Categories
Urban

Urban Street Network Orientation

My new article, Urban Spatial Order: Street Network Orientation, Configuration, and Entropy, has just been published in one of my favorite journals: Applied Network Science (download free PDF). This study explores the spatial signatures of urban evolution and central planning. It examines street network orientation, connectivity, granularity, and entropy in 100 cities around the world using OpenStreetMap data and OSMnx for modeling and visualization:

City street network grid orientations, order, disorder, entropy, rose plot, polar histogram made with Python, OSMnx, OpenStreetMap, matplotlib.

So, who’s got a grid and who doesn’t? Each of the cities above is represented by a polar histogram (aka rose diagram) depicting how its streets orient. Each bar’s direction represents the compass bearings of the streets (in that histogram bin) and its length represents the relative frequency of streets with those bearings. The cities above are in alphabetical order. Here they are again, re-sorted from most-ordered/gridded city (Chicago) to most-disordered (Charlotte):

Categories
Data

New Article in Frontiers in Neurology

I recently teamed up with an international group of public health researchers and spatial analysts to co-author an article, An Introduction to Software Tools, Data, and Services for Geospatial Analysis of Stroke Services, that has been accepted for publication at Frontiers in Neurology (download free PDF).

Hospital catchment basin for stroke services. Spatial analysis in python, geopandas, osmnx.

Categories
Data

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:

Categories
Academia

Spring Teaching

Happy new year! In the spring semester I’ll be teaching two new courses: Big Data for Cities and Advanced Spatial Analysis of Urban Systems. The former serves as a sort of gateway course to Northeastern’s urban informatics master’s program, introducing students to urban theories and scientific methods of analyzing urban data. The latter introduces advanced students to a computational workflow of spatial analysis and statistics with Python, PostGIS, and other open-source tools. I’ll be creating my lectures as Jupyter notebooks and will share a GitHub link soon when they’re all together.

Categories
Planning

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.

Categories
Planning

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.

Categories
Planning

City Street Orientations around the World

City street network grid orientations, order, disorder, entropy, rose plot, polar histogram made with Python, OSMnx, OpenStreetMap, matplotlib.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:

City street network grid orientations, rose plot, polar histogram made with Python, OSMnx, OpenStreetMap, matplotlib. Bangkok, Barcelona, Beijing, Budapest, Cairo, Delhi, Dubai, Glasgow, Hong Kong, Lagos, London, Madrid, Melbourne, Mexico City, Moscow, Mumbai, Munich, Paris, Rio de Janeiro, Rome, Seoul, Sydney, Tehran, Toronto, Warsaw, Tokyo, Berlin, Venice

Categories
Planning

Comparing US City Street Orientations

City street network grid orientations, order, disorder, entropy, rose plot, polar histogram made with Python, OSMnx, OpenStreetMap, matplotlib.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):

City street network grid orientations, rose plot, polar histogram made with Python, OSMnx, OpenStreetMap, matplotlib. Atlanta, Boston, Buffalo, Charlotte, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Las Vegas, Los Angeles, Manhattan, New York, Miami, Minneapolis, Orlando, Philadelphia, Phoenix, Portland, Sacramento, San Francisco, Seattle, St Louis, Tampa, Washington DC.

Categories
Planning

Estimating Daytime Density in RSRS

My short article “Estimating Local Daytime Population Density from Census and Payroll Data” is out now in the latest issue of Regional Studies, Regional Science. I discuss a method for estimating local daytime density across a metropolitan area using US Census and LEHD LODES data, and dig into some limitations and biases. I look at the San Francisco Bay Area as a case study:

Map of the estimated daytime population density in the San Francisco Bay Area

Categories
Data

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