Topographic surface modelling using raster grid datasets by GMT: example of the Kuril–Kamchatka Trench, Pacific Ocean

Lemenkova, Polina

doi:10.2478/rgg-2019-0008

Cited by 27 publications

(24 citation statements)

References 51 publications

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“…Besides programming tools, analysis of the geological datasets can be performed by shell scripts as plugins embedded in GIS, or as scripting method of the geodata analysis and visualization using Generic Mapping Tools (GMT). Despite being a specifically cartographic toolset primarily designed for mapping, GMT also includes available modules for the statistical data analysis: histograms, plotting median and mean in a datasets [48][49][50][51][52][53][54][55].…”

Section: Discussionmentioning

confidence: 99%

R Libraries {dendextend} and {magrittr} and Clustering Package scipy.cluster of Python For Modelling Diagrams of Dendrogram Trees

Lemenkova

2020

Carpathian Journal of Electronic and Computer Engineering

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The paper presents a comparison of the two languages Python and R related to the classification tools and demonstrates the differences in their syntax and graphical output. It indicates the functionality of R and Python packages {dendextend} and scipy.cluster as effective tools for the dendrogram modelling by the algorithms of sorting and ranking datasets. R and Python programming languages have been tested on a sample dataset including marine geological measurements. The work aims to detect how bathymetric data change along the 25 bathymetric profiles digitized across the Mariana Trench. The methodology includes performed hierarchical cluster analysis with dendrograms and plotted clustermap with marginal dendrograms. The statistical libraries include Matplotlib, SciPy, NumPy, Pandas by Python and {dendextend}, {pvclust}, {magrittr} by R. The dendrograms were compared by the model-simulated clusters of the bathymetric ranges. The results show three distinct groups of the profiles sorted by the elevation ranges with maximal depths detected in a group of profiles 19-21. The dendrogram visualization in a cluster analysis demonstrates the effective representation of the data sorting, grouping and classifying by the machine learning algorithms. The programming codes presented in this study enable to sort a dataset in a similar research aimed to group data based on the similarity of attributes. Effective visualization by dendrograms is a useful modelling tool for the geospatial management where data ranking is required. Plotting dendrograms by R, comparing to Python, presented functional and sophisticated algorithms, refined design control and fine graphical data output. The interdisciplinary nature of this work consists in application of the coding algorithms for spatial data analysis.

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Section: Discussionmentioning

confidence: 99%

R Libraries {dendextend} and {magrittr} and Clustering Package scipy.cluster of Python For Modelling Diagrams of Dendrogram Trees

Lemenkova

2020

Carpathian Journal of Electronic and Computer Engineering

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“…Automatization in topographic modelling is largely presented by the use of scripting languages (Lemenkova, 2019b(Lemenkova, , 2019c(Lemenkova, , 2019d or specially designed software that uses approaches of machine learning in geoinformatics (Gauger et al 2007;Iwahashi & Pike, 2007;Schenke & Lemenkova, 2008;Alvioli et al 2016;Kuhn et al 2007). Both practical and theoretical conceptual approaches are the core issues of the GIS-based geomorphic mapping.…”

Section: Discussionmentioning

confidence: 99%

Using R packages 'tmap', 'raster' and 'ggmap' for cartographic visualization: An example of dem-based terrain modelling of Italy, Apennine Peninsula

Lemenkova¹

2020

Zb PMF Geog inst Beograd

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The main purpose of this article is to present the use of R programming language in cartographic visualization demonstrating using machine learning methods in geographic education. Current trends in education technologies are largely influenced by the possibilities of distance-learning, e-learning and selflearning. In view of this, the main tendencies in modern geographic education include active use of open source GIS and publicly available free geospatial datasets that can be used by students for cartographic exercises, data visualization and mapping, both at intermediate and advanced levels. This paper contributes to the development of these methods and is fully based on the datasets and tools available for every student: the R programming language and the free open source datasets. The case study demonstrated in this paper show the examples of both physical geographic mapping (geomorphology) and socio-economic geography (regional mapping) which can be used in the classes and in self-learning. The objective of this research includes geomorphological modelling of the terrain relief in Italy and regional mapping. The data include dem SRTM90 and datasets on regional borders of Italy embedded in R packages 'maps' and 'mapdata'. Modelling references to the characteristics of slope, aspect, hillshade and elevation, their visualization using R packages: 'raster' and 'tmap'. Regional mapping of Italy was made using main package 'ggmap' with the 'ggplot2' as a wrapper. The results present five thematic maps (slope, aspect, hillshade, elevation and regions of Italy) created in R language. Traditionally used in statistical analysis, R is less known as a perfect tool in geographic education. This paper contributes to the development of methods in geographic education by presenting new technologies of the machine learning methods of mapping.

