Tag: street-network

Data

US Street Network Models and Measures

Post author By gboeing
Post date 2019-03-12
2 Comments on US Street Network Models and Measures

My new article, “Street Network Models and Measures for Every U.S. City, County, Urbanized Area, Census Tract, and Zillow-Defined Neighborhood” has been published in Urban Science. This paper reports results from a broader project that collected raw street network data from OpenStreetMap using the Python-based OSMnx software for every U.S. city and town, county, urbanized area, census tract, and Zillow-defined neighborhood boundary. It constructed nonplanar directed multigraphs for each and analyzed their structural and morphological characteristics.

The resulting public data repository contains over 110,000 processed, cleaned street network graphs (which in turn comprise over 55 million nodes and over 137 million edges) at various scales—comprehensively covering the entire U.S.—archived as reusable open-source GraphML files, node/edge lists, and ESRI shapefiles that can be immediately loaded and analyzed in standard tools such as ArcGIS, QGIS, NetworkX, graph-tool, igraph, or Gephi.

Data

Street Network Analysis in a Docker Container

Post author By gboeing
Post date 2019-02-18
3 Comments on Street Network Analysis in a Docker Container

Containerization is the way of the ~~future~~ present. I’ve heard feedback from some folks over the past few months who would like to play around with OSMnx for street network analysis, transport modeling, and urban design—but can’t because they can’t install Python and its data science stack on their computers. Furthermore, it would be nice to have a consistent reference environment to deploy on AWS or elsewhere in the cloud.

So, I’ve created a docker image containing OSMnx, Jupyter, and the rest of the Python geospatial data science stack, available on docker hub alongside additional usage instructions. If you’re starting from scratch, you can get started in four simple steps:

Planning

New Chapter: Street Network Morphology

Post author By gboeing
Post date 2019-01-31
1 Comment on New Chapter: Street Network Morphology

My chapter The Morphology and Circuity of Walkable and Drivable Street Networks is now in-press for publication in the forthcoming book The Mathematics of Urban Morphology (download free PDF). The book integrates recent theoretical and empirical work from urban planning, geography, sociology, architecture, economics, and mathematics around the theme of how we model and understand the urban form’s physical patterns and shaping processes. Fellow authors in this volume include Michael Batty, Diane Davis, Keith Clarke, Bin Jiang, Kay Axhausen, Carlo Ratti, and Stephen Marshall. The book itself can be purchased here.

Planning

New Article: Planar Models of Street Networks

Post author By gboeing
Post date 2018-11-16
2 Comments on New Article: Planar Models of Street Networks

My article, “Planarity and Street Network Representation in Urban Form Analysis,” was recently published in Environment and Planning B: Urban Analytics and City Science. Models of street networks underlie research in urban travel behavior, accessibility, design patterns, and morphology. These models are commonly defined as planar, meaning they can be represented in two dimensions without any underpasses or overpasses. However, real-world urban street networks exist in three-dimensional space and frequently feature grade separation such as bridges and tunnels: planar simplifications can be useful but they also impact the results of real-world street network analysis. This study measures the nonplanarity of drivable and walkable street networks in the centers of 50 cities worldwide, then examines the variation of nonplanarity across a single city. While some street networks are approximately planar, I empirically quantify how planar models can inconsistently but drastically misrepresent intersection density, street lengths, routing, and connectivity.

Urban

New Article: Complexity in Urban Form and Design

Post author By gboeing
Post date 2018-09-28
1 Comment on New Article: Complexity in Urban Form and Design

My article, Measuring the Complexity of Urban Form and Design, is now in-press for publication at Urban Design International (download free PDF). Cities are complex systems composed of many human agents interacting in physical urban space. This paper develops a typology of measures and indicators for assessing the physical complexity of the built environment at the scale of urban design. It extends quantitative measures from city planning, network science, ecosystems studies, fractal geometry, statistical physics, and information theory to the analysis of urban form and qualitative human experience.

