Cellphone data shows Bay Area has become less compliant with stay-at-home orders

Unacast, a data company, created the Social Distancing Scoreboard, which uses data from cellphones to assign a letter grade from A through F to counties in the United States depending on their compliancy with stay-at-home orders. According to Thomas Walle, Unacast CEO and co-founder, each county and state is graded based on three sets of criteria. (Photo by Alfred Twu under CC0 1.0 Universal .)

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Cellphone data collected by Unacast, a data company, suggests that the Bay Area is not as compliant with stay-at-home orders as it was in the spring.

Unacast is a firm that, among other aims, seeks to assimilate data from phone users in order to help create the Unacast Social Distancing Scoreboard, according to Thomas Walle, Unacast CEO and co-founder. The data is then used to assign a letter grade from A through F to counties around the country based on how compliant they are with stay-at-home orders.

“We created this pro bono Social Distancing Scoreboard as the first of many tools we are developing for a Unacast COVID-19 toolkit,” Walle said. “It is designed to provide high-quality insights to public agencies, health care organizations, local governments and businesses to enable them to learn and act in the best interest of at-risk populations and the general public.”

According to Walle, each county and state is graded upon three sets of criteria: change in average distance traveled, change in nonessential visits and change in the number of encounters with others.

Just two weeks after five Bay Area counties took up early stay-at-home orders Dec. 5, the Bay Area shows only one county reaching an A grade, according to Unacast’s scoreboard. In contrast, data from March 30 showed nearly all Bay Area counties achieving an A grade.

Some of this change, however, can be attributed to Unacast’s addition of new criteria, as well as the change in letter grade thresholds, Walle added. In other words, it is harder to achieve an A grade now than it was in the spring.

“Our team quickly realized that there was a second underlying challenge: As the country’s behavior changed, so did the underlying data,” Walle said. “The models, which we had built and optimized towards a non-COVID-19 world, now need revisiting.”

The original model for detecting where residents were located was centered around the assumption that a resident’s home was also the device’s home. As the pandemic progressed, however, many residents have moved to other areas, thereby making the original model not as effective, Walle added.

According to Walle, as the pandemic persists, the Unacast team will continue to update and refine its scoreboard system to aid in the fight against COVID-19.

Audry Jeong is a research and ideas reporter. Contact her at [email protected] and follow her on Twitter at @audryjng_dc.