Building(s and) cities: Delineating urban areas with a machine learning algorithm - City & Employment Centre Boundaries (v1)

City and employment centre boundary delineations for Spain (except Basque Country and Navarra), based on building footprint from the Cadastre, and created using the A-DBSCAN algorithm.

GeoJSON polygons expressed in WGS-84 (ie. lon/lat), GeoPackage polygons expressed in ETRS89 / UTM zone 30N (EPSG:25830).

Files included in this dataset:

- sp_blg_adbscan_city_boundaries_v1.geojson: city boundaries in GeoJSON format. Each boundary contains a city ID (city_id in the table) and the number of building footprints it contains (n_buildings in the table).

- sp_blg_adbscan_city_boundaries_v1.gpkg: city boundaries in GeoPackage format. Each boundary contains a city ID (city_id in the table) and the number of building footprints it contains (n_buildings in the table).

- sp_blg_adbscan_emp_centre_boundaries_v1.geojson: employment centre boundaries in GeoJSON format. Each boundary contains a city ID (city_id in the table), an employment centre ID (centre_id in the table) and the number of building footprints it contains (n_buildings in the table).

- sp_blg_adbscan_emp_centre_boundaries_v1.gpkg: employment centre boundaries in GeoPackage format. Each boundary contains a city ID (city_id in the table), an employment centre ID (centre_id in the table) and the number of building footprints it contains (n_buildings in the table).

For further reference, please see original paper:

Arribas-Bel, D.; Garcia-Lopez, M. A.; Viladecans-Marsal, E. (2020). "Building(s and) cities: Delineating urban areas with a machine learning algorithm". Journal of Urban Economics.