  Citizen science projects in which volunteers collect data are increasingly
popular due to their ability to engage the public with scientific questions.
The scientific value of these data are however hampered by several biases. In
this paper, we deal with geospatial sampling bias by enriching the
volunteer-collected data with geographical covariates, and then using
regression-based models to correct for bias. We show that night sky brightness
estimates change substantially after correction, and that the corrected
inferences better represent an external satellite-derived measure of skyglow.
We conclude that geospatial bias correction can greatly increase the scientific
value of citizen science projects.
