as much as for industrial, commercial zones, and large housing.

Economic acceleration

Predicting urban growth using trend from the previous urban land changes (Ch.2). No change on urban regulations. Set as benchmark scenario

Business as usual

Conservation on existing parks, agriculture areas, and forests. Keep impact on environment as minimum as possible

Sustainable env

Scenario

1AScenario

1BScenario

1C

Scenario

2AScenario

2BScenario

2C

Scenario

3AScenario

3BScenario

3C

STATIC layerD

YN

AM

IC la

yer

1

2

3

A CompetitionB CollaborationC

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OBJECTIVE 4:


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Scenario Scenario ## Scenario ## Scenario ##

Map

Quantitative analysis

Spatial pattern

TASK 4.D SIMULATE THE SCENARIOS


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METHODOLOGIES

Objective 5

OBJECTIVE 5:


• What-if-then ? So what?

• Gap in urban modelling5

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To disseminate the results of urban ABM to improve the effectiveness

of urban decision-making towards a well-coordinated management

among municipalities in JMA

OBJECTIVE 5:


• Develop interactive medias:

• Simulation results in video format and,

• Web-based urban model, e.g. LAND YOUs

• Propose a guideline for urban policies

• Disseminate the findings

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To disseminate the results of urban ABM to improve the effectiveness

of urban decision-making towards a well-coordinated management

among municipalities in JMA

http://apps.giscame.com/glues/game.php?lang=EN&map=1


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MANAGEMENT


Key activities

1st year 2nd year 3rd year

Oct

Dec

Feb

Ap

r

Jun

Au

g

Oct

Dec

Feb

Ap

r

Jun

Au

g

Oct

Dec

Feb

Ap

r

Jun

Au

g

2012 2013 2014 2015

1 Objective 1

1.a To select and filter articles X

1.b To analyse geographic & timeframe X

1.c To summarize the findings X S

2 Objective 2

2.a To select and download the images X X

2.b To classify land cover changes X X

2.c To validate the results X

2.d To quantify spatio-temporal changes X

2.e To explore the spatial pattern X X S

3 Objective 3

3.a FIELDWORK X X

3.b To define the agent & behaviour X

3.c Construct conceptual framework X

3.d Develop a prototype of ABM X X

3.e To verify the model X

3.f To simulate three agents X S

4 Objective 4

4.a To define interrelated behaviour X

4.b To develop computer model X X X

4.c To run model & analyse the results X X

4.d To simulate scenario X X X S

5 Objective 5

5.a To build an interactive media X X X

5.b To propose guideline X

5.c To disseminate the findings X

M PhD Milestones

M.1 Confirmation X

M.2 Mid-candidature review X

M.3 Thesis review X

M.4 Conferences X

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FUNDING SOURCES


No Item GPEM-UQ UQ-ID GSITAData acquisition

1. Flight ticket return to Indonesia 15002. Administration fees for Indonesia statistical data 2003. Administration fees for spatial data (road network map etc) 2004. Local transportation 2005. Duplication of reports and master plans (DVD) 1006. Hard drive for data storage and back-up 100

International Conference (either in Australia or Asia)7 . Registration fees 3008. Return flight tickets 20009. Accommodations and foods 600

International conference (other parts of the world) 10. Registration fees 40011. Return flight tickets 70012. Accommodations and foods 300

Thesis publication13. Proofreading 40014. Printing 200

Total cost 7300

Optional expenditures15. Dissemination in Indonesia 300016. Skill training Repast Java programming 3000

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EXPECTED RESULTS

• Three international peer-reviewed articles:• Submission of “Land covers of JMA, urban growths, and its spatial

patterns” to International Journal of Remote Sensing Obj.2

• Submission of “A prototype of ABM for single agent” to Journal of computer urban modelling OR Int Journal of GI Science Obj.3

• Submission of “Urban modelling with multi-scenario and different types of interaction in JMA” to Journal of computer urban modelling OR Int Journal of GI Science Obj.4

• Interactive medias: movie, and web-based urban simulation models Obj.5

• Articles in the newspapers in Indonesia

• PhD thesis


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EXPECTED LIMITATIONS

• Data and metadata are lacking ; accessibility, availability, and reliability.

• Basic experience in Java for writing the programming code.

• Urban model over-fits the reality.

• From modelling to reality: practical gap.


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References

1. Taubenböck, H., Esch, T., Felbier, A., Wiesner, M., Roth, A., Dech, S., 2012, Monitoring urbanization in mega cities from space, Remote Sensing of Environment 117(0):162-176

2. Nations, U., 2004, World population to 2300, New York: United Nations.

3. Jensen, J. R., 2007, Remote sensing of the environment. an earth resource perspective, Prentice Hall series in geographic information science.

4. Firman, T., 2004a, Major issues in Indonesia's urban land development, Land Use Policy 21(4):347-355

5. Ersahin, V. K., 2008, Toward integration of Bayesian Networks with geographic information systems and complex systems theory for urban land use change modelling, Simon Fraser University (Canada), Canada, pp. 293

THANK YOU

urban growth modelling in jakarta metropolitan area: the research proposal

Science

growth of urban

urban cases

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urban system work

rapid urban growth

example researcher urban

urban modelling concentrates

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