OSMnx Features Round-Up

OSMnx is a Python package for quickly and easily downloading, modeling, analyzing, and visualizing street networks and other spatial data from OpenStreetMap. Here’s a quick round-up of recent updates to OSMnx. I’ll try to keep this up to date as a single reference source. A lot of new features have appeared in the past few months, and people have been asking about what’s new and what’s possible. You can:

• Download and model street networks or other networked infrastructure anywhere in the world with a single line of code
• Download any other spatial geometries, place boundaries, building footprints, or points of interest as a GeoDataFrame
• Download by city name, polygon, bounding box, or point/address + network distance
• Download drivable, walkable, bikeable, or all street networks
• Download node elevations and calculate edge grades (inclines)
• Impute missing speeds and calculate graph edge travel times
• Simplify and correct the network’s topology to clean-up nodes and consolidate intersections
• Fast map-matching of points, routes, or trajectories to nearest graph edges or nodes
• Save networks to disk as shapefiles, geopackages, and GraphML
• Save/load street network to/from a local .osm xml file
• Conduct topological and spatial analyses to automatically calculate dozens of indicators
• Calculate and visualize street bearings and orientations
• Calculate and visualize shortest-path routes that minimize distance, travel time, elevation, etc
• Visualize street networks as a static map or interactive leaflet web map
• Visualize travel distance and travel time with isoline and isochrone maps
• Plot figure-ground diagrams of street networks and building footprints

Here are some research projects and publications using OSMnx for spatial network analysis:

• OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks
• A Multi-Scale Analysis of 27,000 Urban Street Networks: Every US City, Town, Urbanized Area, and Zillow Neighborhood
• Urban Spatial Order: Street Network Orientation, Configuration, and Entropy
• Planarity and Street Network Representation in Urban Form Analysis
• Spatial Information and the Legibility of Urban Form: Big Data in Urban Morphology
• Street Network Models and Measures for Every U.S. City, County, Urbanized Area, Census Tract, and Zillow-Defined Neighborhood
• Urban Street Network Analysis in a Computational Notebook
• Methods and Measures for Analyzing Complex Street Networks and Urban Form
• Exploring Urban Form Through OpenStreetMap Data: A Visual Introduction
• The Morphology and Circuity of Walkable and Drivable Street Networks

And here are some blog posts running through features, findings, and how-to:

• OSMnx: Python for Street Networks
• What’s New with OSMnx
• What’s New With OSMnx, Part 2
• OSMnx Summer Wrap-Up
• Urban Street Network Orientation
• Square-Mile Street Network Visualization
• Urban Form Figure-Ground Diagrams
• Urban Form Analysis with OpenStreetMap Data
• Urban Street Network Centrality
• Isochrone Maps with OSMnx + Python
• OSMnx and Street Network Elevation Data
• Street Network Orientation
• Comparing City Street Orientations
• City Street Orientations around the World
• New Article: OSMnx in CEUS
• R-tree Spatial Indexing with Python
• Network-Based Spatial Clustering
• New Article: Urban Street Networks in EP-B
• New Chapter: Street Network Morphology
• New Article: Planar Models of Street Networks
• US Street Network Models and Measures
• Street Network Analysis in a Docker Container

Finally, you can check out the OSMnx documentation or the usage examples for more info.