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

Summary


In this chapter, we introduced the major code libraries used to process and analyze geospatial data. You learned the characteristics of each library, how they are related or are distinct to each other, how to install them, where to find additional documentation, and typical use cases. GDAL is a major library that includes two separate libraries, OGR and GDAL. Many other libraries and software applications use GDAL functionality under the hood, examples are Fiona and Rasterio, which were both covered in this chapter. These were created to make it easier to work with GDAL and OGR in a more Pythonic way.

The next chapter will introduce spatial databases. These are used for data storage and analysis, with examples being SpatiaLite and PostGIS. You will also learn how to use different Python libraries to connect to these databases.