This paper builds on our recent work studying exclusionary language in rental housing listings. Shared housing is usually framed as an affordability strategy: people rent a room in a shared home to split costs and gain access to an otherwise unaffordable neighborhood. Our paper instead explores home-sharing as a home-making process and proposes a social-transactional spectrum of visions of the “home.”

We analyze thousands of shared rental listings in Los Angeles to see how listers describe domestic life. Most listings lean transactional by emphasizing privacy, rules, boundaries, furnishings, utilities, and limited access to shared space. These listings often try to recreate something like private renting inside a shared home. But some listings are explicitly social: they describe current housemates as actual people, talk about shared routines and responsibilities, and imagine homemaking as something co-tenants do together.

Different sharing arrangements land on different parts of the social-transactional spectrum. Transactional sharing may appeal to people who want affordability without much social obligation, while social sharing may offer connection, care, and a stronger sense of household – but they create very different power dynamics and tenant experiences. If shared housing is becoming a normal part of urban life, planners and policymakers need to treat it as more than cheap bedrooms and instead start thinking about access to kitchens and bathrooms, house rules, lease terms, co-tenant protections, and the everyday domestic conditions that make housing a home.

From the abstract:

Housing unaffordability and online platforms have turned shared housing into a mainstream housing strategy, even among strangers and those sharing by necessity not choice. Planning and policy, however, have not kept up with the evolving landscape. Current discourse often ignores the social dimension of shared housing, yet it is one of its defining features. Here, we examine how shared rental listers envision homemaking and domesticity through analysis of thousands of rental listings in Los Angeles, asking whether shared arrangements are primarily transactional or oriented toward building social home environments. Using human-assisted machine learning informed by qualitative coding, we categorize listings as transactional or social, then analyze subsamples through thematic content analysis. We find that less than one-third of listings signal social sharing environments; most emphasize transactional arrangements. We argue that this spectrum spans radically different visions of “home.” While social listings embrace the unavoidable social dimension of shared living and treat homemaking as a collective household project, transactional listings essentially aim to recreate private living, fashioning the shared home through boundary setting and relegating homemaking to individual co-tenants. Uncovering this spectrum of homemaking practices helps policymakers understand shared housing’s unique affordances and constraints and craft more-effective policy.

For more, check out our free open-access article at IJHP.

I’m grateful for getting to bring an urban planner’s perspective to this group. Cardiovascular health is often framed in terms of individual risk: diet, exercise, smoking, blood pressure, access to care - but these are of course only part of the story. In this paper we consider human health broadly through a planetary health perspective, asking how climate change, air pollution, water quality, food systems, housing, transportation, and urban design shape cardiovascular risk across the life course.

The central argument is that these systems do not operate separately. Food production depends on water and energy; energy production affects air quality and climate; urban planning shapes heat exposure, physical activity, displacement, and access to care; and so on. These connections matter because well-intentioned interventions can have uneven or unintended effects across disciplines that need better integration.

Our paper outlines some research opportunities for studying these links more carefully - bridging spatial analysis, remote sensing, causal inference, exposome science, health informatics, AI, and community-engaged research. Cardiovascular researchers, urban planners, environmental scientists, public health practitioners, and our communities themselves need to work together if we want evidence that can actually guide policy and improve health in a changing world.

From the abstract:

