Python Libraries Matplotlib, Seaborn and Pandas for Visualization Geo-spatial Datasets Generated by QGIS
Analele stiintifice ale Universitatii "Alexandru Ioan Cuza" din Iasi - seria Geografie, vol. 64(1), pp. 13-32, 2020
20 Pages Posted: 13 Nov 2020
Date Written: September 25, 2020
This work aim is to perform modelling and spatial analysis of the marine geological data using combination of the QGIS and Python programming. Selecting proper cartographic software is important part of the geospatial research. QGIS provides organizing data in a GIS project for mapping and spatial visualization through vector and raster layers stored in GIS. Study area is Mariana Trench, west Pacific Ocean. A series of cross-section profiles were digitized in QGIS and used for further data processing in Python. Mariana Trench has complex geomorphic structure and unevenness in profiles stretching south-westwards. The geomorphology is subjected to various phenomena that affect its shape. These include bathymetry, geodesy, gravimetry, tectonics plates and geological settings, studied in this paper. To understand the structure of the trench, a data modelling using bathymetric analysis was performed by combination of QGIS mapping and statistical analysis in Python’s library Seaborn. Statistical data modelling aimed at the analysis of the spatial variation of the geomorphology of the trench using following methods: multiple facet grids, area charts for the data frames, regression analysis, letter- value plots, hexagonal and Kernel density estimation. The results of the geospatial data analysis show spatial unevenness of the geomorphic structure, gravimetric, geodetic and bathymetric settings of the Mariana Trench. The study demonstrated effectiveness of Python application in geographic data analysis with Python codes provided for repeatability.
Keywords: Quantum GIS, Python, Matplotlib, Geospatial Analysis
JEL Classification: Y92, Q00, Q01, Q40, Q50, Q51, Q54, Q55, Q56, Q57
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