GUNSA: Global gridded urban natural-space accessibility for 2000-2020 and SSP-RCP scenarios through 2100

This dataset provides global gridded estimates of potential accessibility to urban natural spaces for the historical period 2000–2020 and under four future SSP–RCP scenarios through 2100. Accessibility is expressed as square meters of accessible natural space per person (m²/person) and was calculated using a Gaussian two-step floating catchment area (Gaussian 2SFCA) method. The historical dataset contains annual accessibility estimates from 2000 to 2020 at a 300 m spatial resolution, covering 189 countries. The future dataset provides estimates for 2030, 2050, 2070, and 2100 at a 1 km spatial resolution, covering 183 countries under SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. Five potential walking-distance thresholds are included: 1,800 m, 1,900 m, 2,000 m, 2,100 m, and 2,500 m. The data are distributed as country-level GeoTIFF files in the Equal Earth projected coordinate system (EPSG:8857). Historical and future products are organized by period, scenario, distance threshold, year, and country. Each country was processed independently using its national boundary. Consequently, raster grids of neighboring countries are not necessarily aligned, and cross-border access to natural spaces was not considered. Therefore, the country-level files should not be mosaicked directly without first defining a common target grid and applying an appropriate resampling procedure. -guss_accessibility.py: Historical period and SSP-RCP universal Gaussian 2SFCA calculation. -raster_health_check.py: Grid complete inspection, output file by file CSV and summary JSON. -Coding_README.md: Concentrate on introducing the parameters, directory structure, five distance scenarios, historical and future operating methods, output rules, and physical examination standards involved in the script. Historical estimates were derived from ESA land-use data and WorldPop population data. Future estimates were derived from published 1 km land-use projections under SSP–RCP scenarios (https://doi.org/10.6084/m9.figshare.23542860) and from global population projections under the SSPs (https://doi.org/10.6084/m9.figshare.19608594.v2). Country and city boundaries were obtained from the FAO Global Administrative Unit Layers (GAUL) dataset. The release includes data documentation and calculation scripts to support interpretation, file identification, integrity verification, and reproducibility. Version: 1.0.0