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QGIS 2 Cookbook

QGIS 2 Cookbook

By : Mandel, Olaya Ferrero, Anita Graser
5 (1)
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QGIS 2 Cookbook

QGIS 2 Cookbook

5 (1)
By: Mandel, Olaya Ferrero, Anita Graser

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.
Table of Contents (14 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Sections
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Data Input and Output
1. 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
2. Data Management
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2. 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
3. Common Data Preprocessing Steps
3. 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
4. Data Exploration
4. 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
5. Classic Vector Analysis
5. Classic Vector Analysis
Introduction
Selecting optimum sites
Dasymetric mapping
Calculating regional statistics
Estimating density heatmaps
Estimating values based on samples
6
6. Network Analysis
6. 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
7. Raster Analysis I
7. 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
8. Raster Analysis II
8. 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
9. QGIS and the Web
9. 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
10. Cartography Tips
10. 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
11. Extending QGIS
11. 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
12. Up and Coming
12. 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
13
Index
Index

Configuring relations

In the Joining layer data recipe, we discussed that joins only append additional columns to existing features (1:1 or n:1 relationships). Using joins, it is, therefore, not possible to model 1:n relationships, such as "one zip code area containing n schools". These kinds of relationships can instead be modeled using relations. This recipe introduces the concept of relations and shows how you can put them to use.

Getting ready

To follow this exercise, load zip code areas and schools from zipcodes_wake.shp and schools_wake.shp.

How to do it…

Relations are configured in Project Properties. The dialog is very similar to the join dialog:

  1. Define the two layers (Referencing/Child and Referenced/Parent), as well as the fields containing the common values/IDs. As you want to model "one zip code area contains n schools," the zip code dataset is the parent layer and the school dataset is the child layer. The connection between both datasets is established...
End of Section 5
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