Title: Kernel methods for spatiotemporal learning in criminology (or, the methods behind our winning entry in the US National Institute of Justice’s crime forecasting challenge)
Abstract: In this talk I will highlight the statistical machine learning methods that I am developing to address public policy questions in criminology. We develop a scalable inference method for the log-Gaussian Cox Process, and show that an expressive kernel parameterisation can learn space/time structure in a large point pattern dataset [Flaxman et al, ICML 2015]. Our approach has nearly linear scaling, allowing us to efficiently fit a point pattern dataset of n = 233,088 crime events over a decade in Chicago and discover spatially varying multiscale seasonal trends and produce highly accurate long-range local area forecasts. Building on this work, we used scalable approximate kernel methods to provide a winning solution to the US National Institute of Justice “Real-Time Crime Forecasting Challenge,” providing forecasts of four types of crime at a very local level (less than 1 square mile) 1 week, 1 month, and 3 months into the future. In another line of work, we use a Hawkes process model to quantify the spatial and temporal scales over which shooting events diffuse in Washington, DC, using data collected by an acoustic gunshot locator system, in order to assess the hypothesis that crime is an infectious process. While we find robust evidence for spatiotemporal diffusion, the spatial and temporal scales are extremely short (126 meters and 10 minutes), and thus more likely to be consistent with a discrete gun fight, lasting for a matter of minutes, than with a diffusing, infectious process linking violent events across hours, days, or weeks [Loeffler and Flaxman, Journal of Quantitative Criminology 2017] Papers and replication code available at www.sethrf.com