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

Stephen Mangara Wainana; Joseph Njuguna Karomo; Rachael Kyalo; Noah Mutai

doi:doi:10.11648/j.ijdsa.20200601.13

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

Stephen Mangara Wainana, Joseph Njuguna Karomo, Rachael Kyalo, Noah Mutai

Published in International Journal of Data Science and Analysis (Volume 6, Issue 1)

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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[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.
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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

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

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Plain Text BibTeX RIS

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

Copy | Download

@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  -

Copy | Download