High Resolution Population Maps for Low Income Nations: Combining Land Cover and Census in East Africa

被引:144
作者
Tatem, Andrew J. [1 ,2 ]
Noor, Abdisalan M. [2 ]
von Hagen, Craig [3 ]
Di Gregorio, Antonio [4 ]
Hay, Simon I. [1 ,2 ]
机构
[1] Univ Oxford, Dept Zool, Spatial Ecol & Epidemiol Grp, Oxford OX1 3PS, England
[2] Univ Oxford, Wellcome Trust Collaborat Programme, Malaria Publ Hlth & Epidemiol Grp, Ctr Geog Med,Kenya Med Res Inst KEMRI, Nairobi, Kenya
[3] Food & Agr Org United Nations, Somali Water & Land Informat Management Project, Nairobi, Kenya
[4] Food & Agr Org United Nations, Global Land Cover Network, Rome, Italy
来源
PLOS ONE | 2007年 / 2卷 / 12期
基金
英国惠康基金;
关键词
D O I
10.1371/journal.pone.0001298
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background. Between 2005 and 2050, the human population is forecast to grow by 2.7 billion, with the vast majority of this growth occurring in low income countries. This growth is likely to have significant social, economic and environmental impacts, and make the achievement of international development goals more difficult. The measurement, monitoring and potential mitigation of these impacts require high resolution, contemporary data on human population distributions. In low income countries, however, where the changes will be concentrated, the least information on the distribution of population exists. In this paper we investigate whether satellite imagery in combination with land cover information and census data can be used to create inexpensive, high resolution and easily-updatable settlement and population distribution maps over large areas. Methodology/Principal Findings. We examine various approaches for the production of maps of the East African region (Kenya, Uganda, Burundi, Rwanda and Tanzania) and where fine resolution census data exists, test the accuracies of map production approaches and existing population distribution products. The results show that combining high resolution census, settlement and land cover information is important in producing accurate population distribution maps. Conclusions. We find that this semi-automated population distribution mapping at unprecedented spatial resolution produces more accurate results than existing products and can be undertaken for as little as $0.01 per km(2). The resulting population maps are a product of the Malaria Atlas Project (MAP: http://www.map.ox.ac.uk) and are freely available.
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页数:8
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