Table of Contents for
QGIS: Becoming a GIS Power User
QGIS: Becoming a GIS Power User
Published by
Packt Publishing, 2017
- Cover
- Table of Contents
- QGIS: Becoming a GIS Power User
- QGIS: Becoming a GIS Power User
- QGIS: Becoming a GIS Power User
- Credits
- Preface
- What you need for this learning path
- Who this learning path is for
- Reader feedback
- Customer support
- 1. Module 1
- 1. Getting Started with QGIS
- Running QGIS for the first time
- Introducing the QGIS user interface
- Finding help and reporting issues
- Summary
- 2. Viewing Spatial Data
- Dealing with coordinate reference systems
- Loading raster files
- Loading data from databases
- Loading data from OGC web services
- Styling raster layers
- Styling vector layers
- Loading background maps
- Dealing with project files
- Summary
- 3. Data Creation and Editing
- Working with feature selection tools
- Editing vector geometries
- Using measuring tools
- Editing attributes
- Reprojecting and converting vector and raster data
- Joining tabular data
- Using temporary scratch layers
- Checking for topological errors and fixing them
- Adding data to spatial databases
- Summary
- 4. Spatial Analysis
- Combining raster and vector data
- Vector and raster analysis with Processing
- Leveraging the power of spatial databases
- Summary
- 5. Creating Great Maps
- Labeling
- Designing print maps
- Presenting your maps online
- Summary
- 6. Extending QGIS with Python
- Getting to know the Python Console
- Creating custom geoprocessing scripts using Python
- Developing your first plugin
- Summary
- 2. Module 2
- 1. Exploring Places – from Concept to Interface
- Acquiring data for geospatial applications
- Visualizing GIS data
- The basemap
- Summary
- 2. Identifying the Best Places
- Raster analysis
- Publishing the results as a web application
- Summary
- 3. Discovering Physical Relationships
- Spatial join for a performant operational layer interaction
- The CartoDB platform
- Leaflet and an external API: CartoDB SQL
- Summary
- 4. Finding the Best Way to Get There
- OpenStreetMap data for topology
- Database importing and topological relationships
- Creating the travel time isochron polygons
- Generating the shortest paths for all students
- Web applications – creating safe corridors
- Summary
- 5. Demonstrating Change
- TopoJSON
- The D3 data visualization library
- Summary
- 6. Estimating Unknown Values
- Interpolated model values
- A dynamic web application – OpenLayers AJAX with Python and SpatiaLite
- Summary
- 7. Mapping for Enterprises and Communities
- The cartographic rendering of geospatial data – MBTiles and UTFGrid
- Interacting with Mapbox services
- Putting it all together
- Going further – local MBTiles hosting with TileStream
- Summary
- 3. Module 3
- 1. Data Input and Output
- 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
- 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
- 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
- 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
- Selecting optimum sites
- Dasymetric mapping
- Calculating regional statistics
- Estimating density heatmaps
- Estimating values based on samples
- 6. Network Analysis
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Bibliography
- Index
A very useful technique to work with raster data is changing their values or grouping them into categories. In this recipe, we will see how to do this.
We will classify the elevation in three groups:
- Lower than 1,000m
- Between 1,000 and 2,000m
- Higher than 2,000m
To do this, follow these steps:
- Open the Change grid values algorithm from the Processing Toolbox menu. Set the Replace condition parameter to Low Value <= Grid Value < High Value.
- Click on the button in the Lookup table parameter and fill the table that will appear, as shown in the following screenshot:
- Run the algorithm. This will create the reclassified layer:
Values for each cell are compared with the range limits in the lookup table, considering the specified comparison criteria. Whenever a value falls into a given range, the class value specified for this range will be used in the output layer.
Other strategies can be used to automate a reclassification, especially when this involves dividing the raster layer values into classes with some constant property. Here, we show two of these cases.
A typical case of reclassification is dividing the total range of values of the layer into a given number of classes. This is similar to slicing it, and if applied on a DEM, such as our example data, this will have a result similar to that of defining contour lines with a regular interval (although the result is a not vector layer with lines in this case, but a new raster layer).
To reclassify in equal intervals, follow these steps:
- Open Raster calculator from the Processing Toolbox menu.
- Select the DEM as the only layer to use