Series mining for public safety advancement in emerging smart cities

Abstract

The identification of crime series is of great importance for public safety in a smart city development. This research presents a novel crime clustering model, CriClust+, for detecting Crime Series Pattern (CSP). The analysis is augmented using geometric projection with a dual-threshold model. The pattern prevalence information extracted from the model is encoded in similarity graphs. Clusters are identified by finding highly-connected subgraphs using adaptive graph size and Monte-Carlo heuristics in the Karger–Stein mincut algorithm. We propose two new interest measures: (i) Proportion Difference Evaluation (PDE), which reveals the propagation effect of a series and dominant series; and (ii) Pattern Space Enumeration (PSE), which reveals strong underlying correlations and defining features for a series. Our findings on experimental dataset based on a Gaussian distribution and expert knowledge recommendation reveal that, identifying CSP and statistically interpretable patterns could contribute significantly to strengthening public safety service delivery. Evaluation was conducted to investigate: (i) the reliability of the model in identifying all inherent series in a crime dataset; (ii) the scalability of the model with varying crime records volume; and (iii) unique features of the model compared to related research. The study also found that PDE and PSE of series clusters can provide valuable insight into crime deterrence strategies. This research presents considerable empirical evidence, which shows that the proposed crime clustering (CriClust+) model is promising and can assist in deriving useful crime pattern knowledge.