I’m a co-author on a new article out in Environment and Planning B: Urban Analytics and City Science titled “A Roundtable Discussion: Defining Urban Data Science” (download free PDF). It arises from a panel discussion I participated in at the 2019 AAG Annual Meeting in DC. Vanessa Frias-Martinez, Song Gao, Ate Poorthuis, and Wenfei Xu joined me on the panel, which was organized and moderated by Wei Kang, Taylor Oshan, and Levi Wolf. From the abstract:
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:
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):
My article, Online Rental Housing Market Representation and the Digital Reproduction of Urban Inequality, has just been published in Environment and Planning A (download free PDF). It explores the representation of different communities in online rental listings from two perspectives: 1) how might biases in representativeness impact housing planners’ knowledge of rental markets, and 2) how might information inequality impact residential mobility, community legibility, gentrification, and housing voucher utilization?
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).
I’m happy to announce that I have accepted a tenure-track offer from the University of Southern California as an assistant professor in the Sol Price School’s Department of Urban Planning and Spatial Analysis. I will be starting in the Fall and moving to Los Angeles later this summer! Looking forward to heading back home to California.
I am giving the Transactions in GIS plenary address at the AAG conference this afternoon. I’ll be reflecting on urban science, spatial networks, and tool-building in academia, focusing on OSMnx. A paper will be forthcoming soon, but in the meantime, for any interested plenary session attendees or other folks, here are a few links to more info and related resources:
What is OSMnx? What does it do? Here’s a succinct overview.
The easiest way to get started with street network modeling and analysis in OSMnx is with this docker image and these example/tutorial Jupyter notebooks. The OSMnx software documentation is available here and this journal article introduces it more formally.
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.
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:
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.
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.