Version 1
: Received: 27 November 2018 / Approved: 28 November 2018 / Online: 28 November 2018 (10:28:16 CET)
How to cite:
Lemenkova, P. Statistical Analysis of the Mariana Trench Geomorphology Using R Programming Language. Preprints2018, 2018110610. https://doi.org/10.20944/preprints201811.0610.v1
Lemenkova, P. Statistical Analysis of the Mariana Trench Geomorphology Using R Programming Language. Preprints 2018, 2018110610. https://doi.org/10.20944/preprints201811.0610.v1
Lemenkova, P. Statistical Analysis of the Mariana Trench Geomorphology Using R Programming Language. Preprints2018, 2018110610. https://doi.org/10.20944/preprints201811.0610.v1
APA Style
Lemenkova, P. (2018). Statistical Analysis of the Mariana Trench Geomorphology Using R Programming Language. Preprints. https://doi.org/10.20944/preprints201811.0610.v1
Chicago/Turabian Style
Lemenkova, P. 2018 "Statistical Analysis of the Mariana Trench Geomorphology Using R Programming Language" Preprints. https://doi.org/10.20944/preprints201811.0610.v1
Abstract
This paper introduces an application of R programming language for geostatistical data processing with a case study of the Mariana Trench, Pacific Ocean. The formation of the Mariana Trench, the deepest among all hadal oceanic depth trenches, is caused by complex and diverse geomorphic factors affecting its development. Mariana Trench crosses four tectonic plates: Mariana, Caroline, Pacific and Philippine. The impact of the geographic location and geological factors on its geomorphology has been studied by methods of statistical analysis and data visualization using R libraries. The methodology includes following steps. Firstly, vector thematic data were processed in QGIS: tectonics, bathymetry, geomorphology and geology. Secondly, 25 cross-section profiles were drawn across the trench. The length of each profile is 1000-km. The attribute information has been derived from each profile and stored in a table containing coordinates, depths and thematic information. Finally, this table was processed by methods of the statistical analysis on R. The programming codes and graphical results are presented. The results include geospatial comparative analysis and estimated effects of the data distribution by tectonic plates: slope angle, igneous volcanic areas and depths. The innovativeness of this paper consists in a cross-disciplinary approach combining GIS, statistical analysis and R programming.
Environmental and Earth Sciences, Geophysics and Geology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
