Modeling Burglar Incidents Data Using Generalized and Quasi Poisson Regression Models: A Case Study of Nairobi City County, Kenya
American Journal of Theoretical and Applied Statistics
Volume 9, Issue 5, September 2020, Pages: 256-262
Received: Oct. 5, 2020;
Accepted: Oct. 19, 2020;
Published: Oct. 26, 2020
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Isaac Muchika, Department of Statistics & Actuarial Science, Jomo Kenyatta University of Agriculture & Technology, Nairobi, Kenya
Antony Ngunyi, Department of Statistics & Actuarial Science, Dedan Kimathi University of Technology, Nyeri, Kenya
Thomas Mageto, Department of Statistics & Actuarial Science, Jomo Kenyatta University of Agriculture & Technology, Nairobi, Kenya
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Serious violent crimes including Burglary, dangerous drug trafficking and sexual offenses make up the bulk of incidents filed at police stations daily. These crimes related activities poses a serious threat to the peace and serenity of a nation as far as safety is concerned. Burglar incidents data are often discrete and do not conform to the general assumptions of the linear model and its variants. Ordinarily, such data could be modeled using a linear regression approach to derive the relationship between the response variable to the underlying covariates. However, the narrowing of the gap between city and suburban burglar crime rates brings about variability invalidating the application of Ordinary linear regression approaches. The main objective of this study focused on the comparative use of Generalized Poisson and Quasi-Poisson models as an alternative to the classical linear regression approach in modeling Burglar incidents in Nairobi City County, Kenya. The prime advantage of applying Quasi Poisson in count data analysis is that it fixes the basic fallacy of assuming homogeneity in data and allows estimation of dispersion. The study used secondary data covering Eight (8) Nairobi's Administrative Divisions from the National Crime Research Center (NCRC) for the period 2016-2018. The comparison criteria were the Akaike Information (AIC) Criterion and Deviance Information Criterion (DIC) alongside other model diagnostics tests. Application of this results in burglar events revealed that the number of incidents in the study area are Under-dispersed with the risks of experiencing Burglar crime being above 5% in all the locations surveyed. In an attempt to explore Burglar to location relationship, results from study proved that Generalized Poisson Model performed better than the Quasi Poisson model having posted the lowest AIC value.
Burglar Crime, Generalized Poisson, Quasi Poisson, Dispersion, Count Data
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Modeling Burglar Incidents Data Using Generalized and Quasi Poisson Regression Models: A Case Study of Nairobi City County, Kenya, American Journal of Theoretical and Applied Statistics.
Vol. 9, No. 5,
2020, pp. 256-262.
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/
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