Geoff Boeing

USC's Department of Urban Planning and Spatial Analysis is hiring 2 tenure-track assistant professors, 1 in transportation planning and 1 in environmental planning. More details...

For the transportation job, we particularly encourage applicants whose work uses a modern spatial analytics toolkit. And for the environmental job, we particularly encourage applicants whose work intersects with design and the built environment.

usccareers.usc.eduAssistant Professor of Transportation Planning at USCLearn more about applying for Assistant Professor of Transportation Planning at USC

Overall we seek creative scholars asking innovative new questions and working/thinking interdisciplinarily to address problems facing cities on a global stage. Application links: Transportation planning: usccareers.usc.edu/job/los-ange... Environmental planning: usccareers.usc.edu/job/los-ange...

theverge.comPHP: The three letters that shut down sites all over the internet todaySites using PHP are rushing to update.

Feeling pretty good today about my decision earlier this year to move my web site from self-hosted Wordpress (after like 20 years!) to static Github pages hosting. www.theverge.com/tech/1015906...

AAG session call for papers! "Research Show & Tell: What Gets You Out of Bed in the Morning?" Rachel Franklin and I are organizing what we think will be a fun session, which asks: why did you do this particular research project, and what did you personally get out of it?

Researchers could spend their precious time and energy on lots of other activities. Where do you find the joy, passion, satisfaction, or meaning that gets you out of bed in the morning? We seek "show and tell" talks: show us your research, and then tell us its otherwise invisible personal story.

Abstracts should aim for a 50/50 split: half describing the research and half telling the story of why you did it. Your AAG presentation should do the same. Email your abstract and PIN to both Rachel (rachel_franklin at cga.harvard.edu) and Geoff (boeing at usc.edu) by 22 October for consideration.

It's that time of year when I receive 20 AI slop emails every day from prospective students. In kindness, some friendly advice: be yourself. I understand English may not be your first language. That's ok.

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I'm looking for curiosity and effort and thirst and critical thinking. Generic AI emails suggest none of these, and are indistinguishable from the other 20 such emails I'll receive today.

Just be yourself. Write your own ideas in your own words. They'll be flawed but much better than these warmed-over microwaved slop emails that make my brain go numb.

I'm much more interested in how *you* (an imperfect but interesting, unique human) think, communicate, and care about the research you're proposing to do with me for the next 5 years. After all, we're going to be talking about it in person an awful lot, without AI aids, if you work with me.

5 July

Happy 4th of July, LA. We're #1! 🇺🇸

doi.orgTravel time prediction from sparse open dataTravel time prediction is central to transport geography and planning’s accessibility analyses, sustainable transportation infrastructure provision, and active transportation interventions. However...

My article "Travel Time Prediction from Sparse Open Data" has just been published in the International Journal of Geographical Information Science. We tackle a longstanding problem of how to predict realistic driving travel times without access to expensive proprietary data. buff.ly/vvSgm6s

Our goal is not to replace state-of-the-art congested travel models, but to equip less-resourced planners, scholars, and community advocates with a free, open, and accurate tool for accessibility analysis, scenario planning, and evidence-based interventions when resources are limited.

For more, check out our article at IJGIS: doi.org/10.1080/1365... Or the open-access preprint here: osf.io/qepc6_v1

Using LA as a case study, we combine open data on street networks, speed limits, traffic controls, and turns with a small training sample of empirical travel times from the Google Routes API.

Whereas a naïve model under-predicts travel time by >3 minutes on average, our model mis-predicts by <1 second on average and achieves an out-of-sample MAPE of ~8%, similar to far more data-intensive approaches.

At the other extreme, state-of-the-art models in computer science and transportation engineering can achieve really good accuracy, but often require billions of observations, deep learning models, and massive computational resources and capacity.

We argue that planners and applied researchers need a cheaper, easier middle ground to predict minimally-congested but accurate travel times: - a method that uses free, open data - runs on ordinary hardware - and substantially improves accuracy over old naïve approaches

Planners often still rely on "naïve" methods (e.g., minimizing Euclidean distance, network distance, or speed limit based traversal time) that systematically under-predict real driving times. This is a problem if it makes driving seem unrealistically fast relative to transit, biking, or walking.

24 Mar

Marc Barthelemy and I recently published an article in Physical Review Letters titled "Universal Model of Urban Street Networks." We present an algorithm to generate urban street networks that reproduce key empirical characteristics. Paper link: doi.org/10.1103/1vj4... Here's a short summary...

Our proposed model starts with a minimum spanning tree (the initial backbone) then adds edges iteratively (the subsequent urbanization) to match empirical degree distributions. We find that this successfully reproduces key empirical characteristics of real-world street networks.

doi.orgUniversal Model of Urban Street NetworksAnalyzing 9000 urban areas' street networks, we identify properties, including extreme betweenness centrality heterogeneity, that typical spatial network models fail to explain. Accordingly we…

24 Mar

For more, check out the article: doi.org/10.1103/1vj4... Or the open-access preprint: arxiv.org/abs/2509.21931

In other words, most network nodes have very few shortest paths that depend on them, but there are some nodes on which a very high number of shortest paths depend. Theoretically, we explain this as a street network that started as a backbone road, then grew/filled in as the area around it urbanized.

