Redefining population enumeration based on bottom-up mapping

The distribution of human populations is the most important piece of information required for understanding many aspects of population health and wellbeing. Yet in low-income countries, such fundamental information is often out-dated or inaccurate, frequently due to the prohibitive cost of conducting a full population census. In addition, traditional census methods often miss out certain kinds of populations such as Nomads or displaced peoples. Given this, alternate methods for delineating population coverage and density are needed, particularly where such methods can be more responsive to population change at shorter time intervals. The current availability of frequently-updated, high-resolution satellite imagery as well as machine-assisted image processing methods mean that it is now feasible to delineate areas of human settlement at high spatial resolution. Furthermore, the increasing prevalence of nationally-representative household surveys in low-income countries means that measures of population density can be collected on a regular basis. By taking advantage of the fact that population density often correlates well with spatial and environmental characteristics it is possible to model variation in sampled population density at fine spatial scales. Such population modelling can be combined with settlement extractions to create high-resolution population density estimates that 1) can be updated when new imagery and survey data become available and 2) can be adapted to non-standard populations such as Nomadic or displaced peoples. This project will work to develop methods for creating fast and accessible population density estimates that can be used to supplement or augment national census methods in scenarios where a full enumeration is not feasible.

The WorldPop research group: The WorldPop group is in the department of geography and environment and works in partnership with our sister group, the Flowminder Foundation. We are a dynamic and productive research team focussed providing a geographic evidence base in aid of the world’s vulnerable populations. We work with governments and development agencies to produce high-impact, operationally relevant analytics to help solve some of the world’s most pressing problems.

Candidates must have a first or strong upper second class degree in Geography, Environmental Science, or a closely related subject. In addition, an MSc in an associated subject would be advantageous. Experience of using GIS and remote sensing is required, as is a strong knowledge of statistics. Experience of programming in R is desirable but not essential as training can be provided.

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