Dergiler / Jeomorfolojik Araştırmalar Dergisi (Online) / 2019 / Cilt: 0 - Sayı: 3

COMPUTING AND PLOTTING CORRELOGRAMS BY PYTHON AND R LIBRARIES FOR CORRELATION ANALYSIS OF THE ENVIRONMENTAL DATA IN MARINE GEOMORPHOLOGY

COMPUTING AND PLOTTING CORRELOGRAMS BY PYTHON AND R LIBRARIES FOR CORRELATION ANALYSIS OF THE ENVIRONMENTAL DATA IN MARINE GEOMORPHOLOGY

Polina LEMENKOVA

Sayfa
1–16
DOI
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Abstract

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