Herfort, B., Lautenbach, S., Porto de Albuquerque, J. et al. A spatio-temporal analysis investigating completeness and inequalities of global urban building data in OpenStreetMap. Nat Commun

Objective:

• Investigate building data completeness and inequality in OSM on the global scale for 13,189 urban centres around the world

Case:

• Global urban centres

Methodology:

• Random forest
• Generalized additive models
• K-means

Data Source

• GHS-UCDB
• Building in OSM and Microsoft
• Geo-Wiki
• Population
• Road length
• Waterbody
• Fossil fuel consumption

Findings:

• For 14% cities, OSM building footprint data exceed 80% completeness, while 69% cities do not reach 20% completeness

• Humanitarian mapping activities in urban centres contributed an average of about 10% of the building footprint globally

• Size of population was positively correlated to completeness

• Spatially clustered completeness pattern became less intense

Coding Reference:

• Python