PCPD: A Parallel Crime Pattern Discovery System for Large-Scale Spatiotemporal Data Based on Fuzzy Clustering

In this paper, we focus on the discovering criminal behaviors and patterns issue and propose a Parallel Crime Pattern Discovery system using machine learning and high-performance computing techniques. We formulate the problem of criminal behaviors and propose a Criminal Activity Clustering (CAC) algorithm based on fuzzy clustering to detect potential criminal patterns in large-scale spatiotemporal datasets. Based on the detected criminal patterns, we further propose a Crime Rate Evaluation (CRE) algorithm to identify the crime rate for each group of locations and target types. In addition, we propose a Criminal Hotspot Locating (CHL) algorithm to predict and highlight the hotspot areas for the prevention of the target place. Moreover, to improve the performance of the proposed CPD system that mainly contains CAC, CRE, and CHL algorithms, we implement a parallel solution for these algorithms using high-performance computing power. Experimental results show that the proposed algorithms can effectively detect accurate criminal patterns from large-scale spatiotemporal data.