Spatio-temporal clustering in application

The importance of machine learning methods in the data analysis of both academic research and industry applications has advanced rapidly in recent years. This thesis will investigate how a method of unsupervised machine learning known as clustering can be employed to analyse spatial and spatio-temporal data from different fields of application. Spatio-temporal data present a particular challenge. In spatial contexts, the notion of dependency among geographically close elements needs to be considered when analysing the geographic distance as well as other spatial components. The temporal dimension of the data makes traditional dissimilarity metrics unsuitable due to the sequential ordering of data points. For this reason, this thesis will present ways of overcoming the shortcomings in existing methodologies when applied to these data types. By doing so, it will contribute to the literature on clustering through innovative extensions, adaptations, and considerations. The flexibility of clustering will be demonstrated in three different application contexts in health, finance, and marketing. As such, this thesis will also contribute to the academic literature in these areas and offer valuable insights into applicable machine learning methodology for practitioners

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