I have a new article out now in the Journal of Planning Education and Research with Billy Riggs, in which we examine how two-way street conversions impact street network efficiency. Most of the efficiency literature looks at the benefits of one-way streets for signalization and vehicular throughput. We took a different approach, considering how one-way streets inherently increase travel distances.
After many years of hard work, our series of articles on urban design, transport, and health has been published by The Lancet Global Health.
In our first paper, we analyzed urban policies and calculated built environment indicators for 25 cities across 6 continents to assess walkability and accessibility. Our policy analysis found policies inconsistent with public health evidence, rhetoric endorsing health and sustainability but few measurable policy targets, and substantial implementation gaps.
I’m a co-author of a new article, “A Generalized Framework for Measuring Pedestrian Accessibility around the World Using Open Data,” which has just been published by Geographical Analysis. We developed an open source, containerized software framework for modeling pedestrian networks using open data to analyze disaggregate access to daily living needs. We worked with local partners in 25 cities around the world to demonstrate and validate this toolkit.
From the abstract:
Pedestrian accessibility is an important factor in urban transport and land use policy and critical for creating healthy, sustainable cities. Developing and evaluating indicators measuring inequalities in pedestrian accessibility can help planners and policymakers benchmark and monitor the progress of city planning interventions. However, measuring and assessing indicators of urban design and transport features at high resolution worldwide to enable city comparisons is challenging due to limited availability of official, high quality, and comparable spatial data, as well as spatial analysis tools offering customizable frameworks for indicator construction and analysis. To address these challenges, this study develops an open source software framework to construct pedestrian accessibility indicators for cities using open and consistent data. It presents a generalized method to consistently measure pedestrian accessibility at high resolution and spatially aggregated scale, to allow for both within- and between-city analyses. The open source and open data methods developed in this study can be extended to other cities worldwide to support local planning and policymaking. The software is made publicly available for reuse in an open repository.
For more, check out the article.
My article, “Street Network Models and Indicators for Every Urban Area in the World” has been published by Geographical Analysis. This project was a massive undertaking and I’m excited to share it. As you might guess from the title, I modeled and analyzed the street network of each urban area in the world then deposited all the source code and models and indicators in open repositories for public reuse. The article also includes a high-level analysis of urban street network form across the world.
Cities worldwide exhibit a variety of street patterns and configurations that shape human mobility, equity, health, and livelihoods. Using boundaries derived from the Global Human Settlement Layer, I modeled and analyzed the street networks of every urban area in the world using OSMnx and OpenStreetMap raw data. In total, I modeled over 160 million street network nodes and over 320 million edges across 8,914 urban areas in 178 countries. I attached node elevations and street grades to every node/edge in the final models. All the final models were topologically simplified such that nodes represent intersections and dead-ends, and edges represent the street segments linking them.
I have a new chapter “GIS and Computational Notebooks,” co-authored with Dani Arribas-Bel, out now in The Geographic Information Science & Technology Body of Knowledge. Want to make your spatial analyses more reproducible, portable, and well-documented? Our chapter is a short, gentle intro to using code and notebooks for modern GIS work.
Science and analytics both struggle with reproducibility, documentation, and portability. But GIS in both research and practice particularly suffers from these problems due to some of its unique characteristics. Our chapter discusses this challenge and its urgency for building better and more actionable knowledge from geospatial data. Then we introduce an emerging solution, the computational notebook, using Jupyter as our central example to illustrate what it does and how it works.
My chapter “Exploring Urban Form Through OpenStreetMap Data: A Visual Introduction” has just been published in the new book Urban Experience and Design: Contemporary Perspectives on Improving the Public Realm edited by Justin Hollander and Ann Sussman.
From the abstract:
This chapter introduces OpenStreetMap—a crowdsourced, worldwide mapping project and geospatial data repository—to illustrate its usefulness in quickly and easily analyzing and visualizing planning and design outcomes in the built environment. It demonstrates the OSMnx toolkit for automatically downloading, modeling and visualizing spatial data from OpenStreetMap. We explore patterns and configurations in street networks and buildings around the world computationally through visualization methods—including figure-ground diagrams and polar histograms—that help compress urban complexity into comprehensible artifacts that reflect the human experience of the built environment. Ubiquitous urban data and computation can open up new urban form analyses from both quantitative and qualitative perspectives.
For more, check out the chapter.
My article “Off the Grid… and Back Again? The Recent Evolution of American Street Network Planning and Design” has been published by the Journal of the American Planning Association and won the 2020 Stough-Johansson Springer Award for best paper. It identifies recent nationwide trends in American street network design, measuring how urban planners abandoned the grid and embraced sprawl over the 20th century, but since 2000 these trends have rebounded, shifting back toward historical design patterns. In this post I discuss these findings and visualizations across the US today as well as over time, then discuss my analysis methods.
Ah, the travails of academia. Like many universities, USC uses Microsoft Outlook as its email and calendar provider. This presents some integration challenges for those of us, like me, who use Google Calendar everywhere else in life. It’s effectively impossible to sync an Outlook Calendar with a Google Calendar, so I had to juggle between both when trying to schedule anything. Chaos ensues.
So, I created a script to perform an ongoing one-way sync from my USC Microsoft Office 365 Outlook calendar to my personal Google calendar, handling new, updated, and deleted events. I had to develop my own solution because Microsoft/Google inexplicably can’t get their own acts together. For example, you can publish your Outlook calendar’s ICS URL and add it to Google, but it only syncs roughly once per day so you miss any new appointments in the meantime. Microsoft Flow used to work (clumsily) for syncing, but even their official recipes are now broken. So I had to roll my own.
My new article, “Rental Housing Spot Markets: How Online Information Exchanges Can Supplement Transacted-Rents Data,” with Jake Wegmann and Junfeng Jiao is now published in the Journal of Planning Education and Research (download free PDF).
How much does it cost to rent a typical apartment in your city? Answering this basic housing question can be surprisingly difficult. Consider the case of San Francisco in early 2018.
My article “Housing Search in the Age of Big Data: Smarter Cities or the Same Old Blind Spots?” with Max Besbris, Ariela Schachter, and John Kuk is now published in Housing Policy Debate. We look at the quantity and quality of information in online housing listings and find that they are much higher in White and non-poor neighborhoods than they are in poor, Black, or Latino neighborhoods. Listings in White neighborhoods include more descriptive text and focus on unit and neighborhood amenities, while listings in Black neighborhoods focus more on applicant (dis)qualifications. We discuss what this means for housing markets, filter bubbles, residential sorting and segregation, and housing policy. You can download a free PDF.
Housing search technologies are changing and, as a result, so are housing search behaviors. The most recent American Housing Survey revealed that, for the first time, more urban renters found their current homes through online technology platforms than any other information channel. These technology platforms collect and disseminate user-generated content and construct a virtual agora for users to share information with one another. Because they can provide real-time data about various urban phenomena, housing technology platforms are a key component of the smart cities paradigm.
This paradigm promotes information technology as both a technocratic mode of monitoring cities and a utopian mode of improving urban life through big data. In this context, “big data” typically refers to massive streams of user-generated content resulting from millions or billions of decentralized human actions. Data exhaust from Craigslist and other housing technology platforms offers a good example: optimistically, large corpora of rental listings could provide housing researchers and practitioners with actionable insights for policymaking while also equalizing access to information for otherwise disadvantaged homeseekers. But how good are these platforms at resolving the types of problems that already plague old-fashioned, non-big data? Does this broadcasting of information reduce longstanding geographic and demographic inequalities or do established patterns of segmentation and sorting remain?