ArticleBig earth data2019
Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets.
Article in Big earth data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers.
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Who cites it
78 citing papers in PubMed.
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- Climate Change Favors African Malaria Vector Mosquitoes.Global change biology · 2025Article
- Dynamic data visualizations as events: effects of framing and change salience on segmenting dynamic maps.Cognitive research: principles and implications · 2025Article
18 more citing papers are in PubMed but not listed here.
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14 authors.
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Abstract
Multi-temporal, globally consistent, high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health, wealth, and resource access, and monitoring change in these over time. The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multi-temporal scales. This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas. In response to these agendas, a method has been developed to assemble and harmonise a unique, open access, archive of geospatial datasets. Datasets are provided as global, annual time series, where pertinent at the timescale of population analyses and where data is available, for use in the construction of population distribution layers. The archive includes sub-national census-based population estimates, matched to a geospatial layer denoting administrative unit boundaries, and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density. Here, we describe these harmonised datasets and their limitations, along with the production workflow. Further, we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics. The geospatial archive is available at https://doi.org/10.5258/SOTON/WP00650.
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