Open Access

ARTICLE

CHART: Intelligent Crime Hotspot Detection and Real-Time Tracking Using Machine Learning

Rashid Ahmad¹, Asif Nawaz^1,*, Ghulam Mustafa¹, Tariq Ali¹, Mehdi Tlija², Mohammed A. El-Meligy^3,4, Zohair Ahmed⁵

1 University Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi, 46000, Pakistan
2 Industrial Engineering Department, College of Engineering, King Saud University, Riyadh, 11421, Saudi Arabia
3 Jadara University Research Center, Jadara University, Irbid, 21110, Jordan
4 Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan
5 School of Computer Science and Engineering, Central South University, Changsha, 410083, China

* Corresponding Author: Asif Nawaz. Email:

Computers, Materials & Continua 2024, 81(3), 4171-4194. https://doi.org/10.32604/cmc.2024.056971

Received 04 August 2024; Accepted 23 October 2024; Issue published 19 December 2024

Abstract

Crime hotspot detection is essential for law enforcement agencies to allocate resources effectively, predict potential criminal activities, and ensure public safety. Traditional methods of crime analysis often rely on manual, time-consuming processes that may overlook intricate patterns and correlations within the data. While some existing machine learning models have improved the efficiency and accuracy of crime prediction, they often face limitations such as overfitting, imbalanced datasets, and inadequate handling of spatiotemporal dynamics. This research proposes an advanced machine learning framework, CHART (Crime Hotspot Analysis and Real-time Tracking), designed to overcome these challenges. The proposed methodology begins with comprehensive data collection from the police database. The dataset includes detailed attributes such as crime type, location, time and demographic information. The key steps in the proposed framework include: Data Preprocessing, Feature Engineering that leveraging domain-specific knowledge to extract and transform relevant features. Heat Map Generation that employs Kernel Density Estimation (KDE) to create visual representations of crime density, highlighting hotspots through smooth data point distributions and Hotspot Detection based on Random Forest-based to predict crime likelihood in various areas. The Experimental evaluation demonstrated that CHART shows superior performance over benchmark methods, significantly improving crime detection accuracy by getting 95.24% for crime detection-I (CD-I), 96.12% for crime detection-II (CD-II) and 94.68% for crime detection-III (CD-III), respectively. By designing the application with integrating sophisticated preprocessing techniques, balanced data representation, and advanced feature engineering, the proposed model provides a reliable and practical tool for real-world crime analysis. Visualization of crime hotspots enables law enforcement agencies to strategize effectively, focusing resources on high-risk areas and thereby enhancing overall crime prevention and response efforts.

---

Keywords

---