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Review
. 2006:62:119-56.
doi: 10.1016/S0065-308X(05)62004-0.

Determining global population distribution: methods, applications and data

D L Balk  1 , U Deichmann, G Yetman, F Pozzi, S I Hay, A Nelson
Affiliations
Review

Determining global population distribution: methods, applications and data

D L Balk et al. Adv Parasitol. 2006.

Abstract

Evaluating the total numbers of people at risk from infectious disease in the world requires not just tabular population data, but data that are spatially explicit and global in extent at a moderate resolution. This review describes the basic methods for constructing estimates of global population distribution with attention to recent advances in improving both spatial and temporal resolution. To evaluate the optimal resolution for the study of disease, the native resolution of the data inputs as well as that of the resulting outputs are discussed. Assumptions used to produce different population data sets are also described, with their implications for the study of infectious disease. Lastly, the application of these population data sets in studies to assess disease distribution and health impacts is reviewed. The data described in this review are distributed in the accompanying DVD.

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Figures

Figure 1
Figure 1
Administrative level used per country.
Figure 2
Figure 2
Grid cell size in relationship to administrative boundaries, Dominican Republic.
Plate 4.3
Plate 4.3
Process by which GRUMP population surface is constructed, illustrated for southern Ghana. Panel 1 shows inputs side by side with their population counts. Panel 1A is identical to the inputs to GPW, panel 1B shows the additional urban areas used in GRUMP. In panel 2, the inputs are merged, first illustrated as an overlay of the urban footprints over the administrative polygons in panel 2A, and the final grid, in panel 2B (with administrative and urban) boundaries overlaid (density/square km).
Figure 4
Figure 4
The computation of accessibility potential for a single node on the transport network where four towns are within the chosen travel time threshold.
Figure 5
Figure 5
Cumulative percent of the African population represented by mean spatial resolution (MSR) (i.e., for version 4, 60% of the population is represented by an MSR of 50 or better).
Figure 6
Figure 6
Graph of error structure by administrative level for the five large area public-domain human population distribution surfaces (see Hay et al., 2005b). Left axis is the root mean square error expressed as a percentage of the mean population size of the administrative level.
Plate 4.7
Plate 4.7
Spatial Lorenz curve for the population distribution vis-à-vis the land area of Ecuador, 2000 (with insert indicating the non-cumulative distribution of population density).
See this image and copyright information in PMC

References

    1. Adams J. A population map of West Africa. Graduate School of Geography; London School of Economics; London: 1968. Discussion paper no. 26.
    1. Astrom K, Cohen JE, Willett-Brozick JE, Aston CE, Baysal BE. Altitude is a phenotypic modifier in hereditary paraganglioma type 1: evidence for an oxygen-sensing defect. Human Genetics. 2003;113:228–237. - PubMed
    1. Balk D, Pullum T, Storeygard A, Greenwell F, Neuman M. A spatial analysis of childhood mortality in West Africa. Population, Space and Place. 2004b;10:175–216.
    1. Balk D, Pozzi F, Yetman G, Deichmann U, Nelson A. The distribution of people and the dimension of place: methodologies to improve the global estimation of urban extents. Proceedings of the Urban Remote Sensing Conference (of the International Society for the Photogrammetry and Remote Sensing); Tempe, Arizona: International Society for Photogrammetry and Remote Sensing; 2005a. publisher?
    1. Balk D, Levy M, Storeygard A, Gaskell J, Sharma M, Flor R. Child hunger in the developing world: an analysis of environmental and social correlates. Food Policy. 2005b;30:584–611.

Publication types

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