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Planning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsNatural Language Processing: Lexical Semantics and MorphologySearch and Optimization: Distributed Search
Urban Forms Across Continents: A Data-Driven Comparison of Lausanne and Philadelphia
🤖 AI Summary
This study addresses the challenge of systematically identifying and cross-city comparing urban morphological types across geographically and culturally distinct cities to support sustainable planning and quality-of-life enhancement. We propose a data-driven framework that extracts multi-dimensional features—including topography, transportation networks, green spaces, and points of interest—from OpenStreetMap; applies dynamic adaptive gridding coupled with Gaussian Mixture Modeling (GMM) for unsupervised clustering; and incorporates degree centrality to quantify structural centrality. Empirical analysis of Lausanne and Philadelphia reveals, for the first time, functional convergence in urban morphology across continents. Results demonstrate that dynamic grid scaling significantly improves cross-city comparability, and degree centrality alone robustly captures dominant structural patterns. The framework is scalable and supports planning decisions oriented toward walkability, accessibility, and public health.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Compare urban typologies across geographically distinct cities
Develop data-driven framework for sustainable urban planning
Analyze impact of spatial scale on cross-city comparisons
Innovation
Methods, ideas, or system contributions that make the work stand out.
Open-source geospatial data for urban feature extraction
Grid-based clustering with Gaussian Mixture Models
Adaptive grid size for cross-city typology detection
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