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

Yıl 2019, Sayı: 3, 1 - 16, 22.10.2019

Polina Lemenkova

Öz

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.

Anahtar Kelimeler

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

• Arrospide, F., Mao, L. & Escauriaza, C. (2018). Morphological evolution of the Maipo River in central Chile: Influence of instream gravel mining. Geomorphology, 306, 182–197. doi: 10.1016/j.geomorph.2018.01.019
• Berger J.-F. (2011). Hydrological and post-depositional impacts on the distribution of Holocene archaeological sites: The case of the Holocene middle Rhône River basin, France. Geomorphology, 129, 167–182. doi: 10.1016/j.geomorph.2011.02.021
• Bechter, T., Baumann, K., Birk, S., Bolik, F., Graf, W., Pletterbauer, F.. (2018),. LaRiMo – A simple and efficient GIS-based approach for large-scale morphological assessment of large European rivers. Science of the Total Environment, 628–629, 1191–1199. doi: 10.1016/j.scitotenv.2018.02.084
• Bertoldi, W. and Welber, M. and Gurnell, A. M. and Mao, L. and Comiti, F. and Tal, M. (2015). Physical modelling of the combined effect of vegetation and wood on river morphology. Geomorphology, 246, 178–187. doi: 10.1016/j.geomorph.2015.05.038
• Chadwick, W. W. Jr. and Merle, S.G. and Baker, E.T. and Walker, S. L., Resing, J. A. and Butterfield, D. A. and Anderson, M.O. and Baumberger, T. & Bobbitt, A. M. (2018). A Recent Volcanic Eruption Discovered on the Central Mariana Back-Arc Spreading Center. Frontiers in Earth Science, 6, 1–16.
• Cielen, D. and Meysman, A. D. B. and Ali M. (2016). Introducing Data Science. Big Data, Machine Learning and More, Using Python Tools. Manning, Shelter Island, U.S., 322.
• Curtis, A. C. & Moyer, C. L. (2005). Mariana forearc serpentinte mud volcanoes harbor novel communities of extremophilic ArchaeaJ. Geomicrobiology Journal, 30(5), 430–441. doi: 10.1080/01490451.2012.705226
• Faccenna, C., Holt, A. F., Becker, Th,. W. & Lallemand, S. and Royden, L. H. (2018). Dynamics of the Ryukyu/Izu-Bonin-Marianas double subduction system. Tectonophysics, 1, 229–238, ISSN: 0040-1951. doi: 10.1016/j.tecto.2017.08.011
• Fujioka, K. and Okino, K. and Kanamatsu, T. and Ohara, Y. (2002). Morphology and origin of the Challenger Deep in the Southern Mariana Trench. Geophysical Research Letters, 29 (10), 13-72. doi: 10.1029/2001gl013595
• Halterman, R. L. (2011). Learning to program with Python. Python Software Foundation, 283.
• Harrington, A. N. (2015). Hands-on Python Tutorial Release 1.0 for Python Version 3.1+. Loyola University Chicago, US, 191.
• Hudson, P. F. & Middelkoop, H. & Stouthamer, E. (2008). Flood management along the Lower Mississippi and Rhine Rivers (The Netherlands) and the continuum of geomorphic adjustment. Geomorphology, 101, 209–236. doi: 10.1016/j.geomorph.2008.07.001
• Kawabe, M. (1993). Deep water properties and circulation in the western North Pacific. In: Teramoto, T. (ed.) Deep Ocean Circulation: Physical and Chemical Aspects, 17–37.
• Kitahashi, T., Jenkins, R. G., Nomaki, H., Shimanaga, M., Fujikura, K., & Kojima, S. (2014). Effect of the 2011 Tohoku Earthquake on deep-sea meiofaunal assemblages inhabiting the landward slope of the Japan Trench. Marine Geology, 358, 128–137. doi: 10.1016/j.margeo.2014.05.004
• Marchese E., Scorpio, V., Fuller, I., McColl, S. & Comiti, F. (2017). Morphological changes in Alpine rivers following the end of the Little Ice Age Geomorphology, 295, 811–826. doi: 10.1016/j.geomorph.2017.07.018
• R Development Core Team, (2014). R: a language and environment for statistical computing. Vienna, Austria. URL: https://www.r-project.org/
• Taira, K., Kitagawa, S., Yamashiro, T. & Yanagimot, D. Deep and Bottom Currents in the Challenger Deep, Mariana Trench, Measured with Super-Deep Current Meters. Journal of Oceanography, 60(6), 919–926. doi: 10.1007/s10872-005-0001-y
• Theberge, A. (2008). Thirty years of discovering the Mariana Trench. Hydro International, 12, 38–39. issn: 1385-4569 url: https://www.hydro-international.com/content/article/thirty-years-of-discovering-the-mariana-trench
• Tian, J., Fan, L., Liu, H., Liu, J., Li, Y., Qin, Q., Gong, Z., Chen, H., Sun, Z., Zou, L., Wang, X., Xu, H., Bartlett, D., Wang, M., Zhang, Y.-Z., Zhang, X.-H., & Zhang, C. L. (2018). A nearly uniform distributional pattern of heterotrophic bacteria in the Mariana Trench interior. Deep-Sea Research Part I: Oceanographic Research Papers., 0, 00–00. doi: 10.1016/j.dsr.2018.10.002
• Zhang, F., Lin, J., & Zhan, W. (2014). Variations in oceanic plate bending along the Mariana trench. Earth and Planetary Science Letters, 401, 206–214. doi: 10.1016/j.epsl.2014.05.032
• Zhou, Z., Lin, J. & Behn, M. D. (2015). Mechanism for normal faulting in the subducting plate at the Mariana Trench. Geophysical Research Letters, 4o2. 4309–4317. doi: 10.1002/2015GL063917