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

• URL: http://arxiv.org/abs/2505.02938v1
• Date: Mon, 05 May 2025 18:13:22 GMT
• Title: Urban Forms Across Continents: A Data-Driven Comparison of Lausanne and Philadelphia
• Authors: Arthur Carmès, Léo Catteau, Andrew Sonta, Arash Tavakoli,
• Abstract summary: This study presents a data-driven framework to identify and compare urban typologies across geographically and culturally distinct cities.<n>We extracted multidimensional features related to topography, multimodality, green spaces, and points of interest for the cities of Lausanne, Switzerland, and Philadelphia, USA.<n>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.
• Score: 7.693465097015469
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Collaborative Imputation of Urban Time Series through Cross-city Meta-learning [54.438991949772145]
  We propose a novel collaborative imputation paradigm leveraging meta-learned implicit neural representations (INRs)<n>We then introduce a cross-city collaborative learning scheme through model-agnostic meta learning.<n>Experiments on a diverse urban dataset from 20 global cities demonstrate our model's superior imputation performance and generalizability.
  arXiv  Detail & Related papers  (2025-01-20T07:12:40Z)
• StreetviewLLM: Extracting Geographic Information Using a Chain-of-Thought Multimodal Large Language Model [12.789465279993864]
  Geospatial predictions are crucial for diverse fields such as disaster management, urban planning, and public health. We propose StreetViewLLM, a novel framework that integrates a large language model with the chain-of-thought reasoning and multimodal data sources. The model has been applied to seven global cities, including Hong Kong, Tokyo, Singapore, Los Angeles, New York, London, and Paris.
  arXiv  Detail & Related papers  (2024-11-19T05:15:19Z)
• Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks [82.82866901799565]
  We build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task. Beyond the single city, we propose a high-resolution domain adaptation network, HighDAN, to promote the AI model's generalization ability from the multi-city environments. HighDAN is capable of retaining the spatially topological structure of the studied urban scene well in a parallel high-to-low resolution fusion fashion.
  arXiv  Detail & Related papers  (2023-09-26T23:55:39Z)
• Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark] [78.05103666987655]
  This work addresses challenges in accessing and utilizing diverse urban spatial-temporal datasets. We introduceatomic files, a unified storage format designed for urban spatial-temporal big data, and validate its effectiveness on 40 diverse datasets. We conduct extensive experiments using diverse models and datasets, establishing a performance leaderboard and identifying promising research directions.
  arXiv  Detail & Related papers  (2023-08-24T16:20:00Z)
• A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection [68.84486900183853]
  We build an urban region graph (URG) to model the urban area in a hierarchically structured way. Then, we design a novel contextual master-slave framework to effectively detect the urban village from the URG. The proposed framework can learn to balance the generality and specificity for UV detection in an urban area.
  arXiv  Detail & Related papers  (2022-11-26T18:17:39Z)
• Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer [58.6106391721944]
  Cross-city knowledge has shown its promise, where the model learned from data-sufficient cities is leveraged to benefit the learning process of data-scarce cities. We propose a model-agnostic few-shot learning framework for S-temporal graph called ST-GFSL. We conduct comprehensive experiments on four traffic speed prediction benchmarks and the results demonstrate the effectiveness of ST-GFSL compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-05-27T12:46:52Z)
• Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT) [0.0]
  We propose heterogeneous urban graph attention network (HUGAT) for learning the representations of urban regions. In our experiments on NYC data, HUGAT outperformed all the state-of-the-art models.
  arXiv  Detail & Related papers  (2022-02-18T04:59:20Z)
• Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities [7.148078723492643]
  We propose using a neural embedding model-graph neural network (GNN)- that leverages the heterogeneous features of urban areas. Using large-scale high-resolution mobility data sets from millions of aggregated and anonymized mobile phone users in 16 metropolitan counties in the United States, we demonstrate that our embeddings encode complex relationships among features related to urban components. We show that embeddings generated by a model trained on a different county can capture 50% to 60% of the emergent spatial structure in another county.
  arXiv  Detail & Related papers  (2021-10-24T07:13:14Z)
• Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
  We present a method for numerical taxonomy of urban form derived from biological systematics. We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form. After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
  arXiv  Detail & Related papers  (2021-04-30T12:47:52Z)
• City limits in the age of smartphones and urban scaling [0.0]
  Urban planning still lacks appropriate standards to define city boundaries across urban systems. ICT provide the potential to portray more accurate descriptions of the urban systems. We apply computational techniques over a large volume of mobile phone records to define urban boundaries.
  arXiv  Detail & Related papers  (2020-05-06T17:31:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.