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
Contributors
Preface
7
Geoprocessing with Geodatabases
Index

Geospatial Data Abstraction Library (GDAL) and the OGR Simple Features Library


The Geospatial Data Abstraction Library (GDAL)/OGR Simple Features Library combines two separate libraries that are generally downloaded together as a GDAL. This means that installing the GDAL package also gives access to OGR functionality, which is why they're covered together here. The reason GDAL is covered first is that other packages were written after GDAL, so chronologically, it comes first. As you will notice, some of the packages covered in this chapter extend GDAL's functionality or use it under the hood.

GDAL was created in the 1990s by Frank Warmerdam and saw its first release in June 2000. Later, the development of GDAL was transferred to the Open Source Geospatial Foundation (OSGeo). Technically, GDAL is a little different than your average Python package as the GDAL package itself was written in C and C++, meaning that in order to be able to use it in Python, you need to compile GDAL and its associated...