Cardiovascular health (CVH) across the life course requires stable, nurturing environments and a healthy planet. Increasing human demands on the earth’s resources destabilize our world’s ecosystems, compromising CVH. Unique research opportunities for cardiovascular researchers exist at the intersection of planetary and CVH. Using systems thinking can reveal cardiovascular and planetary health connections and mechanisms of action. For example, meeting global demands for water, food and energy threatens food, water and air quality at the local level, and with it, cardiovascular health. A refined understanding of planetary-CVH interconnections is urgently needed to guide decision making. Emerging, cutting-edge research methodologies include the use of spatial indicators and urban analytics to reveal relationships between physical environments and health outcomes; advanced causal inference methods and modeling simulations; studying human exposure patterns and the exposome - the totality of environmental exposures across the life span - in a population; advances in health informatics made possible by evolving computation methods and AI; and new ways of engaging community in research. The study of planetary health is advanced by engaging a diversity of disciplines from the fields of behavioral, medical, and social sciences to earth sciences, climate change, anthropology, Indigenous studies, and engineering. By employing a planetary health lens, systems thinking and research methodology innovations, expanded opportunities exist for CV researchers and others across a diversity of disciplines. The field will benefit from the development of a holistic research agenda, increased cross- and trans-disciplinary engagement, policy evaluation, and implementation science to support dissemination of evidence-based findings.

For more, check out our free open-access article at AJPC.

In expensive, exclusive cities, shared housing is often a pragmatic answer to the affordability crisis: rent a room to split costs and access neighborhoods that would otherwise be out of reach. Studying ~90,000 Craigslist rental listings in Los Angeles, we show that shared rentals are indeed cheaper and more geographically widespread than small whole-unit apartment rentals. But access to them depends on more than income or availability - it also depends on whether existing tenants imagine you as the “right” kind of person to live with.

Our central finding is that shared housing often turns existing tenants into gatekeepers. Unlike conventional whole-unit rental listings, shared housing listings frequently describe desired personal traits, household rules, lifestyles, privacy expectations, and ideas of compatibility. Some of this is understandable: living with strangers involves real social, financial, and emotional risk. But the language of “fit” often becomes a language of similarity, with listers seeking people of similar age, gender, work status, habits, politics, or culture. Even when race, religion, disability, or immigration status are rarely named directly, exclusion still operates through coded signals, insider language, and assumptions about who will “fit.”

The broader implication is important for housing policy. Shared renting may expand affordable options, but it does not automatically expand equitable access to those options. If the cheapest rooms in desirable neighborhoods are filtered through narrow ideas of compatibility, shared housing can reproduce segregation while appearing informal, personal, and benign. We argue that policymakers should take the relational nature of shared renting seriously, designing protections that reduce risk for co-tenants while making shared homes more accessible to a wider range of people.

From the abstract:

Shared renting offers affordability opportunities in unaffordable neighborhoods, but uniquely impels existing and prospective tenants to match on both unit and personal characteristics—creating new opportunities for discrimination and segregation. This study investigates how this matching unfolds. Do existing tenants construct “idealized co-tenants” to signal their selection criteria and signal who is and is not welcome to apply? We analyze online rental listings in Los Angeles, California through a mixed-methods research design, leveraging both quantitative deep learning models of listing language and qualitative content analysis of how listers present selection criteria. We find that, relative to whole unit listings, shared unit listings uniquely emphasize personal characteristics, rental rules, and privacy concerns. Although selection criteria describing behaviors—rather than personal traits—dominate, references to several protected classes appear. Listers often operationalize compatibility as similarity, relying on in-group communication strategies and covert insider signaling. This suggests how shared housing may perpetuate socio-spatial segregation by restricting precious affordability opportunities to narrow subpopulations. Policymakers should craft tenant protections addressing the unique relational nature of shared renting to enable more diverse shared households and counteract trends that reinforce inequitable status quos.

For more, check out our free open-access article at EP-A.

Next week I’ll take a train to Prague to visit Martin Fleischmann at Charles University for a few days. After Prague, I’m off to Bucharest, then Zürich, then Paris. And then I finally settle down in Turin, where I’ll be hosted by the ISI Foundation through July.

Fast forward to the present and I had a packed slate all week. Before the conference started, I got to attend the Spatial Data Science Symposium hosted by the Berkeley Institute for Data Science and Serge Rey’s PySAL team. At AAG proper, I organized three sessions on urban spatial analytics with Elizabeth Delmelle, gave a talk in one of them, and spoke on one of Serge’s open-source spatial software panels.

