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


Django, with its batteries-included philosophy, creates complete applications with very few outside libraries required. This application performs data management and data analysis using only the Django built-in tools and the GDAL/OGR library. Enabling the GeoDjango functionality is a relatively seamless experience because it is an integral part of the Django project.

Creating web applications with Django allows for a lot of instant functionality, including the administrative panel. The LayerMapping makes it easy to import data from shapefiles. The ORM model makes it easy to perform geospatial filters or queries. The templating system makes it easy to add web maps as well as location intelligence to a website.

In the next chapter, we will use a Python web framework to create a geospatial REST API. This API will accept requests and return JSON encoded data representing geospatial features.