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 1. Package Installation and Management

This book focuses on important code libraries for geospatial data management and analysis for Python 3. The reason for this is simple—as Python 2 is near the end of its life cycle, it is quickly being replaced by Python 3. This new Python version comes with key differences in organization and syntax, meaning that developers need to adjust their legacy code and apply new syntax in their code. Fields such as machine learning, data science, and big data have changed the way geospatial data is managed, analyzed, and presented today. In all these areas, Python 3 has quickly become the new standard, which is another reason for the geospatial community to start using Python 3.

The geospatial community has been relying on Python 2 for a long time, as many dependencies weren't available for Python 3 or not working correctly. But now that Python 3 is mature and stable, the geospatial community has taken advantage of its capabilities, resulting in many new libraries and tools. This book aims to help developers understand open source and commercial modules for geospatial programs written in Python 3, offering a selection of major geospatial libraries and tools for doing geospatial data management and data analysis.

This chapter will explain how to install and manage the code libraries that will be used in this book. It will cover the following topics:

  • Installing Anaconda
  • Managing Python packages using Anaconda Navigator, Anaconda Cloud, conda, and pip
  • Managing virtual environments using Anaconda, conda, and virtualenv
  • Running a Jupyter Notebook