24 Mar

It turns out that most generative models don't capture this fundamental characteristic well. So we propose a new generative model of urban street networks, that is, a model that generates street networks which reproduce this distinguishing feature.

A distinguishing feature of urban street networks is their extreme betweenness centrality heterogeneity. That means that street networks are particularly prone to chokepoints (nodes that many trips get funneled through).

25 Feb

I recently published a paper in PNAS Nexus with Marc Barthelemy, Alain Chiaradia, and Chris Webster that addresses the problem of urban networks' elevations: they seem simple enough, but modeling them can be tricky. Open-access paper link: buff.ly/Mt7u2QG Here's a short summary...

We solve this by instead modeling urban networks embedded on the surface of a curving 2D manifold (that is, the topography of the underlying land), then we examine lazy paths and graph arduousness to show how elevation affects betweenness centrality, path ruggedness, and overall network efficiency.

doi.orgSurfacic networksAbstract. Surfacic networks are structures built upon a 2D manifold. Many systems, including transportation networks and various urban networks, fall into

25 Feb

Properly embedding a spatial network on a curving surface -- capturing the unique underlying topography -- gives us better models of systems, like cities, that are built on such irregular surfaces. Free open-access link to the paper: doi.org/10.1093/pnas...

Most spatial network models are embedded in a 2D plane, but this useful fiction discards important information: variations in a city's 3D elevation affect network connectivity, centrality, and difficulty of travel. Steep hills make it hard to walk or build streets, and we need to account for that.

How many street intersections do you see in this figure? I published an article recently in Transactions in GIS (open-access: buff.ly/TZoFrrf) and its first sentence sums it up: "Counting is hard." Hear me out... It really is!

These algorithms’ information compression drastically improves downstream graph analytics’ memory and runtime efficiency, boosting analytical tractability without loss of model fidelity.

doi.orgTopological Graph Simplification Solutions to the Street Intersection Miscount ProblemStreet intersection counts and densities are ubiquitous measures in transport geography and planning. However, typical street network data and typical street network analysis tools can substantially…

Counting is hard, but we can make it a little easier by using better models. For more, check out the open-access article: doi.org/10.1111/tgis...

Mitigating these 3 problems is a project I’ve been iteratively refining for the past decade. It was a central focus of my dissertation and a key motivation for originally developing OSMnx.

This article presents OSMnx’s algorithms to automatically simplify spatial graphs of urban street networks—via edge simplification and node consolidation—resulting in faster parsimonious models and more accurate network measures like intersection counts and densities, street lengths, & node degrees.

This causes spatial uncertainty due to data challenges in representing network nonplanarity, intersection complexity, and curve digitization. Essentially all data sources suffer from at least 1 of these problems in representing divided roads, slip lanes, roundabouts, interchanges, turning lanes, etc

If unaddressed, my assessment shows that typical intersection counts (and downstream densities) would be overestimated by >14%, but very unevenly so in different parts of the world. This bias’s extreme heterogeneity particularly hinders comparative urban analytics.

Street intersection counts and densities are ubiquitous measures in transport geography and planning. However, typical street network data and typical street network analysis tools can substantially overcount them. This article explains the 3 main reasons why this happens and presents solutions.

Street intersections, particularly the complex kind common in modern car-centric urban areas, are fuzzy objects for which most data sources do not provide a simple 1:1 representation.

Most real-world objects belong to fuzzy categories, resulting in subjective decisions about what to include or exclude from counts. Yet this complexity is often obscured by a superficial impression that counting is easy to do because its mechanics seem easy to understand.

But counting is *hard* because defining that set and identifying its members are often nontrivial tasks. Many of the world’s most important analytics rely far less on flashy data science techniques than they do on counting things well and justifying those counts effectively.

25 June 2025

The official OSMnx reference paper was recently published open-access by Geographical Analysis: doi.org/10.1111/gean... Years in the making, it describes what OSMnx does and why it does it that way. But wait, there's more! I discuss many lessons learned in geospatial software development, including:

You can just as easily work with urban amenities/points of interest, building footprints, transit stops, elevation data, street orientations, speed/travel time, and routing. It recently reached version 2.0 with a slew of new features and enhancements.

doi.orgModeling and Analyzing Urban Networks and Amenities With OSMnxOSMnx is a Python package for downloading, modeling, analyzing, and visualizing urban networks and any other geospatial features from OpenStreetMap data. A large and growing body of literature uses i....

25 June 2025

For more, check out the open-access article: doi.org/10.1111/gean...

All of these lessons have become central to the work my RAs do in the Urban Data Lab at USC. They're not always easy, but they make a clear improvement in research quality, clarity, and reusability that directly impacts our downstream empirical analyses and scientific theorizing.