Integration of logistic regression,Markov chain and cellular automata models to simulate urban expansion |
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| Institution: | 1. Department of Geography and Regional Research, University of Vienna, Vienna, Austria;2. Department of Geography, University of Heidelberg, Heidelberg, Germany;3. Department of Geography and Cartography, University of Tehran, Tehran, Iran;1. School of Geodesy and Geomatics, Jiangsu Normal University, Xuzhou 221116, China;2. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093, China;3. School of Public Administration, Nanjing University of Finance & Economics, Nanjing 210023, China;4. College of Public Management, Nanjing Agricultural University, Nanjing 210095, China;1. Syiah Kuala University, Architecture Department, Banda Aceh, Indonesia;2. University of Sumatera Utara, Regional Planning Study Program, Medan, Indonesia;3. University of Sumatera Utara, Sociology Department, Medan, Indonesia;4. University of Sumatera Utara, Architecture Department, Medan, Indonesia;1. Cukurova University, Department of Landscape Architecture, Adana, Turkey;2. Bursa Technical University, Regional and Urban Planning Department, Bursa, Turkey;3. University of California, Department of Geography, Santa Barbara, USA;1. Graduate School of Engineering, Department of Systems Innovation, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan;2. Graduate School of Agriculture, Division of Forest and Biomaterials Sciences, Kyoto University, Kyoto 606-8502, Japan;3. Institute of Forestry and Environmental Sciences, University of Chittagong, Chittagong 4331, Bangladesh |
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| Abstract: | This research analyses the suburban expansion in the metropolitan area of Tehran, Iran. A hybrid model consisting of logistic regression model, Markov chain (MC), and cellular automata (CA) was designed to improve the performance of the standard logistic regression model. Environmental and socio-economic variables dealing with urban sprawl were operationalised to create a probability surface of spatiotemporal states of built-up land use for the years 2006, 2016, and 2026. For validation, the model was evaluated by means of relative operating characteristic values for different sets of variables. The approach was calibrated for 2006 by cross comparing of actual and simulated land use maps. The achieved outcomes represent a match of 89% between simulated and actual maps of 2006, which was satisfactory to approve the calibration process. Thereafter, the calibrated hybrid approach was implemented for forthcoming years. Finally, future land use maps for 2016 and 2026 were predicted by means of this hybrid approach. The simulated maps illustrate a new wave of suburban development in the vicinity of Tehran at the western border of the metropolis during the next decades. |
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| Keywords: | Land use change Logistic regression Markov chain Cellular automata Tehran |
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