Book Image

Mastering Geospatial Analysis with Python

By : Silas Toms, Paul Crickard, Eric van Rees
Book Image

Mastering Geospatial Analysis with Python

By: Silas Toms, Paul Crickard, Eric van Rees

Overview of this book

Python comes with a host of open source libraries and tools that help you work on professional geoprocessing tasks without investing in expensive tools. This book will introduce Python developers, both new and experienced, to a variety of new code libraries that have been developed to perform geospatial analysis, statistical analysis, and data management. This book will use examples and code snippets that will help explain how Python 3 differs from Python 2, and how these new code libraries can be used to solve age-old problems in geospatial analysis. You will begin by understanding what geoprocessing is and explore the tools and libraries that Python 3 offers. You will then learn to use Python code libraries to read and write geospatial data. You will then learn to perform geospatial queries within databases and learn PyQGIS to automate analysis within the QGIS mapping suite. Moving forward, you will explore the newly released ArcGIS API for Python and ArcGIS Online to perform geospatial analysis and create ArcGIS Online web maps. Further, you will deep dive into Python Geospatial web frameworks and learn to create a geospatial REST API.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
Packt Upsell
Geoprocessing with Geodatabases

Chapter 5. Vector Data Analysis

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...