The SMART approach to comprehensive quality assessment of site-based spatial-temporal data

There is a need for comprehensive solutions to address the challenges of spatio-temporal data quality assessment. Emphasis is often placed on the quality assessment of individual observations from sensors but not on the sensors themselves nor upon site metadata such as location and timestamps. The focus of this paper is on the development and evaluation of a representative and comprehensive, interpolation-based methodology for assessment of spatio-temporal data quality. We call our method the SMART method, short for Simple Mappings for the Approximation and Regression of Time series. When applied to a real-world, meteorological data set, we show that our method outperforms standard interpolators and we identify numerous problematic sites that otherwise would not have been flagged as bad. We further identify sites for which metadata is incorrect. We believe that there are many problems with real data sets like these and, in the absence of an approach like ours, these problems have largely gone unidentified. Our results bring into question the validity of provider-based quality control indicators. In addition to providing a comprehensive solution, our approach is novel for the simple but effective way that it accounts for spatial and temporal variation.