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


Using a cloud-based GPU database like MapD Core, and the Immerse visualization studio will pay dividends when designing and implementing a GIS. It offers speed and cloud reliability to both tabular and spatial queries and allows the data to be shared in interactive dashboards (which rely on JavaScript technologies such as D3.js and MapBox GL JavaScript) that are simple to create and publish. 

With the MapD Python module, pymapd, cloud data can become an integrated part of a query engine. Data can be pushed to the cloud or pulled down to use locally. Analyses can be performed rapidly, using the power of GPU parallelization. It's worth installing MapD on a virtual server in the cloud, or even locally, to test out the potential of the software.

In the next chapter, we will explore the use of Flask, SQLAlchemy, and GeoAlchemy2 to create an interactive web map with a PostGIS geodatabase backend.