Open-source data pipeline for street-view images: a case study on
  community mobility during COVID-19 pandemic
Matthew Martell Nick Terry Ribhu Sengupta Chris Salazar Nicole A. Errett Scott B. Miles Joseph Wartman Youngjun Choe
Street View Images (SVI) are a common source of valuable data for
researchers. Researchers have used SVI data for estimating pedestrian volumes,
demographic surveillance, and to better understand built and natural
environments in cityscapes. However, the most common source of publicly
available SVI data is Google Street View. Google Street View images are
collected infrequently, making temporal analysis challenging, especially in low
population density areas. Our main contribution is the development of an
open-source data pipeline for processing 360-degree video recorded from a
car-mounted camera. The video data is used to generate SVIs, which then can be
used as an input for temporal analysis. We demonstrate the use of the pipeline
by collecting a SVI dataset over a 38-month longitudinal survey of Seattle, WA,
USA during the COVID-19 pandemic. The output of our pipeline is validated
through statistical analyses of pedestrian traffic in the images. We confirm
known results in the literature and provide new insights into outdoor
pedestrian traffic patterns. This study demonstrates the feasibility and value
of collecting and using SVI for research purposes beyond what is possible with
currently available SVI data. Limitations and future improvements on the data
pipeline and case study are also discussed.