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


In this chapter, you learned how to set up a Hadoop environment. This required you to install Linux and Docker to download an image from Hortonworks, and to learn the ropes of that environment. Much of this chapter was spent on the environment and how to perform a spatial query using the GUI tools provided. This is because the Hadoop environment is complex and without a proper understanding, it would be hard to fully understand how to use it with Python. Lastly, you learned how to use HDFS and Hive in Python. The Python libraries for working with Hadoop, Hive, and HDFS are still developing. This chapter provided you with a foundation so that when these libraries improve, you will have enough knowledge of Hadoop and the accompanying technologies to implement these new Python libraries.