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High-resolution gridded population data are essential for understanding demographic shifts. This study presents harmonized global geospatial datasets to improve small area population density mapping and inform policy.

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Area of Science:

  • Geographic Information Systems (GIS)
  • Demography
  • Environmental Science

Background:

  • Population distributions show significant spatial and temporal variability, necessitating high-resolution demographic data.
  • Shifting population dynamics, urbanization, and migration present challenges for accurate population mapping.
  • Small area population modeling relies on detailed geospatial ancillary datasets for disaggregating areal unit counts.

Purpose of the Study:

  • To describe an extensive collection of harmonized global geospatial datasets.
  • To drive improvements in mapping small area population density variation.
  • To support population modeling and applications in environmental, economic, and health sectors.

Main Methods:

  • Spatio-temporal harmonization of 73 individual gridded datasets.
  • Integration of diverse geospatial data including topography, climate, land cover, and infrastructure.
  • Creation of annual time series datasets at a 3 arc-second (~100m) spatial resolution.

Main Results:

  • An open-access repository of global, high-resolution, spatio-temporally harmonized geospatial datasets has been created.
  • Datasets cover topography, climate, nighttime lights, land cover, inland water, infrastructure, protected areas, and the built-up environment.
  • Annual time series data are available from 2015 to at least 2020, and up to 2023 where possible.

Conclusions:

  • The developed datasets are crucial for enhancing small area population density mapping.
  • These resources support informed policy-making and resource allocation for sustainable development.
  • The datasets facilitate interdisciplinary research across environmental, economic, and health domains.