(in reverse chronological order; newest first)
Urban Street Network Design and Transport-Related Greenhouse Gas Emissions around the World
with Geoff Boeing and Yougeng Lu, forthcoming at Transportation Research Part D, 2023.
Abstract: This study estimates the relationships between street network characteristics and transport-sector CO2 emissions across every urban area in the world and investigates whether they are the same across development levels and urban design paradigms. The prior literature has estimated relationships between street network design and transport emissions—including greenhouse gases implicated in climate change—primarily through case studies focusing on certain world regions or relatively small samples of cities, complicating generalizability and applicability for evidence-based practice. Our worldwide study finds that straighter, more-connected, and less-overbuilt street networks are associated with lower transport emissions, all else equal. Importantly, these relationships vary across development levels and design paradigms—yet most prior literature reports findings from urban areas that are outliers by global standards. Planners need a better empirical base for evidence-based practice in under-studied regions, particularly the rapidly urbanizing Global South.
with Sarah West, published in Journal of Transport and Land Use, July 2023.
Abstract: Transit station area land-use characteristics can increase or decrease the perceived costs of riding rail relative to driving or taking other modes. This paper focuses on those characteristics that create discomfort to riders who are walking between stations and destinations, with the aim of providing researchers and planners with a tool that can be used to identify pain points in any existing or potential station areas. We propose and demonstrate a scalable, recomputable method of measuring pedestrian quality for trips that relies solely on datasets readily available for almost any location in the United States, and we compare results using data from a global source, OpenStreetMap. We illustrate our tool in neighborhoods surrounding the Blue Line light rail in Minneapolis, Minnesota, calculating the population-weighted distribution of land uses within pathway buffers of walks from stations to nearby destinations. We focus on land uses that pose a disutility to pedestrians such as major highways or industrial tracts, and we compare disamenity levels across station areas. Despite their simplicity, our measures capture important differences in land-use-related pedestrian experiences and reveal the inadequacy of using circular buffers to designate and characterize station catchment areas.
with Geoff Boeing and Yougeng Lu, published in Urban Studies, January 2023.
Abstract: Vehicular air pollution has created an ongoing air quality and public health crisis. Despite growing knowledge of racial injustice in exposure levels, less is known about the relationship between the production of and exposure to such pollution. This study assesses pollution burden by testing whether local populations’ vehicular air pollution exposure is proportional to how much they drive. Through a Los Angeles, California, case study we examine how this relates to race, ethnicity and socio-economic status – and how these relationships vary across the region. We find that, all else equal, tracts whose residents drive less are exposed to more air pollution, as are tracts with a less-White population. Commuters from majority-White tracts disproportionately drive through non-White tracts, compared to the inverse. Decades of racially-motivated freeway infrastructure planning and residential segregation shape today’s disparities in who produces vehicular air pollution and who is exposed to it, but opportunities exist for urban planning and transport policy to mitigate this injustice.
with Marlon Boarnet, published in Urban Findings, July 2022.
Abstract: Several publications studying associations between land use and shared micromobility (such as dockless scooters and bicycles) rely on Negative Binomial regression models or similar, reporting the models’ untransformed coefficients. We demonstrate a new way of reporting the associations identified by such models: By reporting marginal effects for several different starting points resembling real-world locations rather than just coefficients, these models’ implications can be made more approachable to a lay audience. At the same time, we draw attention to the models’ limitations when applied to locations that are outliers in terms of density.
with Sarah West, published in Regional Science and Urban Economics, March 2018.
Abstract: This study uses property-level repeat sales transaction data to test for the presence of a premium for single-family homes within half a mile of stations on the METRO Blue Line in Minneapolis, Minnesota. Using a difference-in-differences approach, we find that the premium for station proximity varies substantially depending on control group and period definitions for “after” light rail. Using homes in the rest of Minneapolis as controls yields growing positive premiums from proximity to light rail stations, while using homes in neighborhoods similar to those near stations yield smaller premiums that fade to zero over time.