2.2. Other relevant global geospatial datasets for SDGs

Global HRLC data are also used in combination with complementary global geospatial information demonstrating their capabilities of directly supporting the computation of indicators e.g. from SDG 2 [1], 6 [2], and 9 [3].

Actually, land cover is only one of the Global Fundamental Geospatial Data Themes to SDGs identified by the United Nations. These Fundamental Themes include a variety of geospatial datasets which are necessary to spatially represent key phenomena and objects for the realization of economic, social, and environmental targets identified by the SDGs. These datasets include - among others - demography, soil/vegetation parameters, administrative boundaries, infrastructures, transport networks, 2D/3D topographic maps, water bodies, etc.

The suitability (fit-for-purpose) of complementary global geospatial datasets is difficult to assess globally for each specific SDG indicator or country. Spatial and temporal resolutions, thematic details and accuracies should be evaluated case-by-case, using recommendations valid also for the global HRLC data, and their use should be functional to the provision of baseline data in support of national SDGs reporting.

Table 2.2.1 contains information on popular global open geospatial datasets which might be employed in combination with global HRLC data for the computation of land cover-connected SDG indicators.

Table 2.2.1 Selection of popular global open geospatial datasets, complementary to global HRLC data, relevant to SDG indicators computation

Global HRLC Dataset	Temporal Coverage	Spatial Resolution	Thematic detail	Notes
World Bank Official Boundaries	Annual	/	Administrative boundaries (and polygons) including international boundaries, disputed areas, coastlines, lakes	-Provider: World Bank -Access: https://datacatalog.worldbank.org/search/dataset/0038272 -CRS: WGS84 -Format: vector
WorldPop Population Counts	2000-2020 (every year)	Up to 100 m (3 arc seconds)	Population	-Provider: WorldPop - University of Southampton -Access: https://hub.worldpop.org/project/categories?id=3 -CRS: WGS84 -Format: raster (Geotiff, ASCII XYZ)
Global Human Settlement population (GHS-POP)	1975-2030 (every 5 years)	Up to 100 m (3 arc seconds)	Population	-Provider: European Commission, Joint Research Centre -Access: https://ghsl.jrc.ec.europa.eu/download.php?ds=pop -CRS: Mollweide -Format: raster
GHS Urban Centre Database (GHS-UCDB)	2015, 2019	1 km (30 arc seconds)	Urban centers polygons	-Provider: European Commission, Joint Research Centre -Access: https://ghsl.jrc.ec.europa.eu/download.php?ds=ucdb -CRS: Mollweide -Format: vector (GeoPackage)
OpenStreetMap	/	/	Multiple (including transport networks)	-Provider: OpenStreetMap -Access: https://www.openstreetmap.org (provider’s portal, login requested); http://download.geofabrik.de; https://overpass-turbo.eu (additional providers) -CRS: WGS84 -Format: vector

Footnotes

[1]

Radwan, T. M., Blackburn, G. A., Whyatt, J. D., & Atkinson, P. M. (2021). Global land cover trajectories and transitions. Scientific reports, 11(1), 1-16.

[2]

Ilie, C. M., Brovelli, M. A., & Coetzee, S. (2019). Monitoring SDG 9 with Global Open Data and Open Software–A Case Study from Rural Tanzania. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1551-1558.

[3]

Mulligan, M., van Soesbergen, A., Hole, D. G., Brooks, T. M., Burke, S., & Hutton, J. (2020). Mapping nature’s contribution to SDG 6 and implications for other SDGs at policy relevant scales. Remote Sensing of Environment, 239, 111671.