Street View Data Collection Design for Disaster Reconnaissance
Nicole A. Errett, Joseph Wartman, Scott B. Miles, Ben Silver, Matthew, Martell, Youngjun Choe

TL;DR
This paper details a comprehensive method for collecting and publishing longitudinal street-view imagery data post-disaster, highlighting challenges faced and providing recommendations for future data collection efforts.
Contribution
It introduces a systematic approach for disaster-related street-view data collection and sharing, emphasizing longitudinal data generation and addressing practical challenges.
Findings
Successful generation of longitudinal street-view data post-disaster
Identification of challenges in data consistency and volume management
Recommendations for future disaster-related street-view data collection
Abstract
Over the last decade, street-view type images have been used across disciplines to generate and understand various place-based metrics. However efforts to collect this data were often meant to support investigator-driven research without regard to the utility of the data for other researchers. To address this, we describe our methods for collecting and publishing longitudinal data of this type in the wake of the COVID-19 pandemic and discuss some of the challenges we encountered along the way. Our process included designing a route taking into account both broad area canvassing and community capitals transects. We also implemented procedures for uploading and publishing data from each survey. Our methods successfully generated the kind of longitudinal data that can be beneficial to a variety of research disciplines. However, there were some challenges with data collection consistency…
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Urban Transport and Accessibility · Place Attachment and Urban Studies
