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 3. Introduction to Geospatial Databases

In the previous chapters, you learned how to set up your Python environment and learned about the different libraries available for working with geospatial data using Python. In this chapter, you will start working with data.

Databases provide one of the most popular ways to store large amounts of data, and one of the most popular open source databases is PostgreSQL. PostGIS extends PostgreSQL, adding geographic objects and the ability to query records spatially. When PostgreSQL and PostGIS are combined, they create a powerful geospatial data repository.

Geospatial databases improve on basic relational database queries by allowing you to query your data by location or by location to other features in the database. You can also perform geospatial operations such as measurements of features, distances between features, and converting between projections. Another feature of geospatial databases is the ability to create new geometries from existing...