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Building Mapping Applications with QGIS

By : Westra

4.6 (9)

Building Mapping Applications with QGIS

4.6 (9)

By: Westra

Overview of this book

If you are an experienced Python developer who wants to create your own geospatial applications with minimum fuss, this is the book for you. While some familiarity with mapping applications would be an advantage, no prior knowledge of geospatial concepts is required. Even if you've never used QGIS before, this book will quickly get you up to speed.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Getting Started with QGIS

1. Getting Started with QGIS

About QGIS

Linking QGIS and Python

Summary

2. The QGIS Python Console

2. The QGIS Python Console

Using the console

Working with geospatial data in the console

Scripting the QGIS user interface

Summary

3. Learning the QGIS Python API

3. Learning the QGIS Python API

About the QGIS Python APIs

Deciphering the C++ documentation

Organizing the QGIS Python libraries

Using the PyQGIS library

Summary

4. Creating QGIS Plugins

4. Creating QGIS Plugins

Getting ready

Understanding the QGIS plugin architecture

Creating a simple plugin

The plugin development process

Writing a useful plugin

Possibilities and limitations of plugins

Summary

5. Using QGIS in an External Application

5. Using QGIS in an External Application

Introducing Lex

Getting the data

Designing the application

Creating the application's framework

Adding the user interface

Connecting the actions

Creating the map canvas

Labeling the points

Filtering the landmarks

Implementing the zoom tool

Implementing the pan tool

Implementing the explore mode

Further improvements and enhancements

Summary

6. Mastering the QGIS Python API

6. Mastering the QGIS Python API

Working with symbol layers

Combining symbol layers

Implementing symbol layers in Python

Implementing renderers in Python

Working with custom map layers

Creating custom map canvas items

Using memory-based layers

Summary

7. Selecting and Editing Features in a PyQGIS Application

7. Selecting and Editing Features in a PyQGIS Application

Working with selections

Using the layer editing mode

Adding Points

Editing Points

Deleting Points and other features

Adding lines and polygons

Editing lines and polygons

Summary

8. Building a Complete Mapping Application using Python and QGIS

8. Building a Complete Mapping Application using Python and QGIS

Introducing ForestTrails

Designing the ForestTrails application

Creating the application

Obtaining the basemap

Defining the map layers

Defining the map renderers

The Pan Tool

Implementing the track editing mode

Summary

9. Completing the ForestTrails Application

9. Completing the ForestTrails Application

The Add Track map tool

Testing the application

Vertex snapping

The Edit Track map tool

The Delete Track map tool

The Get Info map tool

The Set Start Point and Set End Point actions

The Find Shortest Path action

Adjusting the toolbar actions

Suggested improvements

Summary

Index

Index

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Getting the data

Lex will make use of two map layers: a basemap layer that displays a shaded-relief raster image, and a landmark layer that shows the individual landmarks based on a set of place names. Both of these datasets can be downloaded from the Natural Earth Data website. Visit http://www.naturalearthdata.com and click on the Get the Data link to jump to the Downloads page.

The basemap data can be found by clicking on the Raster link. We'll want the highest-resolution data available, so use the link in the Large scale data, 1:10m section.

While you could use any of these datasets as a basemap, we will download the Natural Earth I with Shaded Relief, Water, and Drainages dataset. Make sure you download the high-resolution version of this dataset so that the raster image will still look good when the user has zoomed in.

For the landmarks, we'll be using the "populated places" dataset. Go back to the main downloads page and click on the Cultural link in the Large scale data, 1:10m section...

Visually different images

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