Modelling spatial patterns of urban growth in Africa |
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| Affiliation: | 1. Biological Control and Spatial Ecology, Université Libre de Bruxelles, CP 160/12, Av. F.D. Roosevelt 50, B-1050 Brussels, Belgium;2. Fonds National de la Recherche Scientifique (F.R.S-FNRS), Rue d''Egmont 5, B-1000 Brussels, Belgium;3. Department of Geography and Environment, University of Southampton, Highfield, Southampton SO17 1BJ, UK;4. Fogarty International Center, National Institutes of Health, Bethesda, MD, United States;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. Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA;2. Social, Urban, Rural and Resilience Global Practice, World Bank, Washington, DC 20433, USA;1. German centre for Integrative Biodiversity (iDiv) Research Halle-Jena-Leipzig, Germany;2. Department of Economics and Finance, Nisantasi University, Istanbul, Turkey;3. Department of Mathematical and Computer Sciences, University of Medical Sciences, Ondo City, Ondo State, Nigeria;1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;2. Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;3. College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China;1. Yale School of Forestry and Environmental Studies, Yale University, 195 Prospect Street, New Haven, CT 06511, United States;2. Department of Political Science, The Ohio State University, 2140 Derby Hall, 154 North Oval Mall, Columbus, OH 43210-1373, United States;3. Center for Chinese Agricultural Policy, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China |
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| Abstract: | The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare for these changes, it is important to better understand urban growth dynamics in Africa and better predict the spatial pattern of rural-urban conversions. Previous work on urban expansion has been carried out at the city level or at the global level with a relatively coarse 5–10 km resolution. The main objective of the present paper was to develop a modelling approach at an intermediate scale in order to identify factors that influence spatial patterns of urban expansion in Africa. Boosted Regression Tree models were developed to predict the spatial pattern of rural-urban conversions in every large African city. Urban change data between circa 1990 and circa 2000 available for 20 large cities across Africa were used as training data. Results showed that the urban land in a 1 km neighbourhood and the accessibility to the city centre were the most influential variables. Results obtained were generally more accurate than results obtained using a distance-based urban expansion model and showed that the spatial pattern of small, compact and fast growing cities were easier to simulate than cities with lower population densities and a lower growth rate. The simulation method developed here will allow the production of spatially detailed urban expansion forecasts for 2020 and 2025 for Africa, data that are increasingly required by global change modellers. |
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| Keywords: | Africa Urban growth Modelling Spatial pattern Boosted regression trees |
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