The use of Global Positioning Systems (GPS) and Geographical Information Systems (GIS) in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models.Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites.In this paper we discuss the deficiencies of existing spatial population datasets and their limitations on epidemiological analyses. We review sources of detailed, contemporary, freely available and relevant spatial demographic data focusing on low income regions where such data are often sparse and highlight the value of incorporating these through a set of examples of their application in disease studies. Moreover, the importance of acknowledging, measuring, and accounting for uncertainty in spatial demographic datasets is outlined. Finally, a strategy for building an open-access database of spatial demographic data that is tailored to epidemiological applications is put forward.

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Appraisal of urbanization trends is limited by the lack of a globally consistent definition of what is meant by urban. This article seeks to identify and explain differences in the definition of “urbanness” as used in two largely distinct research communities. We compare the Global Rural–Urban Mapping Project (GRUMP), which defines urban areas based primarily on satellite imagery of nighttime lights, to the urban classification found in Demographic and Health Surveys (DHS), which relies on the urban definitions of individual countries' national statistical offices. We analyze the distribution of DHS clusters falling within and outside of GRUMP urban extents and examine select characteristics of these clusters (notably, household electrification). Our results show a high degree of agreement between the two data sources on what areas are considered urban; furthermore, when used together, GRUMP and DHS data reveal urban characteristics that are not evident when one data source is used independently. GRUMP urban extents are overwhelmingly medium and large highly electrified localities. DHS clusters that are classified as non‐urban but that fall within GRUMP extents tend to be peri‐urban areas.

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