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

Reading and writing vector data with GeoPandas

It's time for some hands-on exercises. We'll start with reading and writing some vector data in the form of GeoJSON using the GeoPandas library, which is the application used to demonstrate all examples is Jupyter Notebook, which comes preinstalled with Anaconda3. If you've installed all geospatial Python libraries from Chapter 2, Introduction to Geospatial Code Libraries, you're good to go. If not, do this first. You might decide to create virtual environments for different combinations of Python libraries because of different dependencies and versioning. Open up a new Jupyter Notebook and a browser window and head over to and download the Natural Earth quick start kit at a convenient location. We'll examine some of that data for the rest of this chapter, along with some other geographical data files.

First, type the following code in a Jupyter Notebook with access to the GeoPandas library and run the...