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Global gridded multi-temporal datasets to support human population distribution modelling [version 1; peer review: awaiting peer review]

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Názov: Global gridded multi-temporal datasets to support human population distribution modelling [version 1; peer review: awaiting peer review]
Autori: Tom McKeen, Dorothea Woods, Rhorom Priyatikanto, Alexander Cunningham, Alessandro Sorichetta, Andrew J. Tatem, Maksym Bondarenko
Zdroj: Gates Open Research, Vol 9 (2025)
Informácie o vydavateľovi: F1000 Research Ltd, 2025.
Rok vydania: 2025
Zbierka: LCC:Medicine
Predmety: spatial demography, geospatial covariates, high-resolution gridded data, human population, subnational, global, spatial dataset, multi-temporal, eng, Medicine
Popis: Population distributions across countries and regions exhibit significant spatial and temporal variability. This variation highlights the need for high-resolution, small-area demographic data to address the challenges posed by shifting population dynamics, urbanization, and migration. Small area population modelling, particularly the production of gridded population estimates, has advanced rapidly over the past decade. Gridded population estimates rely heavily on the availability of detailed geospatial ancillary datasets to capture, inform and explain the variabilities in population densities and distributions at small area scales, enabling the disaggregation from areal unit-based counts. Here we describe an extensive geospatial collection of annual, high resolution, spatio-temporally harmonised, global datasets aimed at driving improvements in mapping small area population density variation. This article presents the spatio-temporal harmonisation process that results in an open access repository of 73 individual gridded datasets addressing topography, climate, nighttime lights, land cover, inland water, infrastructure, protected areas as well as the built-up environment on a global level at a spatial resolution of 3 arc-seconds (approximately 100 metres). Datasets are available as annual time series from 2015 up to and including at least 2020, and as recent as 2023 where source datasets allow. Such datasets not only support population modelling but also applications across environmental, economic, and health sectors, supporting informed policy-making and resource allocation for sustainable development.
Druh dokumentu: article
Popis súboru: electronic resource
Jazyk: English
ISSN: 2572-4754
Relation: https://gatesopenresearch.org/articles/9-72/v1; https://doaj.org/toc/2572-4754
DOI: 10.12688/gatesopenres.16363.1
Prístupová URL adresa: https://doaj.org/article/2dff5959d0b24b2da9b6d26dcb4764c4
Prístupové číslo: edsdoj.2dff5959d0b24b2da9b6d26dcb4764c4
Databáza: Directory of Open Access Journals
Popis
Abstrakt:Population distributions across countries and regions exhibit significant spatial and temporal variability. This variation highlights the need for high-resolution, small-area demographic data to address the challenges posed by shifting population dynamics, urbanization, and migration. Small area population modelling, particularly the production of gridded population estimates, has advanced rapidly over the past decade. Gridded population estimates rely heavily on the availability of detailed geospatial ancillary datasets to capture, inform and explain the variabilities in population densities and distributions at small area scales, enabling the disaggregation from areal unit-based counts. Here we describe an extensive geospatial collection of annual, high resolution, spatio-temporally harmonised, global datasets aimed at driving improvements in mapping small area population density variation. This article presents the spatio-temporal harmonisation process that results in an open access repository of 73 individual gridded datasets addressing topography, climate, nighttime lights, land cover, inland water, infrastructure, protected areas as well as the built-up environment on a global level at a spatial resolution of 3 arc-seconds (approximately 100 metres). Datasets are available as annual time series from 2015 up to and including at least 2020, and as recent as 2023 where source datasets allow. Such datasets not only support population modelling but also applications across environmental, economic, and health sectors, supporting informed policy-making and resource allocation for sustainable development.
ISSN:25724754
DOI:10.12688/gatesopenres.16363.1