Empirical Data in Disaster Recovery: A Data Pipeline to Investigate a Pandemic's Impact on Community Mobility

Abstract

Disaster events are becoming an increasingly common part of life for people across the world. Climate and weather-based disasters are on the rise, in addition to higher probabilities of future disease emergence. One of the main ways we understand past and current events and work to mitigate the effects of future events is through the use of data. Data, when used appropriately, can help us understand the possible impact of disaster events and our mitigation efforts. Data’s impact is maximized when data sets and methods are shared. Unfortunately, there is a dearth of understanding of what data are available in the disaster research field, leading to slow progress in community resilience efforts. This dissertation provides a greater understanding of the data being used in the disaster recovery space, and generates a new data set on community recovery from COVID-19 using a novel method. First, we conduct a review of data use in lifeline infrastructure restoration modeling. The review covers publications from the 1980s to 2019. It describes both the types of data used and their features, in addition to breakdowns of how different modeling approaches obtained and utilized data differently. The review concludes with some discussion of future research directions for the field and data management best practices, most notably data publication and documentation for fully reproducible methods. Next we create methods for utilizing street-view images to study community mobility during disaster events. The framework is an open-source data pipeline for street-view images. It demonstrates the data management best practices from the review while showing it is possible to gather insights into the effects of community events and public policy on foot traffic using the street-view images. The framework is usable to detect any type of object where an appropriate computer vision algorithm is readily available, and has applications in disaster recovery, urban planning, and even demographics estimation. Our implementation uses the images to count pedestrians and uses the count as a metric for community mobility. Additionally, the entire data set and a sample code base are available for reproducibility, or use in other research efforts. The final chapter of this dissertation implements a distance estimation algorithm on the street-view image data set to understand how outdoor social distancing practices changed over the course of the pandemic. The analysis includes vaccine availability, weather, and time of the week as predictors in addition to socioeconomic factors at the census tract level. In addition to looking at the city of Seattle as a whole, this analysis also subsets the data into various places of interest such as schools or hospitals to understand the distancing trends at these locations.