Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Modeling and Analyzing Urban Networks and Amenities with OSMnx

Geoff Boeing

TL;DR

OSMnx provides an open, reproducible toolkit for end-to-end urban-network analysis starting from OpenStreetMap data, enabling researchers to download, model, analyze, and visualize spatial networks and amenities. Built on NetworkX and GeoPandas, its modular v2.0+ architecture articulates a clear API, with 17 public modules supporting geocoding, feature retrieval, network construction, graph transformations, routing, and visualization. Key contributions include graph simplification and node consolidation to better capture real-world intersections, elevation-aware routing, and a comprehensive open-science framework that covers API design, development pipelines, and dependency management. The work demonstrates substantial practical impact by standardizing urban-network workflows, improving reproducibility, and fostering a reusable software commons for geography, urban planning, and transport research.

Abstract

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.

Modeling and Analyzing Urban Networks and Amenities with OSMnx

TL;DR

OSMnx provides an open, reproducible toolkit for end-to-end urban-network analysis starting from OpenStreetMap data, enabling researchers to download, model, analyze, and visualize spatial networks and amenities. Built on NetworkX and GeoPandas, its modular v2.0+ architecture articulates a clear API, with 17 public modules supporting geocoding, feature retrieval, network construction, graph transformations, routing, and visualization. Key contributions include graph simplification and node consolidation to better capture real-world intersections, elevation-aware routing, and a comprehensive open-science framework that covers API design, development pipelines, and dependency management. The work demonstrates substantial practical impact by standardizing urban-network workflows, improving reproducibility, and fostering a reusable software commons for geography, urban planning, and transport research.

Abstract

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.
Paper Structure (24 sections, 3 figures, 1 table)

This paper contains 24 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Street Network Models and Analysis
  3. Model Fundamentals
  4. Street Network Analysis
  5. Street Network Tools and Data
  6. The OSMnx Package
  7. Overview
  8. Modular Organization
  9. Geocoding and Querying
  10. Urban Amenities
  11. Modeling Networks
  12. Graph Simplification
  13. Converting, Projecting, and Saving
  14. Elevation
  15. Map Matching and Routing
  16. ...and 9 more sections

Figures (3)

  • Figure 1: Graph of a town's street network before (left) and after (right) edge simplification. Source: boeing_graph_2025.
  • Figure 2: Polar histograms and corresponding street maps illustrating orientation entropy: low (Chicago), medium (New Orleans), and high (Rome). Histogram bar directions represent street compass bearings and lengths represent relative frequency. Source: barthelemy_review_2024.
  • Figure 3: Figure-ground diagrams of one square mile of each city, allowing us to compare street network form in different places. Source: boeing_spatial_2021.