This paper focuses on the analysis of murder in Namibia using Bayesian spatial smoothing
approach with temporal trends. The analysis was based on the reported cases from 13 regions of
Namibia for the period 2002–2006 complemented with regional population sizes. The evaluated
random effects include space-time structured heterogeneity measuring the effect of regional
clustering, unstructured heterogeneity, time, space and time interaction and population density.
The model consists of carefully chosen prior and hyper-prior distributions for parameters and
hyper-parameters, with inference conducted using Gibbs sampling algorithm and sensitivity test
for model validation. The posterior mean estimate of the parameters from the model using DIC
as model selection criteria show that most of the variation in the relative risk of murder is due
to regional clustering, while the effect of population density and time was insignificant. The
sensitivity analysis indicates that both intrinsic and Laplace CAR prior can be adopted as prior
distribution for the space-time heterogeneity. In addition, the relative risk map show risk structure
of increasing north-south gradient, pointing to low risk in northern regions of Namibia, while
Karas and Khomas region experience long-term increase in murder risk.