Spatiotemporal variability of urban growth factors: A global and local perspective on the megacity of Mumbai |
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Institution: | 1. Institute of Geography, University of Heidelberg, Germany;2. Tarbiat Modares University, Department of GIS and Remote Sensing, Tehran, Iran;3. Department of Human Geography and Spatial Planning, Utrecht University, Netherlands;1. School of Economics and Management, Southeast University, Nanjing 211189, China;2. Department of Public Policy, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China;3. Shenzhen Research Institute, City University of Hong Kong, Shenzhen 518057, China;4. Department of Public Management, College of Management, Shenzhen University, Shenzhen, China;5. School of Landscape Architecture, Zhejiang Agriculture and Forestry University, Hangzhou 311300, China;6. School of Management, Lanzhou University, Lanzhou 730000, China;1. Department of Geography and Environmental studies, Debre Tabor University, P.O.Box 272, Ethiopia;2. Department of Marine Geology, Mangalore University, Mangalagangothri, Karnataka, India |
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Abstract: | The rapid growth of megacities requires special attention among urban planners worldwide, and particularly in Mumbai, India, where growth is very pronounced. To cope with the planning challenges this will bring, developing a retrospective understanding of urban land-use dynamics and the underlying driving-forces behind urban growth is a key prerequisite. This research uses regression-based land-use change models – and in particular non-spatial logistic regression models (LR) and auto-logistic regression models (ALR) – for the Mumbai region over the period 1973–2010, in order to determine the drivers behind spatiotemporal urban expansion. Both global models are complemented by a local, spatial model, the so-called geographically weighted logistic regression (GWLR) model, one that explicitly permits variations in driving-forces across space. The study comes to two main conclusions. First, both global models suggest similar driving-forces behind urban growth over time, revealing that LRs and ALRs result in estimated coefficients with comparable magnitudes. Second, all the local coefficients show distinctive temporal and spatial variations. It is therefore concluded that GWLR aids our understanding of urban growth processes, and so can assist context-related planning and policymaking activities when seeking to secure a sustainable urban future. |
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Keywords: | Urban growth Logistic regression Autologistic regression Geographically weighted logistic regression GIS |
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