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

Finding the least cost path

Least cost path (LCP) analysis is the raster equivalent of network analysis, which is used to find the optimal path between two points in a raster. In this recipe, we'll perform LCP analysis on a Digital Elevation Model (DEM).

Getting ready

You need to download the following DEM and extract the ZIP file to your qgis_data/rasters directory: must also have a verison of QGIS which includes GRASS and SAGA. For more information, see the Installing QGIS for development recipe from Chapter 1Automating QGIS.

How to do it...

We will load our DEM and two shapefiles consisting of start and end points. Then, we'll use GRASS through the Processing Toolbox to create a cumulative cost layer that assigns a cost to each cell in a raster based on its elevation, the value of the other cells around it, and its distance to and from the end points.

Then, we'll use a SAGA processing algorithm to find the least cost path between two points. Finally...