But now I’ve buried the lede. The most exciting thing for me was that AAG SAM (the spatial analysis and modeling group) awarded me their 2026 Emerging Scholar Award. What I like best about spatial analysis is how it bridges disciplines and brings together so many different people, questions, and ideas. Most of my research is about (urban) geography and is often in collaboration with geographers, and being an urban planner often feels like being an honorary geographer. I’m really grateful to this group for always making me feel welcome as a member of this community.

See you all in New York next year!

This article tackles a longstanding problem in transport geography and planning: how to estimate realistic driving travel times without access to proprietary data, expensive APIs, or massive GPS trace datasets. Much of the planning literature still relies on “naïve” methods (e.g., minimizing Euclidean distance, network distance, or speed-limit-based traversal time) that systematically under-predict real-world travel times. 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 middle ground:

• a method that uses free, open data
• runs on ordinary hardware
• and substantially improves accuracy over old naïve approaches

Using Los Angeles as a case study, we combine OpenStreetMap 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 first compute naïve shortest-path travel times, then train an interpretable random forest model to predict travel time as a function of naïve time plus turn counts and traffic controls encountered along each route. Whereas the naïve model under-predicts travel time by over three minutes on average, our model’s predictions differ from Google’s by just 0.34 seconds on average and achieve an out-of-sample MAPE of 8.4%—comparable to far more data-intensive approaches. Robustness checks with new network data yield similar performance, and SHAP analysis supports the model’s theoretical soundness.

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.

From the abstract:

Travel time prediction is central to transport geography and planning’s accessibility analyses, sustainable transportation infrastructure provision, and active transportation interventions. However, calculating accurate travel times, especially for driving, requires either extensive technical capacity and bespoke data, or resources like the Google Maps API that quickly become prohibitively expensive to analyze thousands or millions of trips necessary for metropolitan-scale analyses. Such obstacles particularly challenge less-resourced researchers, practitioners, and community advocates. This article argues that a middle-ground is needed to provide reasonably accurate travel time predictions without extensive data or computing requirements. It introduces a free, open-source minimally-congested driving time prediction model with minimal cost, data, and computational requirements. It trains and tests this model using the Los Angeles, California urban area as a case study by calculating naïve travel times from open data then developing a random forest model to predict travel times as a function of those naïve times plus open data on turns and traffic controls. Validation shows that this interpretable machine learning method offers a superior middle-ground technique that balances reasonable accuracy with minimal resource requirements.

On a side note, my co-author and doctoral advisee Wendy Zhou was also awarded the 2026 Tiebout Prize in Regional Science at the WRSA conference in Santa Fe this week. Congrats Wendy!

For more, check out our article at IJGIS or via the open-access pre-print.

Urban sustainability is key to planetary health. Yet few cities have measurable policy standards and targets to actually build healthier and more sustainable cities, and their health-supportive built environment features are often inadequate or inequitably distributed. Can residents sustainably access their daily living needs? A critical step toward making cities healthier and more sustainable is to empower policymakers and urban planners to consistently measure neighborhood-level spatial indicators of the built environment.

At GOHSC, we measure spatial and policy indicators of healthy and sustainable urban planning practice in partnership with a network of local collaborators in cities around the world. Our analytics let non-technical users load and transform their own local data to automatically calculate a set of indicators and generate a report and scorecard infographic for advocacy work. Over the past year, local planners, mayors’ offices, citizen advocates, and international organizations have used this software to measure, benchmark, and monitor cities’ progress toward global health and sustainability goals. This supports evidence-informed interventions anywhere from the neighborhood scale to the metropolitan scale. Our software’s accessibility and rigor unlock the ability to effect urgently needed change while reducing longstanding barriers to participation, particularly in under-resourced cities and countries where better planning for health and sustainability are most needed.

I’m really proud of this group. I’ve had the pleasure of serving on our executive committee and as the spatial team co-lead since its inception. We’re a member organization of the UN’s Global Urban Observatory Network and we published a series of articles in The Lancet Global Health a couple years ago about our work to date.

