  Mobile sensing has played a key role in providing digital solutions to aid
with COVID-19 containment policies. These solutions include, among other
efforts, enforcing social distancing and monitoring crowd movements in indoor
spaces. However, such solutions may not be effective without mass adoption. As
more and more countries reopen from lockdowns, there remains a pressing need to
minimize crowd movements and interactions, particularly in enclosed spaces.
This paper conjectures that analyzing user occupancy and mobility via deployed
WiFi infrastructure can help institutions monitor and maintain safety
compliance according to the public health guidelines. Using smartphones as a
proxy for user location, our analysis demonstrates how coarse-grained WiFi data
can sufficiently reflect indoor occupancy spectrum when different COVID-19
policies were enacted. Our work analyzes staff and students' mobility data from
three different university campuses. Two of these campuses are in Singapore,
and the third is in the Northeastern United States. Our results show that
online learning, split-team, and other space management policies effectively
lower occupancy. However, they do not change the mobility for individuals
transitioning between spaces. We demonstrate how this data source can be put to
practical application for institutional crowd control and discuss the
implications of our findings for policy-making.
