This chapter will cover geospatial analysis and processing of vector data. The following three Python libraries will be covered—Shapely, OGR, and GeoPandas. The reader will learn how to use these Python libraries to perform geospatial analysis, including the writing of basic and advanced analysis scripts.

Each library is covered separately, with an overview of its data structures, methods, and classes where appropriate. We'll discuss the best use cases for each library and how to use them together for geospatial workflows. Short example scripts illustrate how to perform the basic geographical analysis. The GeoPandas library enables more complex functionality for doing data science tasks and incorporating geospatial analysis.

In this chapter, we'll cover the following topics:

• Reading and writing vector data
• Creating and manipulating vector data
• Visualizing (plotting) vector data on a map
• Working with map projections and reproject data
• Performing spatial operations such as spatial joins
• Working with vector geometries and attribute data in tabular form
• Analyzing the results to answer questions, such as how many wildfires are there in area x?

After this chapter, you'll have a solid foundation to start working with geospatial vector data. You'll know the characteristics and use cases of all three geospatial libraries, and know how to do basic vector data processing and analysis.