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

Chapter 6. Raster Data Processing

Geographic information systems (GIS) are often comprised of points, lines, and polygons. These data types are called vector data. There is, however, another data type in GIS—rasters. In this chapter, you will learn the basics of working with raster data. You will learn how to:

  • Use the Geospatial Data Abstraction Library (GDAL) to load and query rasters
  • Use GDAL to modify and save rasters
  • Use GDAL to create rasters
  • Load rasters into PostgreSQL
  • Perform queries on rasters using PostgreSQL


Installing GDAL can be difficult. By using virtual environments and running Anaconda, you can simplify this process by using the GUI of the environment.