DEVELOPING SPATIO-TEMPORAL DYNAMIC CLUSTERING ALGORITHMS FOR IDENTIFYING CRIME HOT SPOTS IN KUWAIT
Soliman, T., Al Ommar, K., Mahdy, Y. (2015). DEVELOPING SPATIO-TEMPORAL DYNAMIC CLUSTERING ALGORITHMS FOR IDENTIFYING CRIME HOT SPOTS IN KUWAIT. EKB Journal Management System, 43(No 1), 1-15. doi: 10.21608/jesaun.2015.111010
Taysir H. A. Soliman; Khulood Al Ommar; Youssef B. Mahdy. "DEVELOPING SPATIO-TEMPORAL DYNAMIC CLUSTERING ALGORITHMS FOR IDENTIFYING CRIME HOT SPOTS IN KUWAIT". EKB Journal Management System, 43, No 1, 2015, 1-15. doi: 10.21608/jesaun.2015.111010
Soliman, T., Al Ommar, K., Mahdy, Y. (2015). 'DEVELOPING SPATIO-TEMPORAL DYNAMIC CLUSTERING ALGORITHMS FOR IDENTIFYING CRIME HOT SPOTS IN KUWAIT', EKB Journal Management System, 43(No 1), pp. 1-15. doi: 10.21608/jesaun.2015.111010
Soliman, T., Al Ommar, K., Mahdy, Y. DEVELOPING SPATIO-TEMPORAL DYNAMIC CLUSTERING ALGORITHMS FOR IDENTIFYING CRIME HOT SPOTS IN KUWAIT. EKB Journal Management System, 2015; 43(No 1): 1-15. doi: 10.21608/jesaun.2015.111010

DEVELOPING SPATIO-TEMPORAL DYNAMIC CLUSTERING ALGORITHMS FOR IDENTIFYING CRIME HOT SPOTS IN KUWAIT

JES. Journal of Engineering Sciences
Article 1, Volume 43, No 1, January and February 2015, Page 1-15  XML PDF (1.26 MB)
Document Type: Research Paper
DOI: 10.21608/jesaun.2015.111010
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Authors
Taysir H. A. Soliman email 1; Khulood Al Ommar1; Youssef B. Mahdy2
1Information Systems Dept., Faculty of Computers and Information, Assiut University, Egypt
2Computer Science Dept., Faculty of Computers and Information, Assiut University, Egypt
Abstract
As crime rates are increasing worldwide, crime mining requires more efficient algorithms that can handle current situations. Identifying crime hot spot areas via clustering spatio-temporal data is an emerging research area. In this paper, dynamic clustering algorithms for spatio-temporal crime data are proposed to detect hot crime spots in Kuwait. Kuwait governorates are taken as case study: the capital, Hawalli, Al-Ahmady, Al-Jahra, Al-Farawaniya, and Mubarak Al-kebeer. In addition, different crime types are considered: act of discharge and humiliation, adultery, aggravated assault, bribery, counter fitting, drugs, embezzlement, fight or resist employee on job, forging of official documents, weapon, robbery and attempted robbery, suicide and attempted suicide, and bank theft. Applying Random subspace classification to those clustered data, 98% accuracy and 99.4% ROC are obtained, having precision (98.7%), recall (98.4%), and F1 (98.28%).
Keywords
Spatio-temporal data mining; hot spot detection; intelligent crime mining; random subspace classification; and clustering
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