PROCESSING OCEANOGRAPHIC DATA BY PYTHON LIBRARIES NUMPY, SCIPY AND PANDAS

Polina Lemenkova

doi:10.3153/AR19009

EN

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 research 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

Mariana Trench,Pacific Ocean,Python,SciPy,NumPy,Pandas,Programming language,Statistics,Data analysis

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Details

Primary Language

English

Subjects

Hydrobiology , Maritime Engineering

Journal Section

Research Article

Authors

Polina Lemenkova ^*
0000-0002-5759-1089
China

Publication Date

April 1, 2019

Submission Date

March 24, 2019

Acceptance Date

April 8, 2019

Published in Issue

Year 1970 Volume: 2 Number: 2

DOI

https://doi.org/10.3153/AR19009

IZ

https://izlik.org/JA55KM62CP

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RIS / Bibtex

APA

Lemenkova, P. (2019). PROCESSING OCEANOGRAPHIC DATA BY PYTHON LIBRARIES NUMPY, SCIPY AND PANDAS. Aquatic Research, 2(2), 73-91. https://doi.org/10.3153/AR19009

Öz

PROCESSING OCEANOGRAPHIC DATA BY PYTHON LIBRARIES NUMPY, SCIPY AND PANDAS

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite