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Using Data Mining Techniques and R Software to Analyze Crime Data in Kenya

Received: 8 January 2020     Accepted: 31 January 2020     Published: 14 February 2020
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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.

Published in International Journal of Data Science and Analysis (Volume 6, Issue 1)
DOI 10.11648/j.ijdsa.20200601.13
Page(s) 20-31
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Crime, Clustering, Data Mining Techniques, Specifically K-means Algorithm, Mapping and APRIORI Algorithm, Shiny App

References
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[3] M. Kaufmann, J. Han and J. Pei, Data mining: concepts and techniques, Morgan Kaufmann, 2000.
[4] P.-N. Tan, M. Steinbach and V. Kumar, Introduction to data mining, Pearson Education India, 2016.
[5] M. Brown, "Data mining techniques.," Developer Works, IBM Corporation, pp. 1-16, 11 December 2012.
[6] C. C. Yang and o. D. Ng, "Terrorism and crime related weblog social network: Link, content analysis and information visualization," in 2007 IEEE Intelligence and Security Informatics, 2007.
[7] H. Chen, W. Chung,. J.. J. Xu, G. Wang,. Y. Qin and M. Chau, "Crime data mining: a general framework and some examples," computer, vol. 4, pp. 50--56, 2004.
[8] D. E. Brown, "The regional crime analysis program (RECAP): a framework for mining data to catch criminals," in SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 98CH36218), 1998.
[9] S. V. Nath, "Crime pattern detection using data mining," in 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, 2006.
[10] S. Lin and. D. E. Brown, "An outlier-based data association method for linking criminal incidents," Decision Support Systems, vol. 41, no. 3, pp. 604--615, 2006.
[11] V. Estivill-Castro and I. Lee, "Data mining techniques for autonomous exploration of large volumes of geo-referenced crime data," in Proc. of the 6th International Conference on Geocomputation, 2001.
[12] P. L. Brantingham and P. J. Brantingham, "Environment, routine and situation: Toward a pattern theory of crime," Advances in criminological theory, vol. 5, no. 2, pp. 259--94, 1993.
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Cite This Article
  • APA Style

    Stephen Mangara Wainana, Joseph Njuguna Karomo, Rachael Kyalo, Noah Mutai. (2020). Using Data Mining Techniques and R Software to Analyze Crime Data in Kenya. International Journal of Data Science and Analysis, 6(1), 20-31. https://doi.org/10.11648/j.ijdsa.20200601.13

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    ACS Style

    Stephen Mangara Wainana; Joseph Njuguna Karomo; Rachael Kyalo; Noah Mutai. Using Data Mining Techniques and R Software to Analyze Crime Data in Kenya. Int. J. Data Sci. Anal. 2020, 6(1), 20-31. doi: 10.11648/j.ijdsa.20200601.13

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    AMA Style

    Stephen Mangara Wainana, Joseph Njuguna Karomo, Rachael Kyalo, Noah Mutai. Using Data Mining Techniques and R Software to Analyze Crime Data in Kenya. Int J Data Sci Anal. 2020;6(1):20-31. doi: 10.11648/j.ijdsa.20200601.13

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  • @article{10.11648/j.ijdsa.20200601.13,
      author = {Stephen Mangara Wainana and Joseph Njuguna Karomo and Rachael Kyalo and Noah Mutai},
      title = {Using Data Mining Techniques and R Software to Analyze Crime Data in Kenya},
      journal = {International Journal of Data Science and Analysis},
      volume = {6},
      number = {1},
      pages = {20-31},
      doi = {10.11648/j.ijdsa.20200601.13},
      url = {https://doi.org/10.11648/j.ijdsa.20200601.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20200601.13},
      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.},
     year = {2020}
    }
    

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  • TY  - JOUR
    T1  - Using Data Mining Techniques and R Software to Analyze Crime Data in Kenya
    AU  - Stephen Mangara Wainana
    AU  - Joseph Njuguna Karomo
    AU  - Rachael Kyalo
    AU  - Noah Mutai
    Y1  - 2020/02/14
    PY  - 2020
    N1  - https://doi.org/10.11648/j.ijdsa.20200601.13
    DO  - 10.11648/j.ijdsa.20200601.13
    T2  - International Journal of Data Science and Analysis
    JF  - International Journal of Data Science and Analysis
    JO  - International Journal of Data Science and Analysis
    SP  - 20
    EP  - 31
    PB  - Science Publishing Group
    SN  - 2575-1891
    UR  - https://doi.org/10.11648/j.ijdsa.20200601.13
    AB  - 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.
    VL  - 6
    IS  - 1
    ER  - 

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Author Information
  • Department of Mathematics and Informatics, Taita Taveta University, Nairobi, Kenya

  • Department of Pure and Applied Sciences, Kirinyaga University, Nairobi, Kenya

  • Department of Mathematics and Informatics, Taita Taveta University, Nairobi, Kenya

  • Department of Mathematics and Informatics, Taita Taveta University, Nairobi, Kenya

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