Urban forms across continents: A data-driven comparison of Lausanne and Philadelphia

Understanding urban form is crucial for sustainable urban and transportation planning, and for enhancing quality of life. This study presents a data-driven framework to systematically identify and compare urban forms across geographically and culturally distinct cities. Using open-source geospatial data from OpenStreetMap, we extracted multidimensional features related to network structure, multimodality, green spaces, and points of interest for the cities of Lausanne, Switzerland, and Philadelphia, USA. A grid-based approach was used to divide each city into Basic Spatial Units (BSU), and Gaussian Mixture Models (GMM) were applied to cluster BSUs based on their urban characteristics. The results reveal coherent and interpretable urban forms within each city, with some cluster types emerging across both cities despite their differences in scale, density, and cultural context. Comparative analysis showed that adapting the grid size to each city’s forms improves the detection of shared typologies. Simplified clustering based solely on network degree centrality further demonstrated that meaningful structural patterns can be captured even with minimal feature sets. Our findings suggest the presence of functionally convergent urban forms across continents and highlight the importance of spatial scale in cross-city comparisons. The framework offers a scalable and transferable approach for urban analysis and transportation planning providing valuable insights for planners and policymakers aiming to enhance various aspects of cities including walkability, accessibility, and well-being. Limitations related to data completeness and feature selection are discussed, and directions for future work — including the integration of additional data sources and human-centered validation — are proposed. • This study presents a scalable, open-source framework to classify urban forms. • Case studies in Philadelphia and Lausanne reveal shared urban typologies. • Neighborhood clusters reflect walkability, green space, and transport access. • The results show that urban context strongly shapes cluster interpretation.