Privacy Preserving, Crowd Sourced Crime Hawkes Processes

Social sensing plays an important role in crime analytics and predictive policing. When humans play the role of sensor, several issues around privacy and trust emerge that must be carefully handled. We provide a framework for deploying predictive crime models based upon crowd-sourced information (crime reports, tips, Nextdoor posts, etc.) while protecting individual privacy and striving for a high level of algorithmic transparency. For this purpose we introduce a novel online Hawkes process estimation algorithm requiring no event history coupled with an online k-means type algorithm based upon the word movers distance. We illustrate the methodology using synthetic data, crime report data from Los Angeles, and public safety posts from Nextdoor in Indianapolis. In particular, we show that privacy and transparency can be maintained without sacrificing accuracy in space-time models of criminal incidents. Furthermore, our methodology provides a framework for sharing of information between private companies collecting crime tips or public safety information, law enforcement agencies, and the general public.