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Georeferenced Gridded Data Handled by GMT: Cartographic Solutions for Geophysical Mapping. / Леменкова, Полина Алексеевна.

In: Georeview: Scientific Annals of Stefan cel Mare University of Suceava. Geography Series, Vol. 31, No. 1, 01.11.2021, p. 78-93.

Research output: Contribution to journalArticlepeer-review

Harvard

Леменкова, ПА 2021, 'Georeferenced Gridded Data Handled by GMT: Cartographic Solutions for Geophysical Mapping', Georeview: Scientific Annals of Stefan cel Mare University of Suceava. Geography Series, vol. 31, no. 1, pp. 78-93. https://doi.org/10.5281/zenodo.5637076

APA

Леменкова, П. А. (2021). Georeferenced Gridded Data Handled by GMT: Cartographic Solutions for Geophysical Mapping. Georeview: Scientific Annals of Stefan cel Mare University of Suceava. Geography Series, 31(1), 78-93. https://doi.org/10.5281/zenodo.5637076

Vancouver

Леменкова ПА. Georeferenced Gridded Data Handled by GMT: Cartographic Solutions for Geophysical Mapping. Georeview: Scientific Annals of Stefan cel Mare University of Suceava. Geography Series. 2021 Nov 1;31(1):78-93. https://doi.org/10.5281/zenodo.5637076

Author

Леменкова, Полина Алексеевна. / Georeferenced Gridded Data Handled by GMT: Cartographic Solutions for Geophysical Mapping. In: Georeview: Scientific Annals of Stefan cel Mare University of Suceava. Geography Series. 2021 ; Vol. 31, No. 1. pp. 78-93.

BibTeX

@article{e82b0b2d67b64e87bf6ba2c88f4a717a,
title = "Georeferenced Gridded Data Handled by GMT: Cartographic Solutions for Geophysical Mapping",
abstract = "The functionality of Generic Mapping Tools (GMT) to process and visualize geospatial information is crucial to the development of the advanced cartographic method. This paper presents modelling and spatial analysis of the marine geological data using GMT shell scripting. GMT demonstrated effective cartographic solutions for visualization of the georeferenced data. The particular feature of GMT consists in its scripting modular approach that enables to use machine learning to explore reliable georeferenced data. Here, the study applies a sequential shell scripting to devise GMT modules for depicting marine geological data on the Mariana Trench. The data cover bathymetry, geophysics, tectonics and geology. The first method makes use of the 'nearneighbor' GMT module for grid contour modelling using Nearest Neighbor algorithm. This form of modelling classifies the geospatial data based on a similarity. The second method presents surface modelling from the initial XYZ-ASCII dataset by a combination of the 'blockmean' and 'surface' modules. The third method includes the use of the modules 'grdimage', 'psbasemap' and 'grdcontour' for plotting. Compared to GIS methods in which data are processed in a menu, GMT presents the console-based approach which automates cartographic data processing. The results present seven new maps and explanations of scripts.A combination of visual approaches applied using a color fill and various textures to represent data, which is effective in allowing readers to assess geophysical setting. The study demonstrated the effectiveness of GMT in geodata visualization. ",
keywords = "cartography, machine learning, GMT, scripting, data analysis, data visualization",
author = "Леменкова, {Полина Алексеевна}",
year = "2021",
month = nov,
day = "1",
doi = "10.5281/zenodo.5637076",
language = "English",
volume = "31",
pages = "78--93",
journal = "Georeview: Scientific Annals of Stefan cel Mare University of Suceava. Geography Series",
issn = "2343-7405",
number = "1",

}

RIS

TY - JOUR

T1 - Georeferenced Gridded Data Handled by GMT: Cartographic Solutions for Geophysical Mapping

AU - Леменкова, Полина Алексеевна

PY - 2021/11/1

Y1 - 2021/11/1

N2 - The functionality of Generic Mapping Tools (GMT) to process and visualize geospatial information is crucial to the development of the advanced cartographic method. This paper presents modelling and spatial analysis of the marine geological data using GMT shell scripting. GMT demonstrated effective cartographic solutions for visualization of the georeferenced data. The particular feature of GMT consists in its scripting modular approach that enables to use machine learning to explore reliable georeferenced data. Here, the study applies a sequential shell scripting to devise GMT modules for depicting marine geological data on the Mariana Trench. The data cover bathymetry, geophysics, tectonics and geology. The first method makes use of the 'nearneighbor' GMT module for grid contour modelling using Nearest Neighbor algorithm. This form of modelling classifies the geospatial data based on a similarity. The second method presents surface modelling from the initial XYZ-ASCII dataset by a combination of the 'blockmean' and 'surface' modules. The third method includes the use of the modules 'grdimage', 'psbasemap' and 'grdcontour' for plotting. Compared to GIS methods in which data are processed in a menu, GMT presents the console-based approach which automates cartographic data processing. The results present seven new maps and explanations of scripts.A combination of visual approaches applied using a color fill and various textures to represent data, which is effective in allowing readers to assess geophysical setting. The study demonstrated the effectiveness of GMT in geodata visualization.

AB - The functionality of Generic Mapping Tools (GMT) to process and visualize geospatial information is crucial to the development of the advanced cartographic method. This paper presents modelling and spatial analysis of the marine geological data using GMT shell scripting. GMT demonstrated effective cartographic solutions for visualization of the georeferenced data. The particular feature of GMT consists in its scripting modular approach that enables to use machine learning to explore reliable georeferenced data. Here, the study applies a sequential shell scripting to devise GMT modules for depicting marine geological data on the Mariana Trench. The data cover bathymetry, geophysics, tectonics and geology. The first method makes use of the 'nearneighbor' GMT module for grid contour modelling using Nearest Neighbor algorithm. This form of modelling classifies the geospatial data based on a similarity. The second method presents surface modelling from the initial XYZ-ASCII dataset by a combination of the 'blockmean' and 'surface' modules. The third method includes the use of the modules 'grdimage', 'psbasemap' and 'grdcontour' for plotting. Compared to GIS methods in which data are processed in a menu, GMT presents the console-based approach which automates cartographic data processing. The results present seven new maps and explanations of scripts.A combination of visual approaches applied using a color fill and various textures to represent data, which is effective in allowing readers to assess geophysical setting. The study demonstrated the effectiveness of GMT in geodata visualization.

KW - cartography

KW - machine learning

KW - GMT

KW - scripting

KW - data analysis

KW - data visualization

UR - http://georeview.ro/ojs/index.php/revista/article/view/439

U2 - 10.5281/zenodo.5637076

DO - 10.5281/zenodo.5637076

M3 - Article

VL - 31

SP - 78

EP - 93

JO - Georeview: Scientific Annals of Stefan cel Mare University of Suceava. Geography Series

JF - Georeview: Scientific Annals of Stefan cel Mare University of Suceava. Geography Series

SN - 2343-7405

IS - 1

ER -

ID: 134167347