Risk and Pattern Analysis of Pakistani Crime Data Using Unsupervised Learning Techniques
To reduce crime rates, there is a need to understand and analyse emerging patterns of criminal activities. This study examines the occurrence patterns of crimes using the crime dataset of Lahore, a metropolitan city in Pakistan. The main aim is to facilitate crime investigation and future risk analy...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/7/3675 |
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| author | Faria Ferooz Malik Tahir Hassan Sajid Mahmood Hira Asim Muhammad Idrees Muhammad Assam Abdullah Mohamed El-Awady Attia |
| author_facet | Faria Ferooz Malik Tahir Hassan Sajid Mahmood Hira Asim Muhammad Idrees Muhammad Assam Abdullah Mohamed El-Awady Attia |
| author_sort | Faria Ferooz |
| collection | DOAJ |
| description | To reduce crime rates, there is a need to understand and analyse emerging patterns of criminal activities. This study examines the occurrence patterns of crimes using the crime dataset of Lahore, a metropolitan city in Pakistan. The main aim is to facilitate crime investigation and future risk analysis using visualization and unsupervised data mining techniques including clustering and association rule mining. The visualization of data helps to uncover trends present in the crime dataset. The K-modes clustering algorithm is used to perform the exploratory analysis and risk identification of similar criminal activities that can happen in a particular location. The Apriori algorithm is applied to mine frequent patterns of criminal activities that can happen on a particular day, time, and location in the future. The data were acquired from paper-based records of three police stationsin the Urdu language. The data were then translated into English and digitized for automatic analysis. The result helped identify similar crime-related activities that can happen in a particular location, the risk of potential criminal activities occurring on a specific day, time, and place in the future, and frequent crime patterns of different crime types. The proposed work can help the police department to detect crime events and situations and reduce crime incidents in the early stages by providing insights into criminal activity patterns. |
| first_indexed | 2024-03-09T12:05:11Z |
| format | Article |
| id | doaj.art-e6b52bdcdb2e415c8d8ac1d6865b8d8d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T12:05:11Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e6b52bdcdb2e415c8d8ac1d6865b8d8d2023-11-30T22:58:59ZengMDPI AGApplied Sciences2076-34172022-04-01127367510.3390/app12073675Risk and Pattern Analysis of Pakistani Crime Data Using Unsupervised Learning TechniquesFaria Ferooz0Malik Tahir Hassan1Sajid Mahmood2Hira Asim3Muhammad Idrees4Muhammad Assam5Abdullah Mohamed6El-Awady Attia7Department of Software Engineering, University of Management and Technology, Lahore 54770, PakistanDepartment of Software Engineering, University of Management and Technology, Lahore 54770, PakistanDepartment of Informatics and Systems, University of Management and Technology, Lahore 54770, PakistanDepartment of Software Engineering, University of Management and Technology, Lahore 54770, PakistanDepartment of Computer Science and Engineering, University of Engineering and Technology Lahore, Narowal Campus, Lahore 54400, PakistanCollege of Computer Science and Technology, Zhejiang University, Hangzhou 310027, ChinaResearch Centre, Future University in Egypt, New Cairo 11835, EgyptDepartment of Industrial Engineering, Prince Sattam Bin Abdulaziz University, AI Kharj 16273, Saudi ArabiaTo reduce crime rates, there is a need to understand and analyse emerging patterns of criminal activities. This study examines the occurrence patterns of crimes using the crime dataset of Lahore, a metropolitan city in Pakistan. The main aim is to facilitate crime investigation and future risk analysis using visualization and unsupervised data mining techniques including clustering and association rule mining. The visualization of data helps to uncover trends present in the crime dataset. The K-modes clustering algorithm is used to perform the exploratory analysis and risk identification of similar criminal activities that can happen in a particular location. The Apriori algorithm is applied to mine frequent patterns of criminal activities that can happen on a particular day, time, and location in the future. The data were acquired from paper-based records of three police stationsin the Urdu language. The data were then translated into English and digitized for automatic analysis. The result helped identify similar crime-related activities that can happen in a particular location, the risk of potential criminal activities occurring on a specific day, time, and place in the future, and frequent crime patterns of different crime types. The proposed work can help the police department to detect crime events and situations and reduce crime incidents in the early stages by providing insights into criminal activity patterns.https://www.mdpi.com/2076-3417/12/7/3675crime analyticsclusteringrisk identificationfrequent pattern miningdata miningpublic safety |
| spellingShingle | Faria Ferooz Malik Tahir Hassan Sajid Mahmood Hira Asim Muhammad Idrees Muhammad Assam Abdullah Mohamed El-Awady Attia Risk and Pattern Analysis of Pakistani Crime Data Using Unsupervised Learning Techniques Applied Sciences crime analytics clustering risk identification frequent pattern mining data mining public safety |
| title | Risk and Pattern Analysis of Pakistani Crime Data Using Unsupervised Learning Techniques |
| title_full | Risk and Pattern Analysis of Pakistani Crime Data Using Unsupervised Learning Techniques |
| title_fullStr | Risk and Pattern Analysis of Pakistani Crime Data Using Unsupervised Learning Techniques |
| title_full_unstemmed | Risk and Pattern Analysis of Pakistani Crime Data Using Unsupervised Learning Techniques |
| title_short | Risk and Pattern Analysis of Pakistani Crime Data Using Unsupervised Learning Techniques |
| title_sort | risk and pattern analysis of pakistani crime data using unsupervised learning techniques |
| topic | crime analytics clustering risk identification frequent pattern mining data mining public safety |
| url | https://www.mdpi.com/2076-3417/12/7/3675 |
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