High resolution population maps for low income nations: combining land cover and census in East Africa

Abstract

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.

Figure 1. The highest levels of administrative boundaries for which national census data were available for Burundi, Kenya, Rwanda, Tanzania and Uganda.

Figure 2. Kenya enumeration area census data.

The 50 Kenyan districts for which enumeration area census data were available are shaded grey. Within each district the enumeration area boundaries are shown.

Figure 3. Example of settlement mapping.

(a) Landsat ETM false colour composite of bands 2,3 and 4 in red, green and blue respectively showing Kampala, Uganda and surrounding areas, (b) The same image as (a), but with the outlines of mapped settlements overlaid.

Figure 4. RMSE plots of the three population mapping procedures tested for the six different administrative levels of Kenyan census data.

Figure 5. Per-district RMSE plots of the three population mapping procedures.

Figure 6. East Africa region population density estimated using the EApop3 approach.

The spatial resolution has been degraded and vertical exaggeration has been applied for visualisation purposes. The full-resolution close-ups reveal detail for (a) Bujumbura, (b) Kigali, (c) Kampala, (d) Nairobi and (e) Dar Es Salaam.