Python for Geospatial Data Analysis

Abstract

This chapter serves as a valuable resource for computer science enthusiasts, researchers, and practitioners seeking an understanding of geospatial analysis with Python. The chapter begins with an introduction to geospatial analysis, highlighting the significance of geospatial data across various domains. It establishes Python's stand in this field, positioning it as a powerful tool for geospatial analyses. The subsequent sections explore fundamental concepts, such as vector versus raster data, coordinate reference systems and projections, geometric objects, topological relationships, and spatial operations. Further, prominent Python libraries for geospatial analysis are explored. GeoPandas is introduced, detailing its capabilities in working with geospatial data, handling geometric data structures, and leveraging spatial operations. Shapely is examined for its role in geometric manipulations. Fiona is explored as a library for handling geospatial data. Discussion on Folium showcases its utility in creating interactive and customized maps.