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osmnx-urban

Generate urban street network analytics from OpenStreetMap as NetworkX graphs.

33|6|Updated Mar 17, 2026
Search Tags:#centrality#osmnx#openstreetmap#isochrone#walkability#network statistics#urban street network
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill osmnx-urban
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: osmnx-urban
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/18-urban-science/osmnx-urban
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill osmnx-urban

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires osmnx, networkx, numpy, pandas, geopandas, matplotlib, shapely, scipy, json, warnings.

What problem does it solve?

This Skill solves the problem of turning OpenStreetMap street data into measurable urban network insights for planning, research, and comparative analysis.

Core Features & Use Cases

  • Download and project OSM street graphs: Build walk/drive/bike networks for a named place or around a lat/lon point using OSMnx, then project to enable distance/area computations.
  • Compute network statistics and spatial metrics: Produce node/edge counts, density indicators, average block lengths, and circuity for quantitative city comparisons.
  • Perform centrality, isochrone, POI, and routing analyses: Compute centrality measures, generate walkability isochrone polygons, measure nearest-POI accessibility, and support shortest-path workflows.
  • Use Case: Compare multiple cities’ walkability by generating 5- and 10-minute isochrone areas from downtown centers and relating them to network density and circuity.

Quick Start

Ask your AI to download walking networks for five city centers, compute extended network statistics for each city, and generate 5- and 10-minute isochrones for walkability comparison.

Frequently Asked Questions about osmnx-urban

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze urban street networks from OpenStreetMap using Python?▼

Analyze urban street networks from OpenStreetMap by downloading and projecting street graphs with OSMnx, then modeling them as NetworkX graphs to compute metrics like density, circuity, and centrality.

How do I generate walkability isochrones for a city center?▼

Generate walkability isochrones by downloading a walking network from OpenStreetMap, projecting it, and calculating reachable node polygons based on edge lengths to map 5- or 10-minute walk areas.

Can I compare network statistics and walkability across multiple cities?▼

You can compare multiple cities by downloading consistent network types for each location, calculating extended network statistics, and generating isochrones to evaluate relative walkability and density.

Does this approach require projecting OpenStreetMap graphs before computing spatial metrics?▼

Projecting OpenStreetMap graphs is required before computing spatial metrics, as it transforms geographic coordinates into a local projected coordinate system for accurate distance and area calculations.

What is the best way to calculate street network centrality and nearest POI accessibility?▼

Calculate street network centrality and nearest POI accessibility by modeling the projected OSMnx graph in NetworkX and using GeoPandas and Shapely for deterministic spatial computations.

What Python libraries do I need to compute network statistics and edge lengths?▼

Computing network statistics and edge lengths requires Python libraries including OSMnx for graph acquisition, NetworkX for graph modeling, and GeoPandas, NumPy, and SciPy for spatial calculations.

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