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“…In the southeast of Java, it is presented by a series of local depressions separated by the uphills. Comparing to other oceanic trenches, such as Tonga, Kermadec, Vityaz, Vanuatu, Kuril-Kamchatka (Lemenkova, 2019b(Lemenkova, , 2019c(Lemenkova, , 2020d, the slopes of the Sunda Trench are generally steeper and asymmetric. The asymmetry of the slopes is reflected in differential geometry of its oceanic and landward sides as follows: comparing to the oceanic slope, the landward slope is higher and steeper, more dissected by the canyons and complicated by the steps and ledges.…”

Section: Figure 4 Geoid Model Of the West Indonesia Basin World Geoimentioning

confidence: 91%

Analysis of the difference in depths and variation in slope steepness of the Sunda Trench, Indonesia, east Indian Ocean

Lemenkova

2020

Rev. Geomorf.

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The paper discusses geomorphology of the Sunda Trench, an oceanic trench located in eastern Indian Ocean along the Sumatra and Java Islands of the Indonesian archipelago. In particular, it analysis the difference in depths and variation in slope steepness between the two segments of the trench: the southern Java transect (coordinates 108.8°E 10.10°S to 113.0°E 10.75°S) and the northern Sumatra transect (97.5°E 1.1°S to 101.0°E 5.5°S). The thematic maps and geomorphological modelling were plotted using Generic Mapping Tools (GMT). The materials include high-resolution data on topography, geology and geophysics: GEBCO 15 arc-minute resolution grid, EGM2008 2.5 minute Earth Gravitation Model of 2008, GlobSed global 5‐arc‐minute total sediment thickness and vector geological datasets. In addition to the GEBCO-based bathymetric data, geological, topographic and geophysical maps, the results include enlarged transects for the Java and Sumatra segments, their slope gradients and cross-section profiles, derived from the bathymetric GEBCO dataset. The geomorphology framework of the Sunda Trench is largely controlled by the subduction of the Australian plate underneath the Sunda microplate. The geological processes take place in basin of the Indian Ocean at different stages of its evolution and influence the nature of the submarine geomorphology and geometric shape of the trench. Sunda Trench is seismically active part of the Pacific Ring of Fire. A large number of the catastrophic earthquakes are recorded around the trench. The histograms shows variation in depths along the segments of the Sumatra and Java. The Java segment has a bell-shaped data distribution in contrast to the Sumatra with bimodal pattern. The Java segment has the most repetitive depths at -2,500 to -5,200 m. The Sumatra transect has two peaks: 1) a classic bell-shaped peak at depths -4,500 m to -5,500 m; 2) shelf area with a peak from 0 to -1,750 m. The data at middle depths (-1,750 to -4,500 m) have a frequency <300 samples. The most frequent bathymetry for the Sumatra segment corresponds to the -4,750 m to -5,000 m (2,151 samples). Comparing to the Sumatra segment, the Java segment is deeper. For the depths >-6,000 m, there are only 138 samples for the Sumatra while 547 samples for Java. Furthermore, Java segment has more symmetrical geometric shape while Sumatra segment is asymmetric, one-sided. The Sumatra segment has a steepness of 57.86° on its eastern side (facing Sumatra Island) and a contrasting 14.58° on the western part. The Java segment has a steepness of 64.34° on its northern side (facing Java Island) and 24.95° on the southern part (facing Indian Ocean). The paper contributes to the studies of the submarine geomorphology in Indonesia.

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Topographic surface modelling using raster grid datasets by GMT: example of the Kuril–Kamchatka Trench, Pacific Ocean

Cited by 27 publications

References 51 publications

R Libraries {dendextend} and {magrittr} and Clustering Package scipy.cluster of Python For Modelling Diagrams of Dendrogram Trees

R Libraries {dendextend} and {magrittr} and Clustering Package scipy.cluster of Python For Modelling Diagrams of Dendrogram Trees

Using R packages 'tmap', 'raster' and 'ggmap' for cartographic visualization: An example of dem-based terrain modelling of Italy, Apennine Peninsula

Analysis of the difference in depths and variation in slope steepness of the Sunda Trench, Indonesia, east Indian Ocean

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