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We present a novel approach to estimate the delay observed between theoccurrence and reporting of rape crimes. We explore spatial, temporal andsocial effects in sparse aggregated (area-level) and high-dimensionaldisaggregated (event-level) data for New York and Los Angeles. Focusing oninference, we apply Gradient Boosting and Random Forests to assess predictorimportance, as well as Gaussian Processes to model spatial disparities inreporting times. Our results highlight differences and similarities between thetwo cities. We identify at-risk populations and communities which may betargeted with focused policies and interventions to support rape victims,apprehend perpetrators, and prevent future crimes.

Modeling Rape Reporting Delays Using Spatial, Temporal and Social Features