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


This chapter introduced the brand new ArcGIS API for Python, which is built on Python 3.5. You learned how to make use of the API, Jupyter Notebooks, and data processing with data stored in the cloud-based ArcGIS Online system. We covered how the API is organized into different modules, how to install the API, how to use the map widget, how to log in to ArcGIS Online using different user accounts, and working with vector and raster data. Using some of the API modules, we learned how to use the API for Python to perform basic geospatial analysis and to create ArcGIS Online web maps.

The next chapter will introduce Python tools for interacting with cloud-based data for search and fast data processing. In particular, it focuses on the use of Elasticsearch and MapD GPU databases, both of which are based on the AWS cloud infrastructure. The reader will learn to create cloud services for geospatial search, geolocated data processing, geolocated data, and learn how to use Python libraries...