  Uncertainty is an inherent characteristic of biological and geospatial data
which is almost made by measurement error in the observed values of the
quantity of interest. Ignoring measurement error can lead to biased estimates
and inflated variances and so an inappropriate inference. In this paper, the
Gaussian spatial model is fitted based on covariate measurement error. For this
purpose, we adopt the Bayesian approach and utilize the Markov chain Monte
Carlo algorithms and data augmentations to carry out calculations. The
methodology is illustrated using simulated data.