Sincere thanks to the hundreds of researchers and practitioners in hundreds of cities around the world who have joined our collaboration network over the past few years. And thank you to my colleagues on the executive committee: it is truly a pleasure working with you.

I’m organizing a session with Elizabeth Delmelle that asks: how can urban data science and spatial analytics meaningfully address cities’ “wicked problems” instead of just reaching for this year’s shiny new tool or the easy data set we can just grab and analyze?

Cities today face challenges that feel intractable. Despite significant progress in urban data science and spatial analytics over the past 20 years, the problems tackled by this scholarship too often prioritize the “low-hanging fruit” publication pipeline of letting easily available data and trendy methods determine the research question rather than confronting urgent but arduous challenges. Methods-driven research that gestures toward real-world applications frequently features policy implications wholly detached from the political constraints and implementation realities that practitioners must navigate.

We seek papers (theoretical, empirical, or viewpoint/commentary) that challenge and critique this status quo. This is an opportunity to experiment, try new things, attempt something hard that may not work, present null findings and research failures – as long as you propose something ambitious to use spatial methods to meaningfully improve urban living.

Submissions should use real-world problems to drive the methods (rather than letting preferred methods determine the research problem) in urban data science and spatial analytics. Example topics could include:

• spatial data storytelling for societal change
• data visualization to foster urban political conversations in an era of polarization
• evidence-based communication to persuade local stakeholders for the public good
• changing people’s minds about a crisis with frozen battlelines, such as climate change
• tangibly attenuating an urban wicked problem like homelessness or unaffordability
• shifting negative public narratives around cities that run counter to empirical evidence
• communicating the value of planning, funding, and infrastructure in an era of budget crises and defunding
• lessening (rather than merely describing) inequity in deeply segregated and unequal cities
• anything else that ambitiously tries to address a big problem facing cities through the tools of urban data science and spatial analytics that has the potential to produce meaningful and plausible change

Where/When: this session will be in-person at the American Association of Geographers Annual Meeting, March 17-21, 2026, San Francisco, California.

How to submit: submit your abstract to AAG by its Oct 30 deadline. Then email your abstract and AAG PIN to both Geoff (boeing at usc dot edu) and Elizabeth (delmelle at design dot upenn dot edu) by Nov 1 for us to consider for the session. Session participants will be notified by Nov 20.

We argue that a distinguishing feature of urban street networks, which makes them unique compared to other spatial networks, is their extreme betweenness centrality heterogeneity. In plain English that means that street networks are particularly prone to chokepoints: network nodes on which a disproportionately high number of shortest paths depend. Theoretically, we can explain this as a street network that started as a backbone road, then grew and filled in as the area around it urbanized.

Building on this idea, we propose a generative model of urban street networks - that is, a model that generates street networks that reproduce this distinguishing feature. It turns out that most models don’t! Our proposed model starts with a minimum spanning tree (the initial backbone) then adds edges iteratively (the subsequent urbanization) to match empirical degree distributions. Our model, implemented in Python, reproduces key empirical characteristics well.

From the abstract:

Analyzing 9000 urban areas’ street networks, we identify properties, including extreme betweenness centrality heterogeneity, that typical spatial network models fail to explain. Accordingly we propose a universal, parsimonious, generative model based on a two-step mechanism that begins with a spanning tree as a backbone then iteratively adds edges to match empirical degree distributions. Controlled by a single parameter representing lattice-equivalent node density, it accurately reproduces key universal properties to bridge the gap between empirical observations and generative models.

For more, check out the article at PRL or the open-access arXiv pre-print.

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. After all, everyone learns to count in kindergarten by simply enumerating the elements in a set. 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.

Street intersection counts and densities are ubiquitous measures in transportation 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 to each.

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. This results in 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 due to difficulties representing divided roads, slip lanes, roundabouts, interchanges, complex 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.

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

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