Clustering Ensemble And Hybrid Of Deep Learning For Spatio-Temporal Crime Predictions - Butt, Umair Muneer (2024)
The increase in the urban population poses challenges in managing services and safety from criminal activities. The concerned stakeholders intend to predict the time, location, number, and types of crimes to take suitable preventive measures. Accurate identification and prediction of crime hotspots can significantly benefit the concerned stakeholders in preventing crime by creating accurate threat visualizations and allocating police resources efficiently. Several techniques have been proposed for crime prediction, but they are limited in accuracy and predicting crime according to crime type on an hourly, monthly, and seasonal basis. Crime hotspot detection approaches are primarily sensitive to initial parameter selection and finding clusters of varying shapes and densities. Similarly, existing Crime prediction approaches are limited in capturing non-stationary data and long-term dependencies by focusing on crime types. Thus, the crime detection and prediction mechanisms need improvement in the number of crimes, crime span, accuracy, and dense crime region and prediction. The core objective of this study is twofold. First, it proposes a crime hotspot detection model to improve accuracy using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) and its clustering ensemble to capture varying shapes and densities clusters and improve accuracy. HDBSCAN is used with varying parameter initialization in the generation mechanism under the cluster ensemble paradigm. Moreover, six different distance measures are used to ensure diversity. In addition, an evaluation function is proposed parameterized by silhouette score to select the stable clustering among a pool of clustering solutions to ensure quality.