A 2D Clustering Based Hotspot Identification Approach for Spatio-Temporal Crime Prediction

Muhammad Faisal Buland Iqbal; Aman Ullah; Ahmed Alhomoud; Tariq Hussain; Razaz Waheeb Attar; Jianquan Ouyang; Mrim M. Alnfiai; Wesam Atef Hatamleh

doi:10.9781/ijimai.2024.10.001

Authors

Muhammad Faisal Buland Iqbal Xiangtan University
Aman Ullah Virtual University of Pakistan
Ahmed Alhomoud Northern Border University
Tariq Hussain Zhejiang Gongshang University
Razaz Waheeb Attar Princess Nourah bint Abdulrahman University
Jianquan Ouyang Xiangtan University
Mrim M. Alnfiai Taif University
Wesam Atef Hatamleh King Saud University

DOI:

https://doi.org/10.9781/ijimai.2024.10.001

Keywords:

2-D Hotspot Analysis, Crime Prediction, Forensics, Predictive Policing, Spatial, Temporal Clustering

Supporting Agencies

This research is supported by the National Natural Science Foundation of China under Grant 62172366 and Key Projects of the Ministry of Science and Technology of the People's Republic of China (2018AAA0102301). This research is also supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R 343) Riyadh, Saudi Arabia. The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number “NBU-FFR-2024-1092-12”. This research was funded by Taif University, Saudi Arabia, Project No. (TUDSPP-2024-41).

Abstract

This research introduces a method for predicting where crimes will occur based on clustering activity in the area. Hotspots, or locations with a disproportionately high number of crimes, are located by a combination of spatial and temporal grouping methods employed by this strategy. Crime forecasting models use these hotspots to predict where crimes will occur. The approach's efficacy is tested using actual crime data, and it successfully predicts future crimes in high-crime zones. Law enforcement agencies can use the proposed method to protect the public better, and it shows promise as a tool for crime prediction. Academic research into the topic of foreseeing criminal behavior is a newer development. Researchers in this discipline have discovered that criminal behavior has universal patterns. These patterns may help law enforcement agencies plan for criminal activities. Predictive policing, hotspot analysis, and geographical profiling are examples of when crime forecasting has been useful. Several aspects of the census, such as the average yearly income and literacy rate, are related to the prevalence of crime in a certain area. Indicators of potentially criminal behavior, these characteristics may be seen as markers. This investigation aims to discover if any clues can be gleaned from past criminal behavior that may be utilized to forecast future criminal behavior. Using machine learning and 2-D Hotspot analysis, we propose a method for the spatiotemporal prediction of criminal activity within the scope of this study. Clustering is a method used in 2-dimensional hotspot analysis. Methods of modern categorization, both with and without hotspot analysis, are used to evaluate the suggested model's efficacy. It is found that the model that incorporates hotspot analysis performs better than the one that does not.

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