Computing and Plotting Correlograms by Python and R Libraries for Correlation Analysis of the Environmental Data in Marine Geomorphology

Year 2019, Issue: 3, 1 - 16, 22.10.2019

Polina Lemenkova

Abstract

The
geomorphology of the Mariana Trench, the deepest ocean trench on the
Earth, has a complex character: its transverse profile is asymmetric,
the slopes are higher on the side of the Mariana island arc. The
shape of the Mariana Trench is a strongly elongated, arched in plan
and lesser rectilinear depression. The slopes of the trench are
dissected by deep underwater canyons with various narrow steps on the
slopes of various shapes and sizes, caused by active tectonic and
sedimentation processes. Understanding of factors that may affect the
shape of the geomorphology of such complex structure requires
advanced methods of numerical computing. Current research is focused
on the analysis of the geomorphology of the Mariana Trench by
application of statistical libraries embedded in Python and R
programming languages for the data analysis. Workflow algorithms
include processing a data set by analysis, computing and visual
plotting of the graphs. The research aims is to understand the
environmental interactions affecting submarine geomorphology of the
Mariana Trench by statistical data analysis. Technically, used
algorithms included libraries of Python (Seaborn, Matplotlib, Pandas,
SciPy and NumPy) and libraries of R ({hexbin},
{ggally}, {ggplot2}). Technically, following types of the statistical
analysis were tested for computing and plotting: correlograms,
histograms, strip plots, ridgeline plots and hexagonal diagrams for
the bathymetric and geomorphic analysis. Python, being a high-level
language, shown more straightforward approach for the statistical
data analysis, while R implies more power in the data visualization.
The results of the geospatial data modelling show detected
correlation between various factors (geology, bathymetry, tectonics)
affecting submarine geomorphology that reveal unevenness in its
structure. Both programming languages demonstrated significant
functionality for the spatial data analysis. The effective and
accurate geospatial data visualization demonstrated by Python and R
proves high potential of their application in the geomorphological
studies.

Keywords

submarine geomorphology, Mariana Trench, Pacific Ocean, marine geology, Python, R, programming language, geospatial analysis

Computing and Plotting Correlograms by Python and R Libraries for Correlation Analysis of the Environmental Data in Marine Geomorphology

Abstract

The geomorphology of the Mariana Trench, the deepest ocean trench on the Earth, has a complex character: its transverse profile is asymmetric, the slopes are higher on the side of the Mariana island arc. The shape of the Mariana Trench is a strongly elongated, arched in plan and lesser rectilinear depression. The slopes of the trench are dissected by deep underwater canyons with various narrow steps on the slopes of various shapes and sizes, caused by active tectonic and sedimentation processes. Understanding of factors that may affect the shape of the geomorphology of such complex structure requires advanced methods of numerical computing. Current research is focused on the analysis of the geomorphology of the Mariana Trench by application of statistical libraries embedded in Python and R programming languages for the data analysis. Workflow algorithms include processing a data set by analysis, computing and visual plotting of the graphs. The research aims is to understand the environmental interactions affecting submarine geomorphology of the Mariana Trench by statistical data analysis. Technically, used algorithms included libraries of Python (Seaborn, Matplotlib, Pandas, SciPy and NumPy) and libraries of R ({hexbin}, {ggally}, {ggplot2}). Technically, following types of the statistical analysis were tested for computing and plotting: correlograms, histograms, strip plots, ridgeline plots and hexagonal diagrams for the bathymetric and geomorphic analysis. Python, being a high-level language, shown more straightforward approach for the statistical data analysis, while R implies more power in the data visualization. The results of the geospatial data modelling show detected correlation between various factors (geology, bathymetry, tectonics) affecting submarine geomorphology that reveal unevenness in its structure. Both programming languages demonstrated significant functionality for the spatial data analysis. The effective and accurate geospatial data visualization demonstrated by Python and R proves high potential of their application in the geomorphological studies.

Keywords

submarine geomorphology, Mariana Trench, Pasific Ocean, marine geology, Python, R Programming Language, geospatial anaysis

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