IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v8y2016i7p686-d74257.html
   My bibliography  Save this article

Modeling Urban Expansion in Bangkok Metropolitan Region Using Demographic–Economic Data through Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models

Author

Listed:
  • Chudech Losiri

    (Remote Sensing and Geographic Information System FoS, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand)

  • Masahiko Nagai

    (Remote Sensing and Geographic Information System FoS, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand)

  • Sarawut Ninsawat

    (Remote Sensing and Geographic Information System FoS, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand)

  • Rajendra P. Shrestha

    (Natural Resources Management FoS, School of Environmental, Resources and Development, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand)

Abstract

Urban expansion is considered as one of the most important problems in several developing countries. Bangkok Metropolitan Region (BMR) is the urbanized and agglomerated area of Bangkok Metropolis (BM) and its vicinity, which confronts the expansion problem from the center of the city. Landsat images of 1988, 1993, 1998, 2003, 2008, and 2011 were used to detect the land use and land cover (LULC) changes. The demographic and economic data together with corresponding maps were used to determine the driving factors for land conversions. This study applied Cellular Automata-Markov Chain (CA-MC) and Multi-Layer Perceptron-Markov Chain (MLP-MC) to model LULC and urban expansions. The performance of the CA-MC and MLP-MC yielded more than 90% overall accuracy to predict the LULC, especially the MLP-MC method. Further, the annual population and economic growth rates were considered to produce the land demand for the LULC in 2014 and 2035 using the statistical extrapolation and system dynamics (SD). It was evident that the simulated map in 2014 resulting from the SD yielded the highest accuracy. Therefore, this study applied the SD method to generate the land demand for simulating LULC in 2035. The outcome showed that urban occupied the land around a half of the BMR.

