# Modeling and Analyzing Urban Networks and Amenities with OSMnx

arXiv: https://arxiv.org/abs/2505.00736 · HTML: https://mlsanity.com/paper/2505.00736/

## Key facts

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

## Built on by (most cited first, 25 of 25)

- [Constraint-Aware Route Recommendation from Natural Language via Hierarchical LLM Agents](https://mlsanity.com/paper/2510.06078/) (2025)
- [Spatio-Temporal Graph Neural Network for Urban Spaces: Interpolating Citywide Traffic Volume](https://mlsanity.com/paper/2505.06292/) (2025)
- [STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions](https://mlsanity.com/paper/2509.10528/) (2025)
- [Metric Graph Kernels via the Tropical Torelli Map](https://mlsanity.com/paper/2505.12129/) (2025)
- [CartoMapQA: A Fundamental Benchmark Dataset Evaluating Vision-Language Models on Cartographic Map Understanding](https://mlsanity.com/paper/2512.03558/) (2025)
- [Transport Network, Graph, and Air Pollution](https://mlsanity.com/paper/2506.01164/) (2025)
- [Modern approaches to building interpretable models of the property market using machine learning on the base of mass cadastral valuation](https://mlsanity.com/paper/2506.15723/) (2025)
- [Decoding street network morphologies and their correlation to travel mode choice](https://mlsanity.com/paper/2507.19648/) (2025)
- [A Cost-Effective Framework for Predicting Parking Availability Using Geospatial Data and Machine Learning](https://mlsanity.com/paper/2508.14125/) (2025)
- [Coordinates from Context: Using LLMs to Ground Complex Location References](https://mlsanity.com/paper/2510.08741/) (2025)
- [Capturing Context-Aware Route Choice Semantics for Trajectory Representation Learning](https://mlsanity.com/paper/2510.14819/) (2025)
- [Climate Adaptation with Reinforcement Learning: Economic vs. Quality of Life Adaptation Pathways](https://mlsanity.com/paper/2511.03243/) (2025)
- [The changing surface of the world's roads](https://mlsanity.com/paper/2512.04092/) (2025)
- [Topological Spatial Graph Coarsening](https://mlsanity.com/paper/2512.24327/) (2025)
- [Learning long term climate-resilient transport adaptation pathways under direct and indirect flood impacts using reinforcement learning](https://mlsanity.com/paper/2601.18586/) (2026)
- [Traffic-Aware Navigation in Road Networks](https://mlsanity.com/paper/2602.02158/) (2026)
- [GAMMS: Graph based Adversarial Multiagent Modeling Simulator](https://mlsanity.com/paper/2602.05105/) (2026)
- [Feature-based morphological analysis of shape graph data](https://mlsanity.com/paper/2602.16120/) (2026)
- [Improving Spatial Allocation for Energy System Coupling with Graph Neural Networks](https://mlsanity.com/paper/2602.22249/) (2026)
- [Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport](https://mlsanity.com/paper/2603.06278/) (2026)
- [Reconstructing Movement from Sparse Samples: Enhanced Spatio-Temporal Matching Strategies for Low-Frequency Data](https://mlsanity.com/paper/2603.09412/) (2026)
- [Graph Neural Networks for Community Detection in Graph Signal Analysis](https://mlsanity.com/paper/2605.19733/) (2026)
- [Metropolis-Scale Resilient and Trustworthy Traffic Flow Inference Using Multi-Source Data](https://mlsanity.com/paper/2605.25004/) (2026)
- [The Harsh Truth: Segment-Level Analysis of Harsh Driving Events in Milan Using Large-Scale Telematics, Street Networks, and Google Street View](https://mlsanity.com/paper/2606.00261/) (2026)
- [Spatio-temporally complementary feature propagation on graphs for longitudinal AADT estimation](https://mlsanity.com/paper/2609.29906/) (2026)

## 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.