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
Contributors
Preface
7
Geoprocessing with Geodatabases
Index

GeoPandas


GeoPandas has been introduced in the GeoPandas section of  Chapter 2Introduction to Geospatial Code Libraries, where its data structures and methods have also been covered.

Geospatial analysis with GeoPandas

GeoPandas was created to offer data to scientists who want to work with spatial data similar to pandas, and this means giving access to geospatial attribute data through data structures not available through pandas. Combine this with a set of geometric operations, data overlay capabilities, geocoding and plotting capabilities and you have an idea of this library's capabilities. In the examples mentioned as we proceed, we'll cover GeoPandas' plotting methods, explain how to access and subset spatial data, and provide a typical workflow for doing geospatial analysis with GeoPandas, where data processing is an important condition for being able to analyze and interpret the data correctly.

Let's have a look at a few code examples of GeoPandas.

Selecting and plotting geometry data...