Urban Forms Across Continents: A Data-Driven Comparison of Lausanne and Philadelphia

TL;DR

This study introduces a scalable, data-driven framework for comparing urban forms across different cities using open-source geospatial data and clustering techniques, revealing shared typologies and structural patterns.

Contribution

It presents a novel methodology combining grid-based spatial units and Gaussian Mixture Models to identify and compare urban typologies across diverse cities.

Findings

Urban typologies are coherent within each city.

Shared urban types emerge across Lausanne and Philadelphia.

Spatial scale significantly affects typology detection.

Abstract

Understanding urban form is crucial for sustainable urban planning and enhancing quality of life. This study presents a data-driven framework to systematically identify and compare urban typologies across geographically and culturally distinct cities. Using open-source geospatial data from OpenStreetMap, we extracted multidimensional features related to topography, 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 typologies 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 morphology 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, providing valuable insights for planners and policymakers aiming to enhance 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.