Visual Crime Pattern Analysis

Abstract

Studying and analyzing crime patterns in big cities is a challenging spatio-temporal problem. The difficulty of the problem is linked to different factors such as data modeling, unsophisticated hotspot detection techniques, spatio-temporal patterns, and study delimitation. Previous works have mostly focused on the analysis of crimes with the intent of uncovering patterns associated to social factors, seasonality, and urban activities in whole districts, regions, and neighborhoods. Those tools can hardly allow micro-scale crime analysis closely related to crime opportunity, whose understanding is fundamental for planning preventive actions. Given that, enabling a combined analysis of spatial patterns and the visualization of the different crime patterns hidden in their evolution over time is another challenge faced by most crime analysis tools. In this dissertation, we propose a set of approaches for interactive visual crime analysis. Relying on machine learning methods, statistical and mathematical mechanisms, and visualization, each proposed methodology focus on solving specific crime-related problems. These proposed tools to explore specific locations of the city turned out to be essential for domain experts to accomplish their analysis in a bottom-up fashion, revealing how urban features related to mobility, passerby behavior, and presence of public infrastructures (e.g., terminals of public transportation and schools) can influence the quantity and type of crimes. The effectiveness and usefulness of the proposed methodologies have been demonstrated with a comprehensive set of quantitative and qualitative analyses, as well as case studies performed by domain experts involving real data of different-sized cities. The experiments show the capability of our approaches in identifying different crime-related phenomena.