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

Polina Lemenkova (1)

1 OUC - Ocean University of China

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.

Mots clés: Geomorphology analysis; Pacific Ocean; Python; R programming; Programming Language; Correlation Analysis; Statistical analysis; Statistics & numerical data; Data analysis; Data modelling; Mariana Trench; Geospatial analysis; Mapping; Cartography Visualization

Domaines: Informatique [cs]; Synthèse d'image et réalité virtuelle [cs.GR]; Interface homme-machine [cs.HC]; Modélisation et simulation; Recherche d'information [cs.IR]; Langage de programmation [cs.PL]; Arithmétique des ordinateurs; Environnements Informatiques pour l'Apprentissage Humain; Océan, Atmosphère; Interfaces continentales, environnement; Planète et Univers [physics]; Sciences de la Terre; Géomorphologie; Géologie appliquée; Tectonique; Océanographie; Informatique et langage [cs.CL]

Polina Lemenkova. Computing and Plotting Correlograms by Python and R Libraries for Correlation Analysis of the Environmental Data in Marine Geomorphology. Jeomorfolojik Araştırmalar Dergisi / Journal of Geomorphological Researches, 2019, 3, pp.1 - 16. ⟨10.6084/m9.figshare.10012808⟩. ⟨hal-02327797⟩