$The Mandelbrot set, a mathematical fractal. Venice's fractal urban form and fabric. The Eiffel Tower's fractal architecture in Paris.$

Planning

New Article: Urban Street Networks in EP-B

Post author By gboeing
Post date 2018-08-28
1 Comment on New Article: Urban Street Networks in EP-B

My article, “A Multi-Scale Analysis of 27,000 Urban Street Networks: Every US City, Town, Urbanized Area, and Zillow Neighborhood,” was recently published in Environment and Planning B: Urban Analytics and City Science. This study uses OSMnx to download and analyze 27,000 street networks from OpenStreetMap at metropolitan, municipal, and neighborhood scales – namely, every US city and town, census urbanized area, and Zillow-defined neighborhood. It illustrates the use of OSMnx and OpenStreetMap to consistently conduct street network analysis with extremely large sample sizes, with clearly defined network definitions and extents for reproducibility, and using nonplanar, directed graphs.

These 27,000 street networks as well as their measures have been shared in a free public repository at the Harvard Dataverse for anyone to re-purpose. This study’s empirical findings emphasize measures relevant to graph theory, transportation, urban design, and morphology, such as structure, connectedness, density, centrality, and resilience. It uses graph Maximum Betweenness Centrality and Average Node Connectivity to examine how “resilient” a street network is, in terms of how reliant it is on important nodes and how easy it is to disconnect it.

Planning

City Street Orientations around the World

Post author By gboeing
Post date 2018-07-11
92 Comments on City Street Orientations around the World

This post is adapted from this research paper that you can read/cite for more info. It analyzes and visualizes 100 cities around the world.

By popular request, this is a quick follow-up to this post comparing the orientation of streets in 25 US cities using Python and OSMnx. Here are 25 more cities around the world:

Tags bangkok, barcelona, beijing, berlin, budapest, cairo, city, complexity, data, data science, delhi, design, dubai, geocoding, geopandas, geospatial, gis, glasgow, hong kong, lagos, land use, livability, london, madrid, maps, matplotlib, melbourne, mexico city, modeling, moscow, mumbai, munich, neighborhood, networks, osmnx, paris, planning, python, rio de janeiro, rome, science, seoul, street-network, streets, sydney, tehran, tokyo, toronto, urban, urban design, urban planning, venice, visualization, warsaw

Planning

Comparing US City Street Orientations

Post author By gboeing
Post date 2018-07-10
174 Comments on Comparing US City Street Orientations

This post is adapted from this research paper that you can read/cite for more info. It analyzes and visualizes 100 cities around the world.

“We say the cows laid out Boston. Well, there are worse surveyors.” –Ralph Waldo Emerson. In 1960, one hundred years after Emerson’s quote, Kevin Lynch published The Image of the City, his treatise on the legibility of urban patterns. How coherent is a city’s spatial organization? How do these patterns help or hinder urban navigation? I recently wrote about visualizing street orientations with Python and OSMnx. That is, how is a city’s street network oriented in terms of the streets’ compass bearings? How well does it adhere to a straightforward north-south-east-west layout? I wanted to revisit this by comparing 25 major US cities’ orientations (EDIT: by popular request, see also this follow-up comparing world cities):

Data

Network-Based Spatial Clustering

Post author By gboeing
Post date 2018-04-05
5 Comments on Network-Based Spatial Clustering

Jobs, establishments, and other amenities tend to agglomerate and cluster in cities. To identify these agglomerations and explore their causes and effects, we often use spatial clustering algorithms. However, urban space cannot simply be traversed as-the-crow-flies: human mobility is network-constrained. To properly model agglomeration along a city’s street network, we must use network-based spatial clustering.

The code for this example can be found in this GitHub repo. We use OSMnx to download and assemble the street network for a small city. We also have a dataframe of points representing the locations of (fake) restaurants in this city. Our restaurants cluster into distinct districts, as many establishments and industries tend to do:

firm locations on the street network to be clustered: python, osmnx, matplotlib, scipy, scikit-learn, geopandas

Data

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