Suggested Citation

  • Chudech Losiri & Masahiko Nagai & Sarawut Ninsawat & Rajendra P. Shrestha, 2016. "Modeling Urban Expansion in Bangkok Metropolitan Region Using Demographic–Economic Data through Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models," Sustainability, MDPI, vol. 8(7), pages 1-23, July.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:7:p:686-:d:74257
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/8/7/686/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/8/7/686/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huiran Han & Chengfeng Yang & Jinping Song, 2015. "Scenario Simulation and the Prediction of Land Use and Land Cover Change in Beijing, China," Sustainability, MDPI, vol. 7(4), pages 1-20, April.
    2. Kritsana Kityuttachai & Nitin Kumar Tripathi & Taravudh Tipdecho & Rajendra Shrestha, 2013. "CA-Markov Analysis of Constrained Coastal Urban Growth Modeling: Hua Hin Seaside City, Thailand," Sustainability, MDPI, vol. 5(4), pages 1-21, April.
    3. Manfred M. Fischer & Peter Nijkamp (ed.), 2014. "Handbook of Regional Science," Springer Books, Springer, edition 127, number 978-3-642-23430-9, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhanhang Zhou & Linjian Cao & Kuokuo Zhao & Dongliang Li & Ci Ding, 2021. "Spatio-Temporal Effects of Multi-Dimensional Urbanization on Carbon Emission Efficiency: Analysis Based on Panel Data of 283 Cities in China," IJERPH, MDPI, vol. 18(23), pages 1-20, December.
    2. Honglei Jiang & Xia Xu & Mengxi Guan & Lingfei Wang & Yongmei Huang & Yinghui Liu, 2019. "Simulation of Spatiotemporal Land Use Changes for Integrated Model of Socioeconomic and Ecological Processes in China," Sustainability, MDPI, vol. 11(13), pages 1-18, July.
    3. Nij Tontisirin & Sutee Anantsuksomsri, 2021. "Economic Development Policies and Land Use Changes in Thailand: From the Eastern Seaboard to the Eastern Economic Corridor," Sustainability, MDPI, vol. 13(11), pages 1-20, May.
    4. Youjung Kim & Galen Newman, 2019. "Climate Change Preparedness: Comparing Future Urban Growth and Flood Risk in Amsterdam and Houston," Sustainability, MDPI, vol. 11(4), pages 1-24, February.
    5. Esther Shupel Ibrahim & Bello Ahmed & Oludunsin Tunrayo Arodudu & Jibril Babayo Abubakar & Bitrus Akila Dang & Mahmoud Ibrahim Mahmoud & Halilu Ahmad Shaba & Sanusi Bello Shamaki, 2022. "Desertification in the Sahel Region: A Product of Climate Change or Human Activities? A Case of Desert Encroachment Monitoring in North-Eastern Nigeria Using Remote Sensing Techniques," Geographies, MDPI, vol. 2(2), pages 1-23, April.
    6. Bhanage Vinayak & Han Soo Lee & Shirishkumar Gedem, 2021. "Prediction of Land Use and Land Cover Changes in Mumbai City, India, Using Remote Sensing Data and a Multilayer Perceptron Neural Network-Based Markov Chain Model," Sustainability, MDPI, vol. 13(2), pages 1-22, January.
    7. Pongsun Bunditsakulchai & Chen Liu, 2021. "Integrated Strategies for Household Food Waste Reduction in Bangkok," Sustainability, MDPI, vol. 13(14), pages 1-21, July.
    8. Yan Liu & Yongjiu Feng, 2016. "Simulating the Impact of Economic and Environmental Strategies on Future Urban Growth Scenarios in Ningbo, China," Sustainability, MDPI, vol. 8(10), pages 1-16, October.
    9. Ye Zhou & Feng Zhang & Zhenhong Du & Xinyue Ye & Renyi Liu, 2017. "Integrating Cellular Automata with the Deep Belief Network for Simulating Urban Growth," Sustainability, MDPI, vol. 9(10), pages 1-19, October.
    10. Niang Sian Lun & Siddharth Chaudhary & Sarawut Ninsawat, 2023. "Assessment of Machine Learning Methods for Urban Types Classification Using Integrated SAR and Optical Images in Nonthaburi, Thailand," Sustainability, MDPI, vol. 15(2), pages 1-17, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eshetu Yirsaw & Wei Wu & Xiaoping Shi & Habtamu Temesgen & Belew Bekele, 2017. "Land Use/Land Cover Change Modeling and the Prediction of Subsequent Changes in Ecosystem Service Values in a Coastal Area of China, the Su-Xi-Chang Region," Sustainability, MDPI, vol. 9(7), pages 1-17, July.
    2. Rey, Sergio, 2015. "Bells in Space: The Spatial Dynamics of US Interpersonal and Interregional Income Inequality," MPRA Paper 69482, University Library of Munich, Germany.
    3. Juan C. Surís-Regueiro & José L. Santiago, 2016. "An Input-Output methodological proposal to quantifying socio economic impacts linked to supply shocks," Working Papers 1603, Universidade de Vigo, Departamento de Economía Aplicada.
    4. Youjung Kim & Galen Newman, 2019. "Climate Change Preparedness: Comparing Future Urban Growth and Flood Risk in Amsterdam and Houston," Sustainability, MDPI, vol. 11(4), pages 1-24, February.
    5. Manfred M. Fischer & Nico Pintar & Benedikt Sargant, 2016. "Austrian Outbound Foreign Direct Investment in Europe:A spatial econometric study," Romanian Journal of Regional Science, Romanian Regional Science Association, vol. 10(1), pages 1-22, JUNE.
    6. Boeing, Geoff, 2017. "OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks," SocArXiv q86sd, Center for Open Science.
    7. Yunfeng Hu & Batu Nacun, 2018. "An Analysis of Land-Use Change and Grassland Degradation from a Policy Perspective in Inner Mongolia, China, 1990–2015," Sustainability, MDPI, vol. 10(11), pages 1-22, November.
    8. Simon Baumgartinger-Seiringer & David Doloreux & Richard Shearmur & Michaela Trippl, 2021. "When history does not matter? The rise of Quebec’s wine industry," PEGIS geo-disc-2021_05, Institute for Economic Geography and GIScience, Department of Socioeconomics, Vienna University of Economics and Business.
    9. Clemens, Michael A., 2021. "Violence, development, and migration waves: Evidence from Central American child migrant apprehensions," Journal of Urban Economics, Elsevier, vol. 124(C).
    10. Laird, James J. & Venables, Anthony J., 2017. "Transport investment and economic performance: A framework for project appraisal," Transport Policy, Elsevier, vol. 56(C), pages 1-11.
    11. Funashima, Yoshito & Ohtsuka, Yoshihiro, 2019. "Spatial crowding-out and crowding-in effects of government spending on the private sector in Japan," Regional Science and Urban Economics, Elsevier, vol. 75(C), pages 35-48.
    12. Bernard Fosu Frimpong & Frank Molkenthin, 2021. "Tracking Urban Expansion Using Random Forests for the Classification of Landsat Imagery (1986–2015) and Predicting Urban/Built-Up Areas for 2025: A Study of the Kumasi Metropolis, Ghana," Land, MDPI, vol. 10(1), pages 1-21, January.
    13. Julia Frutos Cachorro & Katrin Erdlenbruch & Mabel Tidball, 2019. "Sharing a Groundwater Resource in a Context of Regime Shifts," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 72(4), pages 913-940, April.
    14. Sergio J. Rey, 2018. "Bells in Space," International Regional Science Review, , vol. 41(2), pages 152-182, March.
    15. Giulio Cainelli & Roberto Ganau & Marco Modica, 2019. "Does related variety affect regional resilience? New evidence from Italy," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 62(3), pages 657-680, June.
    16. Andrew Allan & Ali Soltani & Mohammad Hamed Abdi & Melika Zarei, 2022. "Driving Forces behind Land Use and Land Cover Change: A Systematic and Bibliometric Review," Land, MDPI, vol. 11(8), pages 1-20, August.
    17. James P. LESAGE, 2014. "Software For Bayesian Spatial Model Comparison," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 40, pages 11-24.
    18. Mingfei Ma & Ying Jin, 2019. "Economic impacts of alternative greenspace configurations in fast growing cities: The case of Greater Beijing," Urban Studies, Urban Studies Journal Limited, vol. 56(8), pages 1498-1515, June.
    19. Shaikh Shamim Hasan & Xiangzheng Deng & Zhihui Li & Dongdong Chen, 2017. "Projections of Future Land Use in Bangladesh under the Background of Baseline, Ecological Protection and Economic Development," Sustainability, MDPI, vol. 9(4), pages 1-21, March.
    20. Ryan M. Gallagher & Joseph Persky, 2020. "Heterogeneity of birth‐state effects on internal migration," Journal of Regional Science, Wiley Blackwell, vol. 60(3), pages 517-537, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:8:y:2016:i:7:p:686-:d:74257. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.