Abstract
This chapter examines the introduction to geospatial data, Python programming, and the implications of Python in geospatial data analysis. There are different forms of geospatial data: tabular data, raster data, and vector data. The package or library is essential when using geospatial data in Python programming. Geospatial data is huge and complex because of the nature of geospatial data computing Python is used for. This chapter briefly introduced the Role of Python libraries in Geospatial Analysis such as Arcpy, Basemap, Cartopy, EarthPy, Fiona-GO, Folium, GDAL and OGR, GEE-Py, GeoAlchemy, Geocoder, Geodaisy, Geopandas, Geoplot, Geopy, Geopyspark, GeospatialPDF, GeostatsPy, GPSBabel, 3-Py,ipyleaflet, KeplerglPandas, Plotl, Plotly Express, Plotnine, PyGeos, SentinelHub-Py, Shapely, SpatialPandas, Turfpy. Python has emerged as an indispensable tool in geospatial data analysis.
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