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 Jupyter Notebook

We have covered the basic installation of Jupyter Notebook in Chapter 1Package Installation and Management and in the previous chapter at various instances to run code and get the desired output.

Here, we will be using Jupyter Notebook for CARTOframes to connect to an account and analyze geospatial data and display it.

Connecting to an account

In the first code box, we will import the CARTOframes module, and pass the API key string along with the base URL, which is generated from your CARTO username as https://{username} In this case, the URL is

In this code block, the API key and the URL are passed to the CartoContext class, and a CartoContext connect object is returned and assigned to the variable cc. With this object, we can now interact with the datasets associated with our account, load datasets into the account, and even generate maps directly in the Jupyter Notebook. 

Once the code has been entered into the section...