Wondering if anyone has taken a dive into nighttime (2nd/3rd shift) workers for this dataset?

Wondering if anyone has taken a dive into nighttime (2nd/3rd shift) workers for this dataset? Or perhaps migrant workers in farming-heavy areas? The home algorithm seems as if it would handle the data from these individuals incorrectly.

A related issue is with college students, many of whom left campuses and student housing when the pandemic started. We saw weird jumps in the “completely at home” numbers near universities. We think when people move away, they at first appear to not be “at home” (so the “shelter in place” index would look very bad) then SafeGraph probably re-assigns their home CBGs after a few weeks (so the “shelter in place” index for the college neighborhood looks ok again).

Many college students went back to campus in the fall. So if you want to study what happens to Social Distancing data when thousands of new people move to a neighborhood, check university calendars and the populations on or near campuses.

Hi @HJardel_UNC-CH, to add on to what @Dennis_Chao_Institute_for_Disease_Modeling said here, here is an older thread on a similar issue regarding 2nd and 3rd shift workers

The TL;DR is it kind of depends on each case. If the worker consistently works 2nd or 3rd shift it will likely attribute that location to home since SafeGraph has no way of knowing if someone is sleeping or not.


I will keep digging around in threads to see if there is anything more recent, but I believe this information still remains true.

@HJardel_UNC-CH I don’t have any further information than what @Jack_Lindsay_Kraken1 said. My guess is that you’d have to run some time of regressions with demographic/business variables to try and tease that out, but I don’t think that would work well either.