DOI: https://doi.org/10.14710/geoplanning.4.2.201-212

CELLULAR AUTOMATA MODELING IN THE BUILT-UP AREAS WITHIN URBAN DEVELOPMENT AT PONTIANAK

*Ely Nurhidayati  -  Department of Architecture and Planning, Pontianak State Polytechnic, Indonesia
Imam Buchori  -  Diponegoro University, Indonesia
Mussadun Mussadun  -  Diponegoro University, Indonesia

Citation Format:
Abstract
This research integrated the GIS-Cellular Automata model with the regression model to predict urban development in Pontianak within the built up area change phenomena approach. The research aimed to understand built-up land use development in Pontianak during 1990-2015 and to predict its regional development in 2033. The employed method were satellite the image interpretation approach, hybrid interpretation, and built up land development prediction using transition rules like driving factors and inhibiting factors of urban development. The driving ones are accessibility related to distances to CBD, to main roads, and to the existing built regional areas while the inhibiting ones are peatland and the protected areas. The result showed that the hybrid interpretation, between visual and digital interpretations from the landsat images, can be used to map the built up lands with 94.8% of sampling point’s precision. The non-built up areas in Pontianak during 1990-2015 were 83.52 Ha/year, and the modelling result predicts that non-built regional areas in Pontianak during 2015-2033 will be 80.51 Ha/year heading toward northern and central areas of Pontianak.                   
Fulltext View|Download
Keywords: Cellular Automata; Hybird Interpretation; Built-up Area; Landsat Imagery

Article Metrics:

Article Info
Section: Original Research
Language : EN
Recent articles
ASSESSING THE USEFULNESS OF UNMANNED AERIAL VEHICLE (UAV) FOR MONITORING SPATIAL PLAN: LEGAL AND USER PERSPECTIVE OF BOGOR REGENCY, INDONESIA MODEL OF SOIL AND WATER CONSERVATION MEASURES APPLICATION BASED ON DISTRICT SPATIAL PLANNING IN MAMASA WATERSHED, SOUTH SULAWESI MOUNTAIN ECOTOURISM SPATIAL PLANNING TOWARDS GREEN MANAGEMENT: THE CASE OF MOUNT LAWU REGION OF INDONESIA More recent articles
Most cited articles
ASSESSMENT OF MANGROVE FOREST DEGRADATION THROUGH CANOPY FRACTIONAL COVER IN KARIMUNJAWA ISLAND, CENTRAL JAVA, INDONESIA MODEL OF CLIMATE AND LAND-USE CHANGES IMPACT ON WATER SECURITY IN AMBON CITY, INDONESIA THE PERFORMANCE OF LAND USE CHANGE CAUSATIVE FACTOR ON LANDSLIDE SUSCEPTIBILITY MAP IN UPPER UJUNG-LOE WATERSHEDS SOUTH SULAWESI, INDONESIA PEMANFAATAN CITRA SATELIT UNTUK PENENTUAN LAHAN KRITIS MANGROVE DI KECAMATAN TUGU, KOTA SEMARANG EVALUASI CITRA WORLDVIEW-2 UNTUK PENDUGAAN KEDALAMAN PERAIRAN DANGKAL PULAU KELAPA-HARAPAN MENGGUNAKAN ALGORITMA RASIO BAND More cited articles

  1. Aburas, M. M., Ho, Y. M., Ramli, M. F., & Ash’aari, Z. H. (2016). The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review. International Journal of Applied Earth Observation and Geoinformation, 52, 380–389. [https://doi.org/10.1016/j.jag.2016.07.007">Crossref]

  2. Aljoufie, M., Zuidgeest, M., Brussel, M., van Vliet, J., & van Maarseveen, M. (2013). A cellular automata-based land use and transport interaction model applied to Jeddah, Saudi Arabia. Landscape and Urban Planning, 112, 89–99. [https://doi.org/10.1016/j.landurbplan.2013.01.003">Crossref]

  3. Basse, R. M., Omrani, H., Charif, O., Gerber, P., & Bódis, K. (2014). Land use changes modelling using advanced methods: Cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale. Applied Geography, 53, 160–171. [https://doi.org/10.1016/j.apgeog.2014.06.016">Crossref]

  4. Berberouglu, S., Akin, A., & Clarke, K. C. (2016). Cellular automata modeling approaches to forecast urban growth for adana, Turkey: A comparative approach. Landscape and Urban Planning, 153, 11–27. [https://doi.org/10.1016/j.landurbplan.2016.04.017">Crossref]

