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PROCESSING OCEANOGRAPHIC DATA BY PYTHON LIBRARIES NUMPY, SCIPY AND PANDAS

Authors: Lemenkova, Polina;

PROCESSING OCEANOGRAPHIC DATA BY PYTHON LIBRARIES NUMPY, SCIPY AND PANDAS

Abstract

The study area is located in western Pacific Ocean, Mariana Trench. The aim of the data analysis is to analyze the potential influence of how various geological and tectonic factors may affect the geomorphological shape of the Mariana Trench. Statistical analysis of the data set in marine geology and oceanography requires an adequate strategy on big data processing. In this context, current re-search proposes a combination of the Python-based methodology that couples GIS geospatial data analysis. The Quantum GIS part of the methodology produces an optimized representative sampling dataset consisting of 25 cross-section profiles having in total 12,590 bathymetric observation points. The sampling of the geospatial dataset are located across the Mariana Trench. The second part of the methodology consists of statistical data processing by means of high-level programming language Python. Current research uses libraries Pandas, NumPy and SciPy. The data processing also involves the subsampling of two auxiliary masked data frames from the initial large data set that only consists of the target variables: sediment thickness, slope angle degrees and bathymetric observation points across four tectonic plates: Pacific, Philippine, Mariana, and Caroline. Finally, the data were analyzed by several approaches: 1) Kernel Density Estimation (KDE) for analysis of the probability of data distribution; 2) stacked area chart for visualization of the data range across various segments of the trench; 3) spacial series of radar charts; 4) stacked bar plots showing the data distribution by tectonic plates; 5) stacked bar charts for correlation of sediment thickness by profiles, versus distance from the igneous volcanic areas; 6) circular pie plots visualizing data distribution by 25 profiles; 7) scatterplot matrices for correlation analysis between marine geologic variables. The results presented a distinct correlation between the geologic, tectonic and oceanographic variables. Six Python codes are provided in full for repeatability of this research.

Keywords

Hidrobiyoloji, [INFO.INFO-LO] Computer Science [cs]/Logic in Computer Science [cs.LO], [SDU.STU.GM] Sciences of the Universe [physics]/Earth Sciences/Geomorphology, [SDU.STU.OC] Sciences of the Universe [physics]/Earth Sciences/Oceanography, Disciplines graphiques, NumPy, SciPy, Géographie physique, Maritime Engineering, Géodésie, Statistiken, Statistics, Pandas, Méthodes mathématiques et quantitatives, Programming language, Sciences de la terre et du cosmos, Datenanalyse, Marianengraben, [INFO.INFO-NA] Computer Science [cs]/Numerical Analysis [cs.NA], Hydrobiology, Sciences exactes et naturelles, Géodynamique et tectonique, Systèmes d'information géographique, Data analysis, Sciences de l'ingénieur, Physique du globe, Géodésie appliquée topographie [géodésie], Mariana Trench, Mariana Trench;Pacific Ocean;Python;SciPy;NumPy;Pandas;Programming language;Statistics;Data analysis, Océanographie physique et chimique, Deniz Mühendisliği, Géologie, Géomorphologie et orographie, Programmiersprache, Pacific Ocean, Cartographie, Pazifik See, [SDU.OCEAN] Sciences of the Universe [physics]/Ocean, Atmosphere, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Programmation et méthodes de simulation, Méthodologie de la recherche scientifique, Sémantique des langages de programmation, [INFO.INFO-PL] Computer Science [cs]/Programming Languages [cs.PL], Mariana Trench, Pacific Ocean, Python, Programming language, SciPy, NumPy, Pandas, Statistics, Data analysis, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], [SDU.STU.AG] Sciences of the Universe [physics]/Earth Sciences/Applied geology, [SDU.STU] Sciences of the Universe [physics]/Earth Sciences, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Programmation du calcul numérique, Gravimétrie, Python

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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