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
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Rasterizing a vector layer


Sometimes, a raster dataset is a more efficient way to display a complex vector that is merely a backdrop in a map. In these cases, you can rasterize a vector layer to turn it into an image.

Getting ready

We will demonstrate rasterizing a vector layer using the following contour shapefile, which you can download:

https://github.com/GeospatialPython/Learn/raw/master/contour.zip

Extract it to your /qgis_data/rasters directory.

How to do it...

We will run the gdalogr:rasterize algorithm to convert this vector data to a raster:

  1. Start QGIS.

  2. From the Plugins menu, select Python Console.

  3. Import the processing module:

            import processing 
    
  4. Run the algorithm, specifying the input data, the attribute from which to draw raster values, 0 to specify the pixel dimensions for the output instead of map dimensions, then the width and height, and finally the output raster name:

            processing.runalg("gdalogr:rasterize",
                              "/qgis_data/rasters/contour...