QGIS Python Programming Cookbook - Second Edition: Automating geospatial development

Master over 170 recipes that will help you turn QGIS from a desktop GIS tool into a powerful automated geospatial framework

About This Book

• Delve into the undocumented features of the QGIS API
• Get a set of user-friendly recipes that can automate entire geospatial workflows by connecting Python GIS building blocks into comprehensive processes
• This book has a complete code upgrade to QGIS 2.18 and 30 new, valuable recipes

Who This Book Is For

This book is for geospatial analysts who want to learn more about automating everyday GIS tasks as well as programmers responsible for building GIS applications. The short, reusable recipes make concepts easy to understand and combine so you can build larger applications that are easy to maintain.

What You Will Learn

• Use Python and QGIS to produce captivating GIS visualizations and build complex map layouts
• Find out how to effectively use the poorly-documented and undocumented features of the QGIS Python API
• Automate entire geospatial workflows by connecting Python GIS building blocks into comprehensive processes
• Create, import, and edit geospatial data on disk or in-memory
• Change QGIS settings programmatically to control default behavior
• Automatically generate PDF map books
• Build dynamic forms for field input

In Detail

QGIS is a desktop geographic information system that facilitates data viewing, editing, and analysis. Paired with the most efficient scripting language-Python, we can write effective scripts that extend the core functionality of QGIS.

Based on version QGIS 2.18, this book will teach you how to write Python code that works with spatial data to automate geoprocessing tasks in QGIS. It will cover topics such as querying and editing vector data and using raster data. You will also learn to create, edit, and optimize a vector layer for faster queries, reproject a vector layer, reduce the number of vertices in a vector layer without losing critical data, and convert a raster to a vector. Following this, you will work through recipes that will help you compose static maps, create heavily customized maps, and add specialized labels and annotations. As well as this, we'll also share a few tips and tricks based on different aspects of QGIS.

Style and approach

This book follows a recipe-based problem-solution approach to address and dispel challenges faced when implementing and using QGIS on a regular basis.

Joel Lawhead is a PMI-certified Project Management Professional (PMP), a certified Geographic Information Systems Professional, and an award-winning firm specializing in geospatial technology integration and harsh-environment engineering.

Joel builds geospatial systems for US government agencies, including NASA, NOAA, the US Department of Homeland Security, and the military. He also works with private organizations, including the National Oceans and Applications Research Center (NOARC) and The Ocean Cleanup. He has authored other books with Packt Publishing, including Learning Geospatial Analysis with Python, QGIS Python Programming Cookbook, and Learning Geospatial Analysis with Python, Second Edition. His cookbook recipes have been featured in two editions of the O'Reilly Python Cookbook.

Joel began using Python in 1997 and combined it with geospatial software development in 2000. He is also the developer of the widely used open source Python Shapefile Library (PyShp) and Twitter feed, @SpatialPython, discussing the use of Python within the geospatial industry.

In 2011, Joel reverse-engineered and published the undocumented shapefile spatial indexing format and assisted fellow geospatial Python developer, Marc Pfister, in reversing the compression algorithm, allowing developers around the world to create better integrated and more robust geospatial applications involving shapefiles.

In 2002, Joel received the international Esri Special Achievement in GIS award for his work on the Real-Time Emergency Action Coordination Tool (REACT) for emergency management using geospatial analysis.