RAPID: The COVID-19 Pandemic Seattle, Washington Street View Campaign

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Seattle, Washington is the first city in the United States to be severely impacted by the coronavirus disease 2019 (COVID-19) pandemic. Since the outbreak, state and local governments have enacted policies and guidelines (i.e., nonpharmaceutical interventions) to stem the spread of the disease, including a statewide shelter-in-place order. This Grant for Rapid Response Research (RAPID) will conduct longitudinal (repeat) street view surveys for 12 months across a broad cross-section of Seattle to collect data on the community impact of the pandemic. The survey campaign will produce a high-resolution, ground-based record of the urban region both during and after this pandemic. The data set will provide fundamental insights on (1) disaster impacts on business operations, transportation networks, and other community assets, (2) the rate and quality of recovery following shelter-in-place, and how this varies locally based on a community's socioeconomic characteristics, and (3) the impact of shelter-in-place policy relaxation on communities following this disaster. In addition to the data collection, this project will advance the scientific application of post-event mobile imaging by establishing sampling protocols that may be used to guide data collection campaigns for future disruptive events. All project data will be made openly available to research and practice communities in the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) Date Depot (https://www.Designsafe-ci.org). The street view surveys will be conducted using the NSF-supported NHERI RAPID facility's vehicle-mounted mobile imaging system, located at the University of Washington. Two data collection strategies will be adopted: (1) canvassing, an umbrella approach to acquire data that may be used to answer diverse multidisciplinary research questions, and (2) surveying across community capitals transects (social, cultural, built, economic, and public health), an approach grounded in capital-based resilience theory to promote the capacity for replication across a wide range of communities and hazards. The project will also develop and implement a series of open-source routines that automatically process the data to rapidly extract time-sensitive insights from the imagery. The processed data set will serve as a benchmark for calibrating and validating urban simulation and recovery models and many of the emerging artificial intelligence-based models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.