AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (1.5 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Article | Open Access

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

Rashid Ahmad1Asif Nawaz1( )Ghulam Mustafa1Tariq Ali1Mehdi Tlija2Mohammed A. El-Meligy3,4Zohair Ahmed5
University Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi, 46000, Pakistan
Industrial Engineering Department, College of Engineering, King Saud University, Riyadh, 11421, Saudi Arabia
Jadara University Research Center, Jadara University, Irbid, 21110, Jordan
Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan
School of Computer Science and Engineering, Central South University, Changsha, 410083, China
Show Author Information

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.

References

【1】
【1】
 
 
Computers, Materials & Continua
Pages 4171-4194

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Close
Close
Cite this article:
Ahmad R, Nawaz A, Mustafa G, et al. CHART: Intelligent Crime Hotspot Detection and Real-Time Tracking Using Machine Learning. Computers, Materials & Continua, 2024, 81(3): 4171-4194. https://doi.org/10.32604/cmc.2024.056971

10

Views

0

Downloads

8

Crossref

3

Web of Science

7

Scopus

Received: 04 August 2024
Accepted: 23 October 2024
Published: 31 December 2024
© The Author 2024.

This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.