Book Image

QGIS Python Programming Cookbook, Second Edition - Second Edition

By : Joel Lawhead
Book Image

QGIS Python Programming Cookbook, Second Edition - Second Edition

By: Joel Lawhead

Overview of this book

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.
Table of Contents (16 chapters)
QGIS Python Programming Cookbook - Second Edition
About the Author
About the Reviewer
Customer Feedback

Generalizing a vector layer

Generalizing geometry, also known as simplifying, removes points from a vector layer to reduce the space required to store the data. Otherwise, simplification may result in small but acceptable changes within a specified tolerance.

Getting ready

For this recipe, we will use a boundary file for the state of Mississippi, which you can download from the following URL:

Extract the zipped shapefile to a directory named /qgis_data/ms.

How to do it...

Generalizing is native to QGIS, but we will access it in PyQGIS through the Processing Toolbox using the qgis:simplifygeometries algorithm.

  1. Start QGIS.

  2. From the Plugins menu, select Python Console.

  3. Import the processing module:

            import processing 
  4. Now, we run the processing algorithm specifying the algorithm name, the input data, a tolerance value spacing between points, and the output dataset name: