Volume 6, Issue 1, February 2020, Page: 20-31
Using Data Mining Techniques and R Software to Analyze Crime Data in Kenya
Stephen Mangara Wainana, Department of Mathematics and Informatics, Taita Taveta University, Nairobi, Kenya
Joseph Njuguna Karomo, Department of Pure and Applied Sciences, Kirinyaga University, Nairobi, Kenya
Rachael Kyalo, Department of Mathematics and Informatics, Taita Taveta University, Nairobi, Kenya
Noah Mutai, Department of Mathematics and Informatics, Taita Taveta University, Nairobi, Kenya
Received: Jan. 8, 2020;       Accepted: Jan. 31, 2020;       Published: Feb. 14, 2020
DOI: 10.11648/j.ijdsa.20200601.13      View  210      Downloads  137
Abstract
Crimes have been the most dangerous threat to peace, development, human right, social, political and economic stability in Kenya. There is a great need to eradicate crime to facilitate development and counter all vices that are caused by crime. Efficient management of crime requires an adequate understanding of the patterns in which crime occur to put the appropriate measures in place for crime prevention. Crime has been in existence since the beginning of time hence will remain, and one of the solutions is to identify the pattern in which it occurs to prevent or counter it effectively as it occurs. The main objective of the study was to find out how different crimes are related. The study considered a number of data mining techniques which included; clustering, specifically k-means algorithm, mapping and APRIORI algorithm to analyze how different crimes are related and how often they occur. Crime cases were found to be decreasing over the years under study and counties with a high population reported higher number of crimes as compared to those with low population. The study suggested that these crimes could be controlled by directing more resources in the highly populated counties. The study leaves a research gap where the same crime data could be analyzed using time series methods since observed crime offenses are recorded alongside the time they occur.
Keywords
Crime, Clustering, Data Mining Techniques, Specifically K-means Algorithm, Mapping and APRIORI Algorithm, Shiny App
To cite this article
Stephen Mangara Wainana, Joseph Njuguna Karomo, Rachael Kyalo, Noah Mutai, Using Data Mining Techniques and R Software to Analyze Crime Data in Kenya, International Journal of Data Science and Analysis. Vol. 6, No. 1, 2020, pp. 20-31. doi: 10.11648/j.ijdsa.20200601.13
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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