mlsanityData to 14 Oct 2026

Modeling and Analyzing Urban Networks and Amenities with OSMnx

arXiv 2505.00736 ↗First posted 30 Apr 2025physics.soc-ph cs.MS cs.SYcited by 25

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Abstract (arXiv)

OSMnx is a Python package for downloading, modeling, analyzing, and visualizing urban networks and any other geospatial features from OpenStreetMap data. A large and growing body of literature uses it to conduct scientific studies across the disciplines of geography, urban planning, transport engineering, computer science, and others. The OSMnx project has recently developed and implemented many new features, modeling capabilities, and analytical methods. The package now encompasses substantially more functionality than was previously documented in the literature. This article introduces OSMnx's modern capabilities, usage, and design -- in addition to the scientific theory and logic underlying them. It shares lessons learned in geospatial software development and reflects on open science's implications for urban modeling and analysis.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Apr 2025.

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Citing papers per year: 2025: 14; 2026: 11.

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  1. 2025Constraint-Aware Route Recommendation from Natural Language via Hierarchical LLM Agentscited by 3
  2. 2025Spatio-Temporal Graph Neural Network for Urban Spaces: Interpolating Citywide Traffic Volumecited by 2
  3. 2025STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictionscited by 2
  4. 2025Metric Graph Kernels via the Tropical Torelli Mapcited by 1
  5. 2025CartoMapQA: A Fundamental Benchmark Dataset Evaluating Vision-Language Models on Cartographic Map Understandingcited by 1
  6. 2025Transport Network, Graph, and Air Pollutioncited by 0
  7. 2025Modern approaches to building interpretable models of the property market using machine learning on the base of mass cadastral valuationcited by 0
  8. 2025Decoding street network morphologies and their correlation to travel mode choicecited by 0
  9. 2025A Cost-Effective Framework for Predicting Parking Availability Using Geospatial Data and Machine Learningcited by 0
  10. 2025Coordinates from Context: Using LLMs to Ground Complex Location Referencescited by 0
  11. 2025Capturing Context-Aware Route Choice Semantics for Trajectory Representation Learningcited by 0
  12. 2025Climate Adaptation with Reinforcement Learning: Economic vs. Quality of Life Adaptation Pathwayscited by 0
13 more
  1. 2025The changing surface of the world's roadscited by 0
  2. 2025Topological Spatial Graph Coarseningcited by 0
  3. 2026Learning long term climate-resilient transport adaptation pathways under direct and indirect flood impacts using reinforcement learningcited by 0
  4. 2026Traffic-Aware Navigation in Road Networkscited by 0
  5. 2026GAMMS: Graph based Adversarial Multiagent Modeling Simulatorcited by 0
  6. 2026Feature-based morphological analysis of shape graph datacited by 0
  7. 2026Improving Spatial Allocation for Energy System Coupling with Graph Neural Networkscited by 0
  8. 2026Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transportcited by 0
  9. 2026Reconstructing Movement from Sparse Samples: Enhanced Spatio-Temporal Matching Strategies for Low-Frequency Datacited by 0
  10. 2026Graph Neural Networks for Community Detection in Graph Signal Analysiscited by 0
  11. 2026Metropolis-Scale Resilient and Trustworthy Traffic Flow Inference Using Multi-Source Datacited by 0
  12. 2026The Harsh Truth: Segment-Level Analysis of Harsh Driving Events in Milan Using Large-Scale Telematics, Street Networks, and Google Street Viewcited by 0
  13. 2026Spatio-temporally complementary feature propagation on graphs for longitudinal AADT estimationcited by 0