Mapping Urban Segregation with GeoAI: Street View Perceptions and Socio-Spatial Inequality in Thessaloniki, Greece

This study examines the statistical and spatial alignment between urban place perceptions and the census-based evidence of socio-spatial segregation. We process a large dataset of geotagged images from Mapillary and KartaView with ZenSVI to score six place perception dimensions (safety, liveliness, wealth, beauty, boredom, depression) for the metropolitan area of Thessaloniki, Greece. The socio-economic structure is derived from census indicators and property values using Location Quotients and principal component analysis. We assess alignment through Pearson’s correlation (r) to capture statistical association, and bivariate Moran’s I to test spatial correspondence while accounting for spatial dependence. Results reveal a robust northwest–southeast divide: southeastern and central districts are perceived as safer, livelier, wealthier, and more beautiful, while northwestern and industrial zones score higher on boredom and depression. The historic city center emerges as vibrant and affluent, acting as a key interface between social groups, especially students, the elderly, and migrants. Perceptual dimensions vary in spatial form: safety, beauty, and depression cluster locally, whereas wealth and vibrancy extend over broader sectors. The study demonstrates the combined use of perceptual and socio-economic data for urban analysis and provides a replicable framework for monitoring inequalities and guiding participatory and inclusive planning.