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Identifying Crime Hotspot: Evaluating the suitability of Supervised and Unsupervised Machine learning

Hussein, Abdul Aziz
http://orcid.org/0000-0003-0238-1356
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1624914607243042

Abstract Details

2021, MS, University of Cincinnati, Education, Criminal Justice, and Human Services: Information Technology.
Crime hotspot locations identification is a very important endeavor to help ensure public safety. Been able to identify these locations effectively and accurately will help provide useful information to law enforcement bodies to help minimize criminal activities. Considering the limited resources available to law enforcements, a more prudent approach will be to deploy these resources at places that record a considerable higher crime rate. We depart from the traditional “higher than” average thresholds and rather rely on a more pragmatic approach in the analysis. We analyze a five-year crime data from the Cincinnati Police Department using clustering algorithms such K-means, DBSCAN, Hierarchical algorithms, and classification machine learning algorithms such as Random Forest, SVM, Logistic Regression, KNN, and Naive Bayes, on the same dataset. The clustering methods are used as a standalone means of identifying crime hotspots rather than used as a data preprocessing step as done in prior experiments. The results from both approaches are compared using their respective evaluation metrics. From the performances, we find classification performed better than clustering on our dataset. The best performing algorithm is the Random Forest when the number of trees is 30. We also find considerable crime concentration along the hotspot street segments that were identified in the dataset.
M. Murat Ozer, Ph.D. (Committee Chair)
Nelly Elsayed, Ph.D. (Committee Member)
43 p.
Information Technology
Crime hotspots; machine learning; supervised learning; unsupervised learning; classification; clustering

Recommended Citations

Citations

  • Hussein, A. A. (2021). Identifying Crime Hotspot: Evaluating the suitability of Supervised and Unsupervised Machine learning [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1624914607243042

    APA Style (7th edition)

  • Hussein, Abdul Aziz. Identifying Crime Hotspot: Evaluating the suitability of Supervised and Unsupervised Machine learning. 2021. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1624914607243042.

    MLA Style (8th edition)

  • Hussein, Abdul Aziz. "Identifying Crime Hotspot: Evaluating the suitability of Supervised and Unsupervised Machine learning." Master's thesis, University of Cincinnati, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1624914607243042

    Chicago Manual of Style (17th edition)

ucin1624914607243042
408
© 2021, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.