Modelling spatial patterns of urban growth in Africa.
Modelling spatial patterns of urban growth in Africa.
复制标题
非洲城市增长的空间模式建模。
DOI：
10.1016/j.apgeog.2013.07.009
复制
发表时间：
2013-10
影响因子：
4.9
通讯作者：
Gilbert, Marius
中科院分区：
文献类型：
--
作者：
Linard, Catherine;Tatem, Andrew J.;Gilbert, Marius
预计非洲人口将在今后40年翻一番，推动城市扩张速度异常之高，从而引起重大的社会经济、环境和健康变化。为了应对这些变化，必须更好地了解非洲的城市增长动态，更好地预测城乡转换的空间格局。以前关于城市扩展的工作是在城市一级或全球一级进行的，分辨率相对较粗，为5-10公里。本论文的主要目标是发展一种中等尺度的建模方法，以便确定影响非洲城市扩张空间格局的因素。开发了增强回归树模型来预测非洲各大城市城乡转换的空间格局。非洲20个大城市1990年至2000年前后的城市变化数据被用作培训数据。结果表明，城市周边1 km范围内的城市用地和城市中心的可达性是影响城市发展的主要因素。结果表明，与人口密度低、增长率低的城市相比，小型、紧凑和快速增长城市的空间格局更容易模拟。这里开发的模拟方法将允许对非洲2020年和2025年的城市扩张进行详细的空间预测，全球变化建模者越来越需要这些数据。
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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影响因子：
2
作者：
Friedman, JH;Meulman, JJ
通讯作者：
Meulman, JJ
影响因子：
13.7
作者：
Burchfield, Marcy;Overman, Henry G.;Turner, Matthew A.
通讯作者：
Turner, Matthew A.
影响因子：
7.1
作者：
Braimoh, Ademola K.;Onishi, Takashi
通讯作者：
Onishi, Takashi
影响因子：
4.8
作者：
Elith, J.;Leathwick, J. R.;Hastie, T.
通讯作者：
Hastie, T.
影响因子：
1.8
作者：
Friedman, JH
通讯作者：
Friedman, JH