iiinova — where your digital self keeps evolving

Geoff Boeing @geoffboeing.com · 02/07/2026

Awesome. Did you go to the Moomins exhibit?

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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.

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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.

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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.

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

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.

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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.

Travel time prediction from sparse open data

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

doi.org

Travel time prediction is central to transport geography and planning’s accessibility analyses, sustainable transportation infrastructure provision, and active transportation interventions. However...

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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.

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.

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.

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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).

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...

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Curtis Sliwa is the gift that keeps on giving.

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

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...

doi.org

Surfacic networks

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.

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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.

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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...

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

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.

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.

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.

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

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.

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.

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.

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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.

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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!

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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.

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If you haven't used it before OSMnx is a Python package to download, model, analyze, and visualize street networks and any other geospatial features from OpenStreetMap. You can download and model walking, driving, or biking networks with a single line of code then quickly analyze and visualize them.

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.

What makes a good API, and why is it so hard (or is it just me)? How your development pipeline can make or break your quality of life as an open-source developer Dependency ecosystems and the fine line between dependency heaven and dependency hell How we can advance reusable geospatial software

Dani Arribas-Bel @darribas.bsky.social · 29/05/2025

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:

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Reposted by Geoff Boeing

I wrote up the essence of my recent talks in China about why social researchers and policy makers should pay more attention to satellites. Tl;dr: it's never been a better time to use something we've never needed more. Longer form (6mins according to Firefox!): me.darribas.org/2025/05/29/t...

me.darribas.org

The case for satellite imagery in social science and policymaking: A sketch from my talks in China

It’s never been a better time to use something …

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Geoff Boeing @geoffboeing.com · 30/05/2025

That's fantastic! I coauthored this surfacic networks paper with Marc, so I know you're all in for a treat! 😊

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Geoff Boeing @geoffboeing.com · 30/05/2025

Congrats Marta! This looks fantastic.

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Reposted by Geoff Boeing

Rachel Franklin 🦚 @rsfrankl.bsky.social · 03/02/2025

🎙️ New #GLaDpodcast episode! @darribas.bsky.social, @levijohnwolf.bsky.social and I are joined by the amazing @geoffboeing.com (@priceschool.usc.edu) to talk streets, disasters, urban form & open source software. It's a pretty good one, if I do say so myself. #geosky open.spotify.com/episode/4H6K...

open.spotify.com

Episode 20: Street Smart — A Conversation with Geoff Boeing

The GLaD Podcast · Episode

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And it has received hundreds of contributions from many other code contributors. Thank you to everyone who helped make this possible. I hope you find the package as useful as I do. Now I'm looking forward to all your bug reports 😂

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On a personal note, this has now been a labor of love for me for about 9 years. Wow. I initially developed this package to enable the empirical research for my dissertation. Since then, it has powered probably 2/3 of the articles I've published over the years.

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OSMnx 2.0.0 documentationContentsMenuExpandLight modeDark modeAuto light/dark, in light modeAuto light/dark, in dark mode

osmnx.readthedocs.io

If you haven't used it before, OSMnx is a Python package to easily download, model, analyze, and visualize street networks and any other geospatial features from OpenStreetMap. You can download and model walking, driving, or biking networks with a single line of code.

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Geoff Boeing @geoffboeing.com · 19/11/2024

OSMnx 2.0.0 has been released! 🎉 This has been a massive effort over the past year to streamline the package's API, re-think its internal organization, and optimize its code. Today OSMnx is faster, more memory efficient, and fully type-annotated for a better user experience. 🧵

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We did! I'll have to check my notes when I get off from teaching later... I can't remember the reliability numbers off the top of my head.