Detection of inconsistencies in geospatial data with geostatistics
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Since 1905, the Department of Agricultural Engineering, now the Department of Agricultural and Biosystems Engineering (ABE), has been a leader in providing engineering solutions to agricultural problems in the United States and the world. The department’s original mission was to mechanize agriculture. That mission has evolved to encompass a global view of the entire food production system–the wise management of natural resources in the production, processing, storage, handling, and use of food fiber and other biological products.
History
In 1905 Agricultural Engineering was recognized as a subdivision of the Department of Agronomy, and in 1907 it was recognized as a unique department. It was renamed the Department of Agricultural and Biosystems Engineering in 1990. The department merged with the Department of Industrial Education and Technology in 2004.
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1905–present
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- Department of Agricultural Engineering (1907–1990)
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- College of Agriculture and Life Sciences (parent college)
- College of Engineering (parent college)
- Department of Industrial Education and Technology, (merged, 2004)
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Abstract
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.
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This article is from Bol. Ciênc. Geod. vol.23 no.2 Curitiba Apr./June 2017, http://dx.doi.org/10.1590/s1982-21702017000200019.