A SMART approach to quality assessment of site-based spatio-temporal data

A significant challenge we face in assessing spatio-temporal data quality is a lack of ground-truth data. Error is by definition the deviation of observation from ground truth. In the absence of ground truth, we depend on our own or provider quality assessment to evaluate our methods. The focus of this paper is the development of a representative, weather-like spatio- temporal dataset and the use of this dataset to develop and evaluate a robust, interpolation-based method for assessment of data quality. We call our method the SMART method, short for Simple Mappings for the Approximation and Regression of Time series. We present this method as a representative approach to demonstrate and overcome the challenges of spatio- temporal data quality assessment. Our results bring into question the validity of provider-based quality control indicators.