  Measuring veracity or reliability of noisy data is of utmost importance,
especially in the scenarios where the information are gathered through
automated systems. In a recent paper, Chakraborty et. al. (2019) have
introduced a veracity scoring technique for geostatistical data. The authors
have used a high-quality `reference' data to measure the veracity of the
varying-quality observations and incorporated the veracity scores in their
analysis of mobile-sensor generated noisy weather data to generate efficient
predictions of the ambient temperature process. In this paper, we consider the
scenario when no reference data is available and hence, the veracity scores
(referred as VS) are defined based on `local' summaries of the observations. We
develop a VS-based estimation method for parameters of a spatial regression
model. Under a non-stationary noise structure and fairly general assumptions on
the underlying spatial process, we show that the VS-based estimators of the
regression parameters are consistent. Moreover, we establish the advantage of
the VS-based estimators as compared to the ordinary least squares (OLS)
estimator by analyzing their asymptotic mean squared errors. We illustrate the
merits of the VS-based technique through simulations and apply the methodology
to a real data set on mass percentages of ash in coal seams in Pennsylvania.
