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Article Dans Une Revue Land Année : 2023

Quantitative Morphometric 3D Terrain Analysis of Japan Using Scripts of GMT and R

Polina Lemenkova
Olivier Debeir

Résumé

In this paper, we describe two related scripting methods of cartographic data processing and visualization that provide 2D and 3D mapping of Japan with different algorithm complexity. The first algorithm utilizes Generic Mapping Toolset (GMT), which is known as an advanced console-based program for spatial data processing. The modules of GMT combine the functionality of scripting with the aspects of geoinformatics, which is especially effective for the rapid analysis of large geospatial datasets, multi-format data processing, and mapping in 2D and 3D modes. The second algorithm presents the use of the R programming language for cartographic visualization and spatial analysis. This R method utilizes the packages ‘tmap’, ‘raster’, ‘maps’, and ‘mapdata’ to model the morphometric elements of the Japanese archipelago, such as slope, aspect, hillshade and elevation. The general purpose graphical package ‘ggplot2’ of R was used for mapping the prefectures of Japan. The two scripting approaches demonstrated an established correspondence between the programming languages and cartography determined with the use of scripts for data processing. They outperform several well-known and state-of-the-art GIS methods for mapping due to their high automation of data processing. Cartography has largely reflected recent advances in data science, the rapid development of scripting languages, and transfer in the approaches of data processing. This extends to the shift from the traditional GIS to programming languages. As a response to these new challenges, we demonstrated in this paper the advantages of using scripts in mapping, which consist of repeatability and the flexible applicability of scripts in similar works.
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hal-03940975 , version 1 (16-01-2023)

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Polina Lemenkova, Olivier Debeir. Quantitative Morphometric 3D Terrain Analysis of Japan Using Scripts of GMT and R. Land, 2023, Feature Papers for Land Innovations – Data and Machine Learning, 12 (1), pp.261. ⟨10.3390/land12010261⟩. ⟨hal-03940975⟩
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