Categories
Urban

Urban Form and OpenStreetMap

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.

Categories
Planning

Off the Grid… and Back Again?

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.

Map of where street grids exist today across the US, made with OSMnx and Python

Categories
Academia

Geospatial Tool Building

My new article “The Right Tools for the Job: The Case for Spatial Science Tool-Building” has been published in Transactions in GIS (free PDF). I originally presented this paper as the 8th annual Transactions in GIS plenary address at the AAG annual meeting last year. I argue that tool-building is an essential but poorly incentivized component of academic geography and social science more broadly. To conduct better science, we need to build better tools. Better tools and data models, spearheaded by academics, can help infuse theory into our field’s quantitative work where it is too often lacking. But if we want better tools, we have to build them. It is not ESRI’s job to satisfy all the theoretical needs of the spatial sciences.

Categories
Data

New Article on Computational Notebooks

I have a new article out in Region: Journal of the European Regional Science Association, “Urban Street Network Analysis in a Computational Notebook.” It reflects on the use of Jupyter notebooks in applied data science research, pedagogy, and practice, and it uses the OSMnx examples repository as an example.

From the abstract:

Computational notebooks offer researchers, practitioners, students, and educators the ability to interactively conduct analytics and disseminate reproducible workflows that weave together code, visuals, and narratives. This article explores the potential of computational notebooks in urban analytics and planning, demonstrating their utility through a case study of OSMnx and its tutorials repository. OSMnx is a Python package for working with OpenStreetMap data and modeling, analyzing, and visualizing street networks anywhere in the world. Its official demos and tutorials are distributed as open-source Jupyter notebooks on GitHub. This article showcases this resource by documenting the repository and demonstrating OSMnx interactively through a synoptic tutorial adapted from the repository. It illustrates how to download urban data and model street networks for various study sites, compute network indicators, visualize street centrality, calculate routes, and work with other spatial data such as building footprints and points of interest. Computational notebooks help introduce methods to new users and help researchers reach broader audiences interested in learning from, adapting, and remixing their work. Due to their utility and versatility, the ongoing adoption of computational notebooks in urban planning, analytics, and related geocomputation disciplines should continue into the future.

For more, check out the article.

Categories
Planning

Rental Housing Spot Markets

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.

Categories
Urban

Housing Search in the Age of Big Data

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?

Categories
Planning

Off the Grid at TRB

I am presenting my ongoing research into the recent evolution of American street network planning and design at the annual meeting of the Transportation Research Board in Washington DC on January 13. This presentation asks the question: how has street network design changed over time, especially in recent years? I analyze the street networks of every US census tract and estimate each’s vintage.

Street network designs grew more disconnected, coarse-grained, and circuitous over the 20th century… but the 21st century has witnessed a promising rebound back toward more traditional, dense, and interconnected grids. Higher griddedness is associated with less car ownership, even when controlling for related socioeconomic, topographical, and other urban factors.

Update: the paper has been published in JAPA.

Categories
Urban

Big Data in Urban Morphology

My new article “Spatial Information and the Legibility of Urban Form: Big Data in Urban Morphology” has been published in the International Journal of Information Management (download free PDF). It builds on recent work by Crooks et al, presenting workflows to integrate data-driven and narrative approaches to urban morphology in today’s era of ubiquitous urban big data. It situates this theoretically in the visual culture of planning to present a visualization-mediated interpretative process of data-driven urban morphology, focusing on transportation infrastructure via OSMnx.

OSMnx: Figure-ground diagrams of one square mile of each street network, from OpenStreetMap, made in Python with matplotlib, geopandas, and NetworkX

Categories
Data

Defining Urban Data Science

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:

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):