Dynamic urban modelling: Cellular automata (CA) and Agent-based modelling (ABM) approach

Transcription

1 Dynamic urban modelling: Cellular automata (CA) and Agent-based modelling (ABM) approach Agung Wahyudi Spatio-Temporal Analytics Research Laboratory (STAR-Lab)

2 Understanding JMA s urban spatial pattern FIRST STAGE

3 Landsat, USGS True colour Study area Covers 7500 km 2 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 Agung Wahyudi S

4 First stage: Background FIRST: To understand the urban growth patterns in the Jakarta Metropolitan Area (JMA) SECOND: To develop an agent-based urban growth model that incorporates microeconomic theory. THIRD: To understand the impact of multi-type developer behaviours on the spatial pattern of urban growth in JMA

5 First stage: Methodology

6 First stage: Data collection Landsat imagery 1994 : Aux 1995 : TM : Aux 1999, 2001: ETM 7 and TM5 2006: Aux 2005: ETM 7 and TM5 2012: Aux 2011: ETM : Landsat 8 OLI Vector shapefiles: road, adm map, river network.

7 Accuracy assessment results differently depending on land cover classes. Overall accuracy >80%

8 Three major locations of urban developments. 1. South of Tangerang 2. South of Kota Bekasi 3. North of Jakarta

9 (a) (b) Spatial pattern observed among three urban land cover classes, as observed from Jakarta (Monas) (c) (d)

10 High-density settlement Low-density settlement Industrial area Edge density (a) (b) (c) PAFRAC (d) (e) (f) Highly connected high-density settlement near the core of Jakarta, And PLADJ (g) (h) (i) Dispersed on Jakarta s outskirts IJI (j) (k) (l)

11 Developing Agent-based model SECOND STAGE

12 Second stage: background Urban expansion in the JMA is mainly composed of large-scale residential projects by private developers, How the level of capital that developers possess impacts on development in the urban land market is not fully understood. Spatial model that demonstrates a supply-side urban growth model with explicit spatial representation of land analysis and, micro-economic theory (profit-based motivation)

13 Second stage: background Hypothetical profit curves from the developer s perspective Adapted from Winarso (2000).

14 Second stage: Data input (a) (b) 1. Land attributes 2. Developers capital characteristic (c) (d)

15 Second stage: Workflow The crucial element in the ABM workflow is the interactions between the developers, The land, and the decision impacted upon.

16 No Lending Maximum lending at 75% of the capital Initial capital: Rp. 5 trillion (US$ 375 million) a. b. Result: Simulated urban areas Rp. 7 trillion (US$ 525 million) Rp. 10 trillion (US$ 750 million) c. d. Designed 2x3 developers capital scenarios. Hypothetical findings: New urban development occurs mainly on the outskirts of Jakarta. Support past findings (Cybriwsky and Ford, 2001; Firman, 2012; Winarso and Firman, 2002) e. The two concentric red lines on the figure signify 25 km and 50 km from the CBD.

17 No Lending Maximum lending at 75% of the capital Rp. 5 trillion (US$ 375 million) a. b. Result: Simulated land values Rp. 7 trillion (US$ 525 million) Rp. 10 trillion (US$ 750 million) c. d. f. g. Hypothetical findings: Increase in land value is in parallel with development of new urban areas. The more capital possessed, the more increase on land values. Due to infrastructure improvement. The two concentric red lines on the figure signify 25 km and 50 km from the CBD.

18 Result: Simulated new urban areas Rp. 5 trillion (US$ 375 million) Rp. 7 trillion (US$ 525 million) Rp. 10 trillion (US$ 750 million) No lending a. b. c. Maximum lending at 75% of the capital d. e. f. Which type of developer could realize the development?

19 Investigating multi-type developers in influencing urban development process THIRD STAGE

20 Third stage: background Developer has been represented as homogeneous agent in past urban modelling studies. Variations on the strategy, preferences, and development targets may result in location, size, selling price of new urban areas. to explore the spatial pattern and locations of urban areas as a result of varying developers characteristics.

21 Third stage: Workflow Land searching Land assessment Small developer Medium developer Large developer Not visited yet Within 10 km Development cost: - Land acquisition - Land clearing Not visited yet within 30 km + - Road construction Land decision Revenue > 0 Revenue >= 15% AND > Expected profit Land action Develop and release within 24 months Not visited yet + - Road construction Revenue >= 15% AND > Expected profit 36 months 48 months Land target size 10 ha 50 ha 100 ha

22 Third stage: Workflow

23 Result: Simulated land values Hypothetical findings: development processes and actors underpin different shapes and forms of urban areas in a city selected locations by large developers were more predictable developers with less capital form a smaller size urban area with a more fragmented pattern AND less predictable.

24 Small developers Medium. Develoers a. b. c. d. e. f. Result: Simulated land values Large Develoers Mixed Develoers Hierarchy Develoers g. h. i. j. k. l. j. k. l. Hypothetical findings: indicate the imbalance distribution of capital over different types of developers. inevitable consequence of a competitive and non-regulated market

25 Future Potential collaborations Future researches

26 Agung Wahyudi Spatio-temporal analytic research (STAR-Lab) Research interests: agentbased modelling (ABM) for spatial problems, Publication: Education: PhD (UQ, Australia), MSc (UGent, Belgium), ST (ITB, Indonesia),