1 research outputs found
Filaments of crime: Informing policing via thresholded ridge estimation
Objectives: We introduce a new method for reducing crime in hot spots and
across cities through ridge estimation. In doing so, our goal is to explore the
application of density ridges to hot spots and patrol optimization, and to
contribute to the policing literature in police patrolling and crime reduction
strategies.
Methods: We make use of the subspace-constrained mean shift algorithm, a
recently introduced approach for ridge estimation further developed in
cosmology, which we modify and extend for geospatial datasets and hot spot
analysis. Our experiments extract density ridges of Part I crime incidents from
the City of Chicago during the year 2018 and early 2019 to demonstrate the
application to current data.
Results: Our results demonstrate nonlinear mode-following ridges in agreement
with broader kernel density estimates. Using early 2019 incidents with
predictive ridges extracted from 2018 data, we create multi-run confidence
intervals and show that our patrol templates cover around 94% of incidents for
0.1-mile envelopes around ridges, quickly rising to near-complete coverage. We
also develop and provide researchers, as well as practitioners, with a
user-friendly and open-source software for fast geospatial density ridge
estimation.
Conclusions: We show that ridges following crime report densities can be used
to enhance patrolling capabilities. Our empirical tests show the stability of
ridges based on past data, offering an accessible way of identifying routes
within hot spots instead of patrolling epicenters. We suggest further research
into the application and efficacy of density ridges for patrolling.Comment: 17 pages, 3 figure