The accuracy of human population maps for public health application
Article first published online: 13 SEP 2005
Tropical Medicine & International Health
Volume 10, Issue 10, pages 1073–1086, October 2005
How to Cite
Hay, S. I., Noor, A. M., Nelson, A. and Tatem, A. J. (2005), The accuracy of human population maps for public health application. Tropical Medicine & International Health, 10: 1073–1086. doi: 10.1111/j.1365-3156.2005.01487.x
- Issue published online: 13 SEP 2005
- Article first published online: 13 SEP 2005
- areal weighting;
- pycnophylactic interpolation;
- dasymetric mapping;
- smart interpolation
Objectives Human population totals are used for generating burden of disease estimates at global, continental and national scales to help guide priority setting in international health financing. These exercises should be aware of the accuracy of the demographic information used.
Methods The analysis presented in this paper tests the accuracy of five large-area, public-domain human population distribution data maps against high spatial resolution population census data enumerated in Kenya in 1999. We illustrate the epidemiological significance, by assessing the impact of using these different human population surfaces in determining populations at risk of various levels of climate suitability for malaria transmission. We also describe how areal weighting, pycnophylactic interpolation and accessibility potential interpolation techniques can be used to generate novel human population distribution surfaces from local census information and evaluate to what accuracy this can be achieved.
Results We demonstrate which human population distribution surface performed best and which population interpolation techniques generated the most accurate bespoke distributions. Despite various levels of modelling complexity, the accuracy achieved by the different surfaces was primarily determined by the spatial resolution of the input population data. The simplest technique of areal weighting performed best.
Conclusions Differences in estimates of populations at risk of malaria in Kenya of over 1 million persons can be generated by the choice of surface, highlighting the importance of these considerations in deriving per capita health metrics in public health. Despite focussing on Kenya the results of these analyses have general application and are discussed in this wider context.