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.
TopGeospatial Data
Geospatial data refers to information that identifies the location of objects, events, or phenomena on the Earth's surface. This data includes coordinates like latitude and longitude, enabling the visualization of features on maps and facilitating spatial analysis in Geographic Information Systems (GIS) (Meenakshi Kandpal, 2024). Geospatial data can be categorized into two primary models: vector and raster. Vector data represents discrete features using points, lines, and polygons, while raster data consists of a grid of cells where each cell holds a value. Geospatial datasets often include attribute tables that provide additional information about the data, allowing for queries, analyses, and visualization of specific variables. Common file formats in GIS applications include .shp, .dbf, .prj, and .shx. Geospatial data is crucial in various fields, offering insights into patterns, relationships, and trends that help in decision-making processes and spatial analysis (A. Voisard, 2024).