  5. Boavida-Portugal, I., Rocha, J., & Ferreira, C. C. (2016). Exploring the impacts of future tourism development on land use/cover changes. Applied Geography, 77, 82–91. [https://doi.org/10.1016/j.apgeog.2016.10.009">Crossref]

  6. Chander, G., & Markham, B. (2003). Revised landsat-5 tm radiometric calibration procedures and postcalibration dynamic ranges. IEEE Transactions on Geoscience and Remote Sensing, 41(11), 2674–2677. [https://doi.org/10.1109/tgrs.2003.818464">Crossref]

  7. Dabbaghian, V., Jackson, P., Spicer, V., & Wuschke, K. (2010). A cellular automata model on residential migration in response to neighborhood social dynamics. Mathematical and Computer Modelling, 52(9–10), 1752–1762. [https://doi.org/10.1016/j.mcm.2010.07.002">Crossref]

  8. Danoedoro, P. (2012). Pengantar penginderaan jauh digital. Yogyakarta: Andi.

  9. Darlington, T., Odindi, J., Dube, T., & Mutanga, O. (2017). Prediction of future urban surface temperatures using medium resolution satellite data in Harare metropolitan city , Zimbabwe. Building and Environment, 122, 397–410. [https://doi.org/10.1016/j.buildenv.2017.06.033">Crossref]

  10. Deep, S. (2014). Urban sprawl modeling using cellular automata. The Egyptian Journal of Remote Sensing and Space Sciences, 17(2), 179–187. [https://doi.org/10.1016/j.ejrs.2014.07.001">Crossref]

  11. Fuglsang, M., Münier, B., & Hansen, H. S. (2013). Modelling land-use effects of future urbanization using cellular automata: An Eastern Danish case. Environmental Modelling & Software, 50, 1–11. [https://doi.org/10.1016/j.envsoft.2013.08.003">Crossref]

  12. Garc’ia, A. M., Santé, I., Boullón, M., & Crecente, R. (2012). A comparative analysis of cellular automata models for simulation of small urban areas in Galicia, {NW} Spain. Computers, Environment and Urban Systems, 36(4), 291–301. [https://doi.org/10.1016/j.compenvurbsys.2012.01.001">Crossref]

  13. González, P. B., Aguilera-Benavente, F., & Gómez-Delgado, M. (2015). Partial validation of cellular automata based model simulations of urban growth: An approach to assessing factor influence using spatial methods. Environmental Modelling & Software, 69, 77–89. [https://doi.org/10.1016/j.envsoft.2015.03.008">Crossref]

  14. Guan, C., & Rowe, P. G. (2016). Should big cities grow? Scenario-based cellular automata urban growth modeling and policy applications. Journal of Urban Management, 5(2), 65–78. [https://doi.org/10.1016/j.jum.2017.01.002">Crossref]

  15. Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., & Hokao, K. (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, 222(20–22), 3761–3772. [https://doi.org/10.1016/j.ecolmodel.2011.09.009">Crossref]

  16. He, C., Okada, N., & Zhang, Q. (2006). Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing , China, 26, 323–345. [https://doi.org/10.1016/j.apgeog.2006.09.006">Crossref]

  17. He, Y. X., Zhang, J. X., Xu, Y., Gao, Y., Xia, T., & He, H. Y. (2015). Forecasting the urban power load in China based on the risk analysis of land-use change and load density. International Journal of Electrical Power & Energy Systems, 73, 71–79 [https://doi.org/10.1016/j.ijepes.2015.03.018">Crossref]

  18. Hien, D. (2015). Simulation Of Wide Spread Of Fire In Can Tho City By Random Cellular Automata. [https://doi.org/10.13140/RG.2.1.2360.6244">Crossref]

  19. Kim, I., Arnhold, S., Ahn, S., Bao, Q., Joon, S., Jin, S., & Koellner, T. (2017). Environmental Modelling & Software Land use change and ecosystem services in mountainous watersheds : Predicting the consequences of environmental policies with cellular automata and hydrological modeling. Environmental Modelling and Software. [https://doi.org/10.1016/j.envsoft.2017.06.018">Crossref]

  20. Liao, J., Tang, L., Shao, G., Su, X., Chen, D., & Xu, T. (2016). Incorporation of extended neighborhood mechanisms and its impact on urban land-use cellular automata simulations. Environmental Modelling & Software, 75, 163–175. [https://doi.org/10.1016/j.envsoft.2015.10.014">Crossref]

  21. Lin, J., Huang, B., Chen, M., & Huang, Z. (2014). Modeling urban vertical growth using cellular automata d Guangzhou as a case study. Applied Geography, 53, 172–186. [https://doi.org/10.1016/j.apgeog.2014.06.007">Crossref]

  22. Maria de Almeida, C., & Marinaldo Gleriani, J. (2005). Cellular automata and neural networks as a modelling framework for the simulation of urban land use change c. Anais XII Simposio Brasileiro de Sensoriamento Remoto, Goiania, Brasil, 3697–3705.

