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

Chapter 10. Geoprocessing with a GPU Database

With the emergence of multi-core GPUs, new database technologies have been developed to take advantage of this improved technology. MapD, a startup based in San Francisco, is one example of these companies. Their GPU-based database technology was made open source in 2017 and is available for use on cloud services, such as Amazon Web Services (AWS) and Microsoft Azure. By combining the parallelization potential of GPUs with a relational database, the MapD database improves the speed of database queries and visualizations based on the data. 

MapD has created a Python 3 module, pymapd, that allows users to connect to the database and automate queries. This Python binding allows geospatial professionals to integrate the speed of a GPU database into an existing geospatial architecture, adding speed improvements to analysis and queries. Both of MapD's core offerings (the open source community version and the commercial enterprise version) are supported...