Modelling spatial patterns of urban growth in Africa
Modelling spatial patterns of urban growth in Africa
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.
Africa, urban growth, modelling, spatial pattern, boosted regression trees
23-32
Linard, Catherine
231a1de7-72c2-4dc1-bc4e-ea30ed444856
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Gilbert, Marius
c7b7a250-9ec8-47ea-8f08-3b847f0c576c
October 2013
Linard, Catherine
231a1de7-72c2-4dc1-bc4e-ea30ed444856
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Gilbert, Marius
c7b7a250-9ec8-47ea-8f08-3b847f0c576c
Linard, Catherine, Tatem, Andrew J. and Gilbert, Marius
(2013)
Modelling spatial patterns of urban growth in Africa.
Applied Geography, 44, .
(doi:10.1016/j.apgeog.2013.07.009).
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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Published date: October 2013
Keywords:
Africa, urban growth, modelling, spatial pattern, boosted regression trees
Organisations:
Global Env Change & Earth Observation, WorldPop, PHEW – S (Spatial analysis and modelling), Population, Health & Wellbeing (PHeW)
Identifiers
Local EPrints ID: 355455
URI: http://eprints.soton.ac.uk/id/eprint/355455
ISSN: 0143-6228
PURE UUID: fd0f3fc1-0946-431b-bb07-c941970cb05c
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Date deposited: 28 Aug 2013 15:10
Last modified: 15 Mar 2024 03:43
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Author:
Catherine Linard
Author:
Marius Gilbert
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