Application of Principal Component Analysis to Crime Data, Case Study: Mathare Slums, Nairobi County in Kenya
American Journal of Theoretical and Applied Statistics
Volume 8, Issue 1, January 2019, Pages: 7-17
Received: Jan. 8, 2019; Accepted: Jan. 28, 2019; Published: Feb. 21, 2019
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Wafula Mike Erick, Department of Mathematics and Actuarial Science, Kenyatta University (KU), Nairobi, Kenya
Samson Wangila Wanyonyi, Department of Mathematics, University of Eldoret, Eldoret, Kenya
Chris Muchwanju, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
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The study focuses on statistical analysis of causes of crimes in Mathare slums, Nairobi county using data collected via questionnaires in April 2018.The Correlation analysis was carried out to explain the association between the causes of crimes while the principal component analysis (PCA) was used to reduce the dimensionality of the data sets. The Correlation analysis indicates a fairly strong positive relationship between unemployment and drugs and substance abuse which means that their variables can be used to predict one another. PCA analysis reveals that three PCs (drugs and substance abuse, unemployment and neglect from parents) that explains about 52.6% of the total variability of the causes of crimes against person are suggested to be retained. Similarly, two PCs (drugs and substance abuse and unemployment) that explain about 42.2% of the total variability of the causes of crimes against property are suggested to be retained. Generally, the causes of crimes against person and property in Mathare slums are not unique.
Crime, Principal Component Analysis, Total Variation, Scree Plot
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Wafula Mike Erick, Samson Wangila Wanyonyi, Chris Muchwanju, Application of Principal Component Analysis to Crime Data, Case Study: Mathare Slums, Nairobi County in Kenya, American Journal of Theoretical and Applied Statistics. Vol. 8, No. 1, 2019, pp. 7-17. doi: 10.11648/j.ajtas.20190801.12
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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