  23. Maria, R., Omrani, H., Charif, O., & Gerber, P. (2014). Land use changes modelling using advanced methods : Cellular automata and arti fi cial neural networks . The spatial and explicit representation of land cover dynamics at the cross-border region scale, 53, 160–171. [https://doi.org/10.1016/j.apgeog.2014.06.016">Crossref]

  24. Mitsova, D., Shuster, W., & Wang, X. (2011). A cellular automata model of land cover change to integrate urban growth with open space conservation. Landscape and Urban Planning, 99(2), 141–153. [https://doi.org/10.1016/j.landurbplan.2010.10.001">Crossref]

  25. Moghadam, H. S., & Helbich, M. (2013). Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model. Applied Geography, 40, 140–149. [https://doi.org/10.1016/j.apgeog.2013.01.009">Crossref]

  26. Pan, Y., Roth, A., Yu, Z., & Doluschitz, R. (2010). Computers , Environment and Urban Systems The impact of variation in scale on the behavior of a cellular automata used for land use change modeling. Computers. Environment and Urban Systems, 34(5), 400–408. [https://doi.org/10.1016/j.compenvurbsys.2010.03.003">Crossref]

  27. Pérez-Molina, E., Sliuzas, R., Flacke, J., & Jetten, V. (2017). Developing a cellular automata model of urban growth to inform spatial policy for flood mitigation: A case study in Kampala, Uganda. Computers, Environment and Urban Systems, 65, 53–65. [https://doi.org/10.1016/j.compenvurbsys.2017.04.013">Crossref]

  28. Rustiadi, E. (2018). Perencanaan dan pengembangan wilayah. Yayasan Pustaka Obor Indonesia.

  29. Silva, E. A., Ahern, J., & Wileden, J. (2008). Strategies for landscape ecology: An application using cellular automata models. Progress in Planning, 70(4), 133–177. [https://doi.org/10.1016/j.progress.2008.05.002">Crossref]

  30. USGS. (2015). Landsat 8 (L8) Data Users Hectaresndbook.

  31. van Vliet, J., White, R., & Dragicevic, S. (2009). Modeling urban growth using a variable grid cellular automaton. Computers, Environment and Urban Systems, 33(1), 35–43. [https://doi.org/10.1016/j.compenvurbsys.2008.06.006">Crossref]

  32. Votsis, A. (2017). Computers , Environment and Urban Systems Utilizing a cellular automaton model to explore the in fl uence of coastal fl ood adaptation strategies on Helsinki ’ s urbanization patterns. Computers, Environment and Urban Systems, 64, 344–355. [https://doi.org/10.1016/j.compenvurbsys.2017.04.005">Crossref]

  33. Wang, S. Q., Zheng, X. Q., & Zang, X. B. (2012). Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environmental Sciences, 13, 1238–1245. [https://doi.org/10.1016/j.proenv.2012.01.117">Crossref]

  34. Wu, F. (1998). An experiment on the generic polycentricity of urban growth in a cellular automatic city. Environment and Planning B: Planning and Design, 25(5), 731–752. [https://doi.org/10.1068/b250731">Crossref]

  35. Xu, X., Du, Z., & Zhang, H. (2016). Integrating the system dynamic and cellular automata models to predict land use and land cover change. International Journal of Applied Earth Observation and Geoinformation, 52, 568–579. [https://doi.org/10.1016/j.jag.2016.07.022">Crossref]

  36. Yunus, H. S. (2010). Metodologi penelitian wilayah kontemporer. Yogyakarta: Pustaka Pelajar.

  37. Zhou, D., Lin, Z., & Liu, L. (2012). Regional land salinization assessment and simulation through cellular automaton-Markov modeling and spatial pattern analysis. Science of The Total Environment, 439, 260–274. [https://doi.org/10.1016/j.scitotenv.2012.09.013">Crossref]


Last update:

  1. Future Disaster Risk Reduction Strategy Based on Land Use Prediction in a Surrounding Area of a Newly Developed Airport Infrastructure

    Amesta Ramadhani, Laksmi Devi, Dwita Sihombing, Chrisshine Raphonita. Technology for Sustainable Development, 112 , 2022. doi: 10.4028/p-j40cjp

Last update: 2024-12-23 17:48:22

No citation recorded.