Check out the journal article about OSMnx.

How far can you travel on foot in 15 minutes? Urban planners use isochrone maps to show spatial horizons (i.e., isolines) that are equal in time. Isochrones depict areas according to how long it takes to arrive there from some point. These visualizations are particularly useful in transportation planning as they reveal what places are accessible within a set of time horizons.

We can create isochrone maps for anywhere in the world automatically with Python and its OSMnx package:

This travel time map shows how far we can walk in 5, 10, 15, 20, and 25 minutes from an origin point in downtown Berkeley, given an average walking speed of 4.5 km/hour (about 2.8 miles/hour). We can also visualize this by which points along the network we can reach within 5, 10, 15, 20, and 25 minutes:

You can create your own travel time maps for any city in the world by following this example in the OSMnx usage examples GitHub repo. OSMnx is a Python package for easily downloading, analyzing, and visualizing OpenStreetMap street networks anywhere in the world. Installation instructions are on GitHub .

If you’re interested in routing, travel times, and imputing road speeds, make sure to check out OSMnx’s new speed module.

This is magnificent… the easiest open-source implementation of isochrones for street networks that I’ve tried.

I’m trying to figure out if there is a straightforward way of calculating the travel times to each individual node and then exporting these for use in something like Arc?

Yes. You need to put a travel time attribute on each edge, like in the example here. Then run networkx.shortest_path, providing a source node but not a target node (this will calculate shortest paths from the source node to every node in the graph). Finally, sum the travel time attributes of the edges that compose each path.

Thanks – I managed to work this one out in the end and it worked very nicely apart from a couple of locations where the nearest node to my point of interest wasn’t well-connected to the network.

Only issue I have left is that the walk network definition is quite stringent so it leaves out OSM ways that are not primarily walk, e.g. A cycleway that permits foot access such as this:
https://www.openstreetmap.org/way/5364004

I guess that downloading all-private will fix this issue.

This has really introduced me to the powerful things you can do with Python & GIS – I hadn’t done any GIS with Python before…

Beautiful work, I recommend you study modern Italian architecture.
A balanced contrast between nature and urbanism.
I’ll be getting better, I imagine if you use a drone to make the images!
Congratulations

How to save pois_from_place() result to shapfile?

gdf = ox.pois_from_place(place=’Kamppi, Helsinki, Finland’)
ox.save_gdf_shapefile(gdf,’gdf’)

throw error:
ValueError: All geometries in GeoDataFrame must be shapely Polygons or MultiPolygons

I stumbled upon the demonstration notebook by chance and asked myself if there is a way to pass the program a given center point instead of having osmnx calculate the centermost node. The Node closest to the given coordinates would be the center then. Having walking distances ploted fro certain POIs like railway station ea. seems useful. Can You point a direction?

Publications

A Generalized Framework for Measuring Pedestrian Accessibility around the World Using Open Data

A Multi-Scale Analysis of 27,000 Urban Street Networks: Every US City, Town, Urbanized Area, and Zillow Neighborhood

A Review of the Structure and Dynamics of Cities: Urban Data Analysis and Theoretical Modeling

A Roundtable Discussion: Defining Urban Data Science

An Introduction to Software Tools, Data, and Services for Geospatial Analysis of Stroke Services

Converting One-Way Streets to Two-Way Streets to Improve Transportation Network Efficiency and Reduce Vehicle Distance Traveled

Estimating Local Daytime Population Density from Census and Payroll Data

Exploring Urban Form Through OpenStreetMap Data: A Visual Introduction

GIS and Computational Notebooks

Honolulu Rail Transit: International Lessons from Barcelona in Linking Urban Form, Design, and Transportation

Housing Search in the Age of Big Data: Smarter Cities or the Same Old Blind Spots

How Our Neighborhoods Lost Food, and How They Can Get It Back

LEED-ND and Livability Revisited

Measuring the Complexity of Urban Form and Design

Methods and Measures for Analyzing Complex Street Networks and Urban Form

Neighborhood Change, One Pint at a Time

New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings

Off the Grid… and Back Again? The Recent Evolution of American Street Network Planning and Design

Online Rental Housing Market Representation and the Digital Reproduction of Urban Inequality

OSMnx Paper

OSMnx: A Python package to work with graph-theoretic OpenStreetMap street networks

OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks

Planarity and Street Network Representation in Urban Form Analysis

Pynamical: Model and Visualize Discrete Nonlinear Dynamical Systems, Chaos, and Fractals

Rental Housing Spot Markets: How Online Information Exchanges Can Supplement Transacted-Rents Data

Spatial Information and the Legibility of Urban Form: Big Data in Urban Morphology

Street Network Models and Indicators for Every Urban Area in the World

Street Network Models and Measures for Every U.S. City, County, Urbanized Area, Census Tract, and Zillow-Defined Neighborhood

Systems and Methods for Analyzing Requirements

The Effects of Inequality, Density, and Heterogeneous Residential Preferences on Urban Displacement and Metropolitan Structure: An Agent-Based Model

The Morphology and Circuity of Walkable and Drivable Street Networks

The Relative Circuity of Walkable and Drivable Urban Street Networks

The Right Tools for the Job: The Case for Spatial Science Tool-Building

Tilted Platforms: Rental Housing Technology and the Rise of Urban Big Data Oligopolies

Topological Distance Between Nonplanar Transportation Networks

Understanding Cities through Networks and Flows

Urban Analytics: History, Trajectory, and Critique

Urban Spatial Order: Street Network Orientation, Configuration, and Entropy

Urban Street Network Analysis in a Computational Notebook

Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction

We Live in a Motorized Civilization: Robert Moses Replies to Robert Caro

Urban Data Lab