A multi-dimensional framework for measuring urban inclusiveness and crime using mobility traces: A socio-spatial study of Lisbon

Abstract

Urban inclusiveness and safety are shaped by daily interactions between residents, visitors, and the built environment. Capturing these socio-spatial dynamics requires fine-grained data beyond traditional census or survey-based approaches. This study proposes an analytical framework to measure inclusiveness based on the spatial mixing of residents and visitors, combining anonymized mobile phone data with crime records, Points of Interest (POIs), and socioeconomic indicators. The approach integrates spatial statistics, regression models, and association rule mining to explore relationships between inclusiveness, socioeconomic structure, urban functionality, and crime patterns. Results indicate that non-inclusive areas are more prevalent in higher socioeconomic zones and are associated with higher levels of opportunity crimes. In contrast, inclusive areas exhibit greater functional diversity and visitor presence. Locations identified as problematic by official sources also tend to coincide with higher crime counts and lower inclusiveness, suggesting a spatial correspondence between perceived and observed insecurity. Overall, the findings highlight the role of urban functionality and mobility flows in shaping patterns of exposure to crime. By combining mobility data with multi-source urban indicators, the study provides a data-driven framework to explore socio-spatial segregation and its relationship with urban safety.