A framework for modelling spatio-temporal informal settlement growth prediction |
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| Affiliation: | 1. Stellenbosch Unit for Operations Research in Engineering, Department of Industrial Engineering, Stellenbosch University, Private box X1, Matieland, 7602, Stellenbosch, Western Cape, South Africa;2. Urban and Regional Dynamics, Smart Places, Council for Scientific and Industrial Research, PO Box 395, Pretoria, Gauteng, South Africa;1. Department of Geography, College of Arts and Sciences, University at Buffalo, 105 Wilkeson, Buffalo, NY 14261, United States;2. School of Architecture and Planning, University of Auckland, 1010, New Zealand;3. Department of Earth and Environmental Sciences, KU Leuven, Celestijnenlaan 200E, Leuven 3001, Belgium;1. Faculty of Forestry, University of British Columbia, , 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada;2. School of Environmental Studies, University of Victoria, British Columbia, Canada;3. Department of Biological Sciences, University of Calgary, Alberta, Canada;1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;2. Department of Geography & Geoinformation Science, George Mason University, Fairfax, VA 22030, USA;1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China;1. Centre for New Economics Studies, Jindal School of Liberal Arts and Humanities, O.P. Jindal Global University, India;2. Centre for New Economics Studies, India |
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| Abstract: | Many developing countries grapple with the problem of rapid informal settlement emergence and expansion. This exacts considerable costs from neighbouring urban areas, largely as a result of environmental, sustainability and health-related problems associated with such settlements, which can threaten the local economy. Hence, there is a need to understand the nature of, and to be able to predict, future informal settlement emergence locations as well as the rate and extent of such settlement expansion in developing countries.A novel generic framework is proposed in this paper for machine learning-inspired prediction of future spatio-temporal informal settlement population growth. This data-driven framework comprises three functional components which facilitate informal settlement emergence and growth modelling within an area under investigation. The framework outputs are based on a computed set of influential spatial feature predictors pertaining to the area in question. The objective of the framework is ultimately to identify those spatial and other factors that influence the location, formation and growth rate of an informal settlement most significantly, by applying a machine learning modelling approach to multiple data sets related to the households and spatial attributes associated with informal settlements. Based on the aforementioned influential spatial features, a cellular automaton transition rule is developed, enabling the spatio-temporal modelling of the rate and extent of future formations and expansions of informal settlements. |
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