This study proposes a scalable, grid-based framework to delineate intra-urban variations in affluence and deprivation across 59 Indian cities using open-source satellite imagery. The method partitions urban areas into high-resolution spatial grids characterized by interpretable building morphology indicators, which are combined into a rule-based scoring system.
- Urban areas across 59 Indian cities and towns are partitioned into high-resolution spatial grids.
- Classes are validated through ground-level Google Street View observations, revealing sharp contrasts consistent with expected affluence effects.
- Density-based clustering of building footprints in Mumbai identifies dense settlements that substantially overlap with known informal settlements.
- An exploratory analysis maps consumer loan delinquency across the derived affluence classes.
The framework provides a scalable, interpretable, and cost-effective approach for granular urban affluence mapping by relying entirely on publicly available geospatial data.