Affiliation:
1. Coimbatore Institute of Technology, India
2. Kumaraguru College of Technology, India
Abstract
This chapter explores the intricate realm of geospatial analysis leveraging the power of Python. This chapter embarks on a journey through the fundamentals of geospatial data types, formats, and sources, laying a robust foundation for navigating the complexities of spatial analysis. Key Python libraries such as Geopandas, GDAL, and Fiona are meticulously dissected, elucidating their pivotal roles in processing, analyzing, and visualizing geospatial data. Matplotlib's contribution to geospatial data visualization adds insight, enhancing spatial information's communicative power. Furthermore, the chapter delves into data integration techniques, showcasing how Python seamlessly integrates with GIS tools to extend, customize, and streamline spatial analyses. By unraveling the functionalities of these essential libraries and tools, this chapter equips readers with the knowledge and skills necessary to master geospatial analysis in Python.