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

Esri GIS tools for Hadoop

With your environment set up and some basic knowledge of Ambari, HDFS, and Hive, you will now learn how to add a spatial component to your queries. To do so, we will use the Esri GIS tools for Hadoop.

The first step is to download the files located at the GitHub repository, which is located at: You will be using Ambari to move the files to HDFS not the container, so download these files to your local machine.


Esri has a tutorial for downloading the files by using ssh to connect to the container and then using git to clone the repository. You can follow these instructions here:

You can download the files by using the GitHub Clone or download button on the right-hand side of the repository. To unzip the archive, use one of the following commands:

unzip -d /home/pcrickard...