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
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Adding a layer to

Cloud services are becoming very common, and geospatial maps are no exception. This recipe demonstrates using a service named, which serves vector layers online which you can upload from QGIS using Python.

Getting ready

For this recipe, you will need to install the qgisio plugin using the QGIS plugin manager. Note that this plugin is considered experimental, so ensure Show also experimental plugins is checked in you plugin manager settings.

You will also need an unprojected shapefile, such as the one from the following URL:

Decompress the zip file and place it in your qgis_data directory named shapes.

How to do it...

We will convert our shapefile to GeoJSON using a temporary file. We'll then use Python to call the qgisio plugin to upload the data for display online:

  1. First, we need to import all of the relevant Python libraries:

            from PyQt4.QtCore import * 
            from PyQt4.QtGui...