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A case study of Jakarta Metropolitan Area, Indonesia
Modelling the spatial decisions of
private developers:
Agung Wahyudi,...
Agung Wahyudi S42817150
Why model the residential developer behaviour?
• Urban models aim to provide:
• a better understan...
Agung Wahyudi S42817150
The spatial context of this study
• Covers 6800 km2,
• Population: 28 million people
(~10% of Indo...
Agung Wahyudi S42817150
Factors impacting developer decisions
• Developers consider
• multi-dimension factors,
• various a...
Agung Wahyudi S42817150
Conceptual framework – potential profit
hypothetical curve
• Blue line: Raw land
price before
inst...
Agung Wahyudi S42817150
Method #1:
The modules in agent-based urban growth model
• The model has three modules:
1. environ...
Agung Wahyudi S42817150
Method #2:
The workflow
Agung Wahyudi S42817150
Method #3:
The model interface
• The interface offers views on land
cover, land value, distance to...
Agung Wahyudi S42817150
Results #1:
Urban land cover growth
• The movement of the developers: toward the outer
most of stu...
Agung Wahyudi S42817150
Results #2:
Land value changes
• Land value in newly developed areas
increase (∼300%) as developer...
Agung Wahyudi S42817150
Tentative conclusions and future work
• The model offers insight into how new residential areas an...
THANK YOU
@agungwah
Questions? Comments? Follow my research on
Agung Wahyudi S42817150
FAQ
• Why the model have no sensitivity analysis?
• What is the impact of other factors?
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Modelling the spatial decisions of private developers: A case study of Jakarta Metropolitan Area, Indonesia

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A presentation at the 14th International Conference on Computers in Urban Planning and Urban Management (CUPUM) which was held at MIT in Cambridge, Massachusetts USA on July 7-10, 2015


Urban models are important tools for planners in their capacity to offer in-sight into future urban growth. However, the majority of urban models overlook the role of developers’ behaviour in capturing the growth of ur-ban residential spaces. This paper redresses this gap by embedding the spa-tial consequences of privately-driven urban residential development on se-lection of potential land and their impact on land prices within an agent-based model. Jakarta (JMA), Indonesia forms the case study context. Re-sults from the model highlight the emergence of new urban areas on the JMA’s fringe that are strongly tied to land value. The model offers poten-tial to offer new insights into the relationship between land cover and land prices and the role of developers’ decisions in shaping the expansion of residential areas.

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Modelling the spatial decisions of private developers: A case study of Jakarta Metropolitan Area, Indonesia

  1. 1. A case study of Jakarta Metropolitan Area, Indonesia Modelling the spatial decisions of private developers: Agung Wahyudi, Yan Liu, Jonathan Corcoran School of Geography, Planning and Environmental Management (GPEM) The University of Queensland AUSTRALIA
  2. 2. Agung Wahyudi S42817150 Why model the residential developer behaviour? • Urban models aim to provide: • a better understanding of the mechanisms underpinning urban system and; • Simulate the impact of a spatial policies on urban form • There has been various urban models used to simulate urban growth and urban form. • Models accounting for residential developers behaviour are largely missing from these models. • In particular, the representation of profit-driven developers on searching for the most profitable areas, and the impact of this behaviour on land cover and land value.
  3. 3. Agung Wahyudi S42817150 The spatial context of this study • Covers 6800 km2, • Population: 28 million people (~10% of Indonesian). MEGAcity • Jakarta (the capital of Indonesia) as the economic core.
  4. 4. Agung Wahyudi S42817150 Factors impacting developer decisions • Developers consider • multi-dimension factors, • various actors, as well as • details on the factor intrinsic to the site. • In the model, we selected few factors to localize and to focus the impact of these factors on urban form. • In the model, factors intrinsic to the site : • Proximity of the targeted area from the city centre (CBD), • Proximity to the (toll) roads, and • Land value. • Factors intrinsic to the developer : • Access to capital affects the decision (incl loan), • Number of developers in the area.
  5. 5. Agung Wahyudi S42817150 Conceptual framework – potential profit hypothetical curve • Blue line: Raw land price before installment of infrastructure varies according to the distance from city centre • Red line: Road construction improves site accessibility • Green line: Added value increase equally as much as ∼30- 100% of cost • Purple line: Selling price 30% • Monocentric assumption still applicable in JMASource: adapted from Winarso 2000 Landvalue
  6. 6. Agung Wahyudi S42817150 Method #1: The modules in agent-based urban growth model • The model has three modules: 1. environment, 2. agent, and 3. interaction • The environment module represents the properties owned by the land; land cover type, land price, distance to CBD, distance to road • The agent represents properties exhibited by the developers; the capital, number of developers Environment Agent
  7. 7. Agung Wahyudi S42817150 Method #2: The workflow
  8. 8. Agung Wahyudi S42817150 Method #3: The model interface • The interface offers views on land cover, land value, distance to CBD, or distance to road. • Developers’ parameters: number of developers, the searching radius a, initial capital, and loan. • Monitoring panels (on the right).
  9. 9. Agung Wahyudi S42817150 Results #1: Urban land cover growth • The movement of the developers: toward the outer most of study area. • The developers capture the potential economic opportunity on the outer most area. t=0 t=0+n
  10. 10. Agung Wahyudi S42817150 Results #2: Land value changes • Land value in newly developed areas increase (∼300%) as developers construct the infrastructure in these sites. • Increasing land values prevent other developers moving into these areas. t=0 t=0+n
  11. 11. Agung Wahyudi S42817150 Tentative conclusions and future work • The model offers insight into how new residential areas and land values across JMA is shaped by the developer behaviour. • Deviation of the simulated urban pattern from the observed coverage suggests that the profit oriented (profit maximization) seems only one dimension of the decision to develop. • Marketing • Land banking • Limitations: 1. Simple initial set of variables, 2. No temporal dimension. • Future work: 1. A typology of developers, 2. Incorporate notion of land banking, and 3. Investigate inclusion of a temporal dimension.
  12. 12. THANK YOU @agungwah Questions? Comments? Follow my research on
  13. 13. Agung Wahyudi S42817150 FAQ • Why the model have no sensitivity analysis? • What is the impact of other factors?

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