urban growth modelling in jakarta metropolitan area: the research proposal
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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
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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
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• 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)
Agung Wahyudi S42817150
• 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)
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• 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:
Agung Wahyudi S42817150
• 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:
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• 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:
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• 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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To identify the spatio-temporal pattern of urban growths
in JMA using Landsat imaging
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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To identify the spatio-temporal pattern of urban growths
in JMA using Landsat imaging
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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To identify the spatio-temporal pattern of urban growths
in JMA using Landsat imaging
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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To identify the spatio-temporal pattern of urban growths
in JMA using Landsat imaging
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:
Agung Wahyudi S42817150
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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To develop a prototype of ABM featuring
three agents in a separated model
OBJECTIVE 3:
Agung Wahyudi S42817150
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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To develop a prototype of ABM featuring
three agents in a separated model
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OBJECTIVE 3:
To develop a prototype of ABM featuring
three agents in a separated model
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:
To develop a prototype of ABM featuring
three agents in a separated model
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:
Agung Wahyudi S42817150
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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To evaluate the impact of policies on the urban spatial pattern by simulating
three policy scenarios in three types of interrelated agent’s behaviours
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
Scenario Scenario ## Scenario ## Scenario ##
Map
Quantitative analysis
Spatial pattern
TASK 4.D SIMULATE THE SCENARIOS
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METHODOLOGIES
Objective 5
OBJECTIVE 5:
Agung Wahyudi S42817150
• 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:
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• 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
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MANAGEMENT
Agung Wahyudi S42817150
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
Agung Wahyudi S42817150
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
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