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

Creating a heat map

A heat map is used to show the geographic clustering of data using a raster image that shows density. The clustering can also be weighed using a field in the data to not only show geographic density but also an intensity factor. In this recipe, we'll use earthquake point data to create a heat map of the impact of an earthquake and weigh the clustering by the earthquake's magnitude.

Getting ready

This recipe requires no preparation; however, make sure your installation of QGIS includes SAGA. For more information, see the Installing QGIS for development recipe from Chapter 1Automating QGIS.

How to do it...

We will build a map with a worldwide base layer of countries and earthquake locations, both in GeoJSON. Next, we'll run the SAGA kernel density estimation algorithm to produce the heat map image. We'll create a layer from the output, add a color shader to it, and add it to the map. To do this, we need to perform the following steps:

  1. First, we'll import the Python libraries...