Describing the dimensionality of geospatial data in the earth sciences—Recommendations for nomenclature
Author(s) -
Richard R. Jones,
Tim F. Wawrzyniec,
Nicolas S. Holliman,
Ken McCaffrey,
Jonathan B. Imber,
R. E. Holdsworth
Publication year - 2008
Publication title -
geosphere
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.879
H-Index - 58
ISSN - 1553-040X
DOI - 10.1130/ges00158.1
Subject(s) - curse of dimensionality , geospatial analysis , context (archaeology) , data set , sampling (signal processing) , computer science , set (abstract data type) , ambiguity , data mining , data science , sample (material) , outlier , geology , artificial intelligence , remote sensing , paleontology , computer vision , filter (signal processing) , chemistry , chromatography , programming language
Complications exist when describing the dimensionality of geoscientific data sets. One difficulty is that there are a number of different, valid ways to consider dimensionality. Unlike traditional methods of field data capture, modern digital methods typically record the position of every sample point relative to a three-dimensional (3D) coordinate system, even for simple measurement strategies such as 1D line sampling. Critically, the best way to describe the dimensionality of a data set will depend on the context in which the data are presented. Terms such as “2½D” are generally inappropriate for nonspecialist audiences. Because ambiguity and inconsistency are already widespread, it is usually advisable to explain clearly the nature of each data set, the method used to capture the data, and particularly whether data acquisition was restricted to the outcrop surface or includes sampling of the subsurface.
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