The Effect of Urban Infrastructure Change on Movement

Term Start:

September 1, 2023

Term End:

May 31, 2025




Covid-19, Longitudinal, Resiliency, Urban Infrastructure

Thrust Area(s):

Data Modeling and Analytic Tools, Equity and Understanding User Needs

University Lead:

University of Washington


Cynthia Chen

COVID is a crisis that is unanticipated both in its occurrence and also its length of impact. In the early days, many office employers implemented work-from-home policies while retail businesses shuttered, leading to deserted downtowns across the country. Yet crisis is also an opportunity, and municipalities and businesses innovated in response to the fears of infection. In particular, many cities changed transportation infrastructure, including permitting sidewalk cafes that accommodated outdoor dining, reallocating street space from travel or parking to outdoor dining, and redesigning streets to accommodate a wide variety of users etc. What are the effects of these urban infrastructure innovations? How well do they draw visitors and support businesses nearby? What are their effects on the region’s traffic patterns? Are there spillover effects spatially? As cities emerge from COVID and re-imagine the future of our urban cores, answers to these questions are critical. Though the existing literature has a wealth of knowledge on the built environment effect on travel behavior, they are nearly exclusively at much larger scale (e.g., census tracts) and static (comparing different behavioral patterns between places with different built environment characteristics. There is little to no insight on how block-level urban infrastructure innovations lead to changes in visit patterns as well as nearby businesses. And yet, changes at this scale (block-level) are where local policy changes take place. This proposal is to answer these questions.

This research will leverage a variety of data sources to answer the above questions, including app-based GPS data, google street view data, business data, and satellite images etc. All data are longitudinal, covering several years from pre- to during and post-COVID (now). More specifically, app-based GPS data will allow us to quantify people’s visit patterns as well as traffic flow patterns; Google Street View and satellite images will allow us to capture changes in urban infrastructure at the block level; and business data will capture business activities over time. The project will develop novel algorithms to clean and explore these data and address issues inherent to their collection, including biases, sparsity, and unrepresentativeness etic. When necessary, data fusion methods integrating different types of data will also be developed. The project will also develop methods and metrics to quantify changes in urban infrastructure. In addition to answering the questions raised above, project deliverables will also include: 1) open-source notebooks that can be used to process the various kinds of data; and 2) visualizations at selected locations to illustrate the changes from before to after.

The study site will be in the City of Seattle, a medium-large city in the Pacific Northwest that has implemented several innovations in urban infrastructure during COVID. Initial sites include Ballard and University District in Seattle. Both have a vibrant business district (though tailoring to different populations), a popular farmers’ market and saw a surge of outdoor restaurants and cafes during the COVID period. In addition to these initial sites, we will also screen google street view datasets, satellite images, as well as consult local cities for identification of additional sites in the region. The goal is to have a set of sites with contrasting characteristics in built environment and socio-demographic characteristics.

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