  Assessing water quality and recognizing its associated risks to human health
and the broader environment is undoubtedly essential. Groundwater is widely
used to supply water for drinking, industry, and agriculture purposes. The
groundwater quality measurements vary for different climates and various human
behaviors, and consequently, their spatial variability can be substantial. In
this paper, we aim to analyze a groundwater dataset from the Golestan province,
Iran, for November 2003 to November 2013. Our target response variable to
monitor the quality of groundwater is the number of counts that the quality of
water is good for a drink. Hence, we are facing spatial count data. Due to the
ubiquity of over or underdispersion in count data, we propose a Bayesian
hierarchical modeling approach based on the renewal theory that relates
nonexponential waiting times between events and the distribution of the counts,
relaxing the assumption of equidispersion at the cost of an additional
parameter. Particularly, we extend the methodology for the analysis of spatial
count data based on the gamma distribution assumption for waiting times. The
model can be formulated as a latent Gaussian model, and therefore, we can carry
out the fast computation by using the integrated nested Laplace approximation
method. The analysis of the groundwater dataset and a simulation study show a
significant improvement over both Poisson and negative binomial models.
