Title: Spatial and temporal trends reveal: hotspot identification of crimes using machine learning approach
Authors: Khushboo Sukhija; Shailendra Narayan Singh; Mukesh Kumar; Deepti Mehrotra
Addresses: Department of Computer Science and Engineering, Amity University, Noida, 201303, India ' Department of Computer Science and Engineering, Amity University, Noida, 201303, India ' Department of Computer Science and Engineering, TIT&S, 127021, India ' Department of Computer Science and Engineering, Amity University, Noida, 201303, India
Abstract: With the escalation in criminal cases, most of the population all over the country are becoming victims of different types of crime, which is one of the major concerns in the evolution of society. Therefore, hotspot identification of crimes by analysing real-time dataset has become essential and will significantly benefit the public by accurately analysing the dangerous locations. This paper aims to develop the framework model for identifying criminal hotspots using a modified K-nearest neighbour (KNN) algorithm by considering different crime characteristics like the severity of the crime, frequency of crime and temporal data of crime by visualising hotspots using geographic information system (GIS). This study analyses the real dataset of crime for the recent five years collected from the Commissioner of Police of Gurgaon, Haryana. The data cleaning and pre-processing strategies have been applied to make data ready for further training the model. The results demonstrate locations of the different hotspots based on the density of crime occurrences, and accurate visualisation of hotspots using GIS display is done by supervised learning and unsupervised classifiers. The claims have been validated through a proposed model, the modified KNN algorithm, with a comprehensive accuracy of around 99% by appropriately tuning and optimising the parameters.
Keywords: hotspot; crime; GIS; supervised learning; unsupervised learning; spatial analysis; temporal analysis; network.
International Journal of Computational Science and Engineering, 2022 Vol.25 No.2, pp.174 - 185
Received: 16 Dec 2020
Accepted: 26 May 2021
Published online: 12 Apr 2022 *