Detection of inconsistencies in geospatial data with geostatistics
Rodrigues dos Santos, Gerson
Medeiros, Nilcilene das Graças
Pruski, Lígia de Oliveira Serrano
Almost every researcher has come through observations that “drift” from the rest of the sample, suggesting some inconsistency. The aim of this paper is to propose a new inconsistent data detection method for continuous geospatial data based in Geostatistics, independently from the generative cause (measuring and execution errors and inherent variability data). The choice of Geostatistics is based in its ideal characteristics, as avoiding systematic errors, for example. The importance of a new inconsistent detection method proposal is in the fact that some existing methods used in geospatial data consider theoretical assumptions hardly attended. Equally, the choice of the data set is related to the importance of the LiDAR technology (Light Detection and Ranging) in the production of Digital Elevation Models (DEM). Thus, with the new methodology it was possible to detect and map discrepant data. Comparing it to a much utilized detections method, BoxPlot, the importance and functionality of the new method was verified, since the BoxPlot did not detect any data classified as discrepant. The proposed method pointed that, in average, 1,2% of the data of possible regionalized inferior outliers and, in average, 1,4% of possible regionalized superior outliers, in relation to the set of data used in the study.
This article is published as Santos, Adriana Maria Rocha Trancoso, Gerson Rodrigues dos Santos, Paulo César Emiliano, Nilcilene das Graças Medeiros, Amy L. Kaleita, and Lígia de Oliveira Serrano Pruski. "Detection of inconsistencies in geospatial data with geostatistics." Boletim de Ciências Geodésicas 23, no. 2 (2017): 296-308. DOI: 10.1590/s1982-21702017000200019. Posted with permission.