An ecological adjusted random effect model for property crime in Windhoek, Namibia (2011-2016)

Abstract

Count data that are zero inflated are often analysed using Zero-Inflated Negative Binomial Generalized Linear Mixed Model (ZINB-GLMM) when observations are correlated in ways that require random effects. This study investigated ecological factors influencing the number of property crimes in Windhoek by using data obtained from the Windhoek police over the period of six consecutive years (2011 to 2016). The ecological concepts were measured at different levels of aggregation. Limited studies in Windhoek have considered analysing crime data on Generalized Linear Mixed Model via Template Model Builder (TMB) R-package. Crimes were counted with respect to Month, Season, Year, Location and Density. Property crime data contained more zeros than expected. When comparing models fitted, it was found that the Relative Risks (RR) were highly significant for models fitted via Negative Binomial distribution. By adopting a ZINB-GLMM, the study attempted to address the potential covariates for Property crimes. The study showed that most of the variation property crimes was due to locations. Crime was high during spring and winter time during the study period. The study further discovered that areas with high population densities had high crime intensity. Security patrols and surveillance should be stepped up in Windhoek in high density suburbs especially during winter and spring seasons.