Detection of inconsistencies in geospatial data with geostatistics

dc.contributor.author Trancoso Santos, Adriana
dc.contributor.author dos Santos, Gerson
dc.contributor.author Kaleita, Amy
dc.contributor.author Emiliano, Paulo
dc.contributor.author das Graças Medeiros, Nilcilene
dc.contributor.author Kaleita, Amy
dc.contributor.author de Oliveira Serrano Pruski, Lígia
dc.contributor.department Agricultural and Biosystems Engineering
dc.date 2018-02-18T21:46:02.000
dc.date.accessioned 2020-06-29T22:42:57Z
dc.date.available 2020-06-29T22:42:57Z
dc.date.copyright Sun Jan 01 00:00:00 UTC 2017
dc.date.issued 2017-04-01
dc.description.abstract <p>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, <em>BoxPlot</em>, the importance and functionality of the new method was verified, since the <em>BoxPlot</em> 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.</p>
dc.description.comments <p>This article is from Bol. Ciênc. Geod. vol.23 no.2 Curitiba Apr./June 2017, <a target="_blank">http://dx.doi.org/10.1590/s1982-21702017000200019</a>.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/abe_eng_pubs/825/
dc.identifier.articleid 2109
dc.identifier.contextkey 10643049
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_pubs/825
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/1630
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/abe_eng_pubs/825/2017_Kaleita_DetectionInconsistencies.pdf|||Sat Jan 15 02:08:43 UTC 2022
dc.source.uri 10.1590/s1982-21702017000200019
dc.subject.disciplines Agriculture
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.keywords Outliers
dc.subject.keywords Geoprocessing
dc.subject.keywords LiDAR technology
dc.title Detection of inconsistencies in geospatial data with geostatistics
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 8a405b08-e1c8-4a10-b458-2f5a82fcf148
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
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