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B0589
Title: Policing route optimization via density-based principal curves Authors:  Ben Moews - The University of Edinburgh (United Kingdom) [presenting]
Jaime R Argueta - University of Cincinnati (United States)
Antonia Gieschen - The University of Edinburgh (United Kingdom)
Abstract: A new method is introduced for identifying patrol routes in hot spots through ridge estimation to explore the application of density ridges to hot spots and patrol optimization, and to contribute to the literature in police patrolling. 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. The experiments extract density ridges of Part I crime incidents from the City of Chicago during the year 2018 and early 2019, with results demonstrating 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 demonstrate near-complete coverage with narrow envelopes around ridges. We also develop and provide researchers, as well as practitioners, with a user-friendly and open-source software for fast geospatial density ridge estimation. 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.