Abstract:
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.
