A Bayesian Spatial Model to Correct Under-Reporting in Urban
  Crowdsourcing
Gabriel Agostini Emma Pierson Nikhil Garg
Decision-makers often observe the occurrence of events through a reporting
process. City governments, for example, rely on resident reports to find and
then resolve urban infrastructural problems such as fallen street trees,
flooded basements, or rat infestations. Without additional assumptions, there
is no way to distinguish events that occur but are not reported from events
that truly did not occur--a fundamental problem in settings with
positive-unlabeled data. Because disparities in reporting rates correlate with
resident demographics, addressing incidents only on the basis of reports leads
to systematic neglect in neighborhoods that are less likely to report events.
We show how to overcome this challenge by leveraging the fact that events are
spatially correlated. Our framework uses a Bayesian spatial latent variable
model to infer event occurrence probabilities and applies it to storm-induced
flooding reports in New York City, further pooling results across multiple
storms. We show that a model accounting for under-reporting and spatial
correlation predicts future reports more accurately than other models, and
further induces a more equitable set of inspections: its allocations better
reflect the population and provide equitable service to non-white, less
traditionally educated, and lower-income residents. This finding reflects
heterogeneous reporting behavior learned by the model: reporting rates are
higher in Census tracts with higher populations, proportions of white
residents, and proportions of owner-occupied households. Our work lays the
groundwork for more equitable proactive government services, even with
disparate reporting behavior.