Identifying Under-Reported Events in Networks with Spatial Latent Variable Models
Keywords: bayesian modeling, crowdsourcing, PU learning, spatial hidden Markov model
Abstract: Decision-makers often observe the occurrence of events through a reporting process. City governments, for example, rely on resident reports to register and then resolve urban infrastructural problems such as fallen street trees, over-flooding sewers, or rat infestations. In the absence of additional assumptions, events that occur but are not reported cannot be distinguished from events that truly did not occur, leading to systematic neglect in addressing problems in neighborhoods that comparatively under-report events. In this paper, we leverage a Bayesian model to describe this setting in the presence of network correlations in the event occurrence process. We present a sampling routine to estimate the report rates and the event occurrence incidence, as well as infer the ground truth of discrete latent states. We apply the model to flooding reports in New York City, publicly available via the 311 data portal.
Submission Number: 68
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