Book Image

QGIS 2 Cookbook

By : Alex Mandel, Víctor Olaya Ferrero, Anita Graser, Alexander Bruy
Book Image

QGIS 2 Cookbook

By: Alex Mandel, Víctor Olaya Ferrero, Anita Graser, Alexander Bruy

Overview of this book

QGIS is a user-friendly, cross-platform desktop geographic information system used to make maps and analyze spatial data. QGIS allows users to understand, question, interpret, and visualize spatial data in many ways that reveal relationships, patterns, and trends in the form of maps. This book is a collection of simple to advanced techniques that are needed in everyday geospatial work, and shows how to accomplish them with QGIS. You will begin by understanding the different types of data management techniques, as well as how data exploration works. You will then learn how to perform classic vector and raster analysis with QGIS, apart from creating time-based visualizations. Finally, you will learn how to create interactive and visually appealing maps with custom cartography. By the end of this book, you will have all the necessary knowledge to handle spatial data management, exploration, and visualization tasks in QGIS.

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QGIS 2 Cookbook
Alex Mandel | Víctor Olaya Ferrero | Anita Graser | Alexander Bruy
Apr, 2016 | 390 pages
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Table of Contents (19 chapters)
QGIS 2 Cookbook
QGIS 2 Cookbook
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Data Input and Output
Data Input and Output
Introduction
Finding geospatial data on your computer
Describing data sources
Importing data from text files
Importing KML/KMZ files
Importing DXF/DWG files
Opening a NetCDF file
Saving a vector layer
Saving a raster layer
Reprojecting a layer
Batch format conversion
Batch reprojection
Loading vector layers into SpatiaLite
Loading vector layers into PostGIS
2
Data Management
Data Management
Introduction
Joining layer data
Cleaning up the attribute table
Configuring relations
Joining tables in databases
Creating views in SpatiaLite
Creating views in PostGIS
Creating spatial indexes
Georeferencing rasters
Georeferencing vector layers
Creating raster overviews (pyramids)
Building virtual rasters (catalogs)
3
Common Data Preprocessing Steps
Common Data Preprocessing Steps
Introduction
Converting points to lines to polygons and back – QGIS
Converting points to lines to polygons and back – SpatiaLite
Converting points to lines to polygons and back – PostGIS
Cropping rasters
Clipping vectors
Extracting vectors
Converting rasters to vectors
Converting vectors to rasters
Building DateTime strings
Geotagging photos
4
Data Exploration
Data Exploration
Introduction
Listing unique values in a column
Exploring numeric value distribution in a column
Exploring spatiotemporal vector data using Time Manager
Creating animations using Time Manager
Designing time-dependent styles
Loading BaseMaps with the QuickMapServices plugin
Loading BaseMaps with the OpenLayers plugin
Viewing geotagged photos
5
Classic Vector Analysis
Classic Vector Analysis
Introduction
Selecting optimum sites
Dasymetric mapping
Calculating regional statistics
Estimating density heatmaps
Estimating values based on samples
6
Network Analysis
Network Analysis
Introduction
Creating a simple routing network
Calculating the shortest paths using the Road graph plugin
Routing with one-way streets in the Road graph plugin
Calculating the shortest paths with the QGIS network analysis library
Routing point sequences
Automating multiple route computation using batch processing
Matching points to the nearest line
Creating a routing network for pgRouting
Visualizing the pgRouting results in QGIS
Using the pgRoutingLayer plugin for convenience
Getting network data from the OSM
7
Raster Analysis I
Raster Analysis I
Introduction
Using the raster calculator
Preparing elevation data
Calculating a slope
Calculating a hillshade layer
Analyzing hydrology
Calculating a topographic index
Automating analysis tasks using the graphical modeler
8
Raster Analysis II
Raster Analysis II
Introduction
Calculating NDVI
Handling null values
Setting extents with masks
Sampling a raster layer
Visualizing multispectral layers
Modifying and reclassifying values in raster layers
Performing supervised classification of raster layers
9
QGIS and the Web
QGIS and the Web
Introduction
Using web services
Using WFS and WFS-T
Searching CSW
Using WMS and WMS Tiles
Using WCS
Using GDAL
Serving web maps with the QGIS server
Scale-dependent rendering
Hooking up web clients
Managing GeoServer from QGIS
10
Cartography Tips
Cartography Tips
Introduction
Using Rule Based Rendering
Handling transparencies
Understanding the feature and layer blending modes
Saving and loading styles
Configuring data-defined labels
Creating custom SVG graphics
Making pretty graticules in any projection
Making useful graticules in printed maps
Creating a map series using Atlas
11
Extending QGIS
Extending QGIS
Introduction
Defining custom projections
Working near the dateline
Working offline
Using the QspatiaLite plugin
Adding plugins with Python dependencies
Using the Python console
Writing Processing algorithms
Writing QGIS plugins
Using external tools
12
Up and Coming
Up and Coming
Introduction
Preparing LiDAR data
Opening File Geodatabases with the OpenFileGDB driver
Using Geopackages
The PostGIS Topology Editor plugin
The Topology Checker plugin
GRASS Topology tools
Hunting for bugs
Reporting bugs
Index
Index

Selecting optimum sites

Optimum site selection is a pretty common problem, for example, when planning shop or warehouse locations or when looking for a new apartment. In this recipe, you will learn how to perform optimum site selection manually using tools from the Processing Toolbox option, but you will also see how to automate this workflow by creating a Processing model.

In the optimum site selection in this recipe, we will combine different vector analysis tools to find potential locations in Wake County that match the following criteria:

  • Locations are near a big lake (up to 500 m)

  • Locations are close to an elementary school (up to 500 m)

  • Locations are within a reasonable distance (up to 2 km) from a high school

  • Locations are at least 1 km from a main road

Getting ready

To follow this exercise, load the following datasets, lakes.shp, schools_wake.shp, and roadsmajor.shp.

As all datasets in our test data already use the same CRS, we can get right to the analysis. If you are using different data...

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