Spatially disaggregated population estimates in the absence of national population and housing census data

Abstract

Population numbers at local levels are fundamental data for many applications, including the delivery and planning of services, election preparation, and response to disasters. In resource-poor settings, recent and reliable demographic data at subnational scales can often be lacking. National population and housing census data can be outdated, inaccurate, or missing key groups or areas, while registry data are generally lacking or incomplete. Moreover, at local scales accurate boundary data are often limited, and high rates of migration and urban growth make existing data quickly outdated. Here we review past and ongoing work aimed at producing spatially disaggregated local-scale population estimates, and discuss how new technologies are now enabling robust and cost-effective solutions. Recent advances in the availability of detailed satellite imagery, geopositioning tools for field surveys, statistical methods, and computational power are enabling the development and application of approaches that can estimate population distributions at fine spatial scales across entire countries in the absence of census data. We outline the potential of such approaches as well as their limitations, emphasizing the political and operational hurdles for acceptance and sustainable implementation of new approaches, and the continued importance of traditional sources of national statistical data.

Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine

“…35,53 Mobile phone data are particularly promising for analysing travel-related phenomena on a scale previously impossible, providing a 'big data' approach to understanding human mobility and its changes. [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Two types of mobile-based positioning data that have so far been increasingly explored in travel-related studies are call detail records (CDRs) and mobile location history.…”

“…In resource-poor settings, demographic data collected via traditional censuses and surveys at subnational scales can often be lacking or outdated. 18 However, many recent studies have highlighted how our understanding of human mobility across contexts can be significantly improved through quantitative analyses of positioning data from the huge population of mobile phone users. 19,20 In 2017, there were already over 5 billion unique mobile subscribers globally, with a penetration rate of 66% of the global population, and the total number of mobile cellular subscriptions exceeds the world population at 7.79 billion.…”

Variability in urban population distributions across Africa

“…We use five datasets from four providers: WorldPop 2015 by the University of Southampton [36]; LandScan 2015 from Oak Ridge National Lab [37]; WPE 2016 by Esri Inc. [38]; and GHS-Pop 2000 and 2015, produced by the European Union Joint Research Center [39]. All datasets are available at 1 km grid cell, but the methodologies and input data vary [24]. WorldPop establishes spatial weights between areal features and census population estimates by applying a random forest algorithm to a suite of remote-sensed derived land cover classes and GIS layers, such as distance to roads, and environmental data, including elevation and mean temperature, to estimate population counts [40].…”

“…It is unclear how many people live in many of Africa's cities and towns, especially those with fewer than 1 million residents [21][22][23]. For many countries, censuses are infrequently conducted, can be unreliable [24], and most do not provide geolocated municipality-level population counts. At present, United Nations Population Division data is widely used to track urbanization across the continent.…”

“…Since the 1990s, researchers have generated synthetic gridded population datasets to overcome the lack fine-scale population data across the planet. These data sets are available at the continental scale at 1 km spatial resolution and are produced using geospatial modeling techniques that assigns individual pixels a population value based on spatial-covariates derived from remote-sensed imagery and/or auxiliary GIS data [24]. It is not possible to directly estimate the population of individual urban settlements with these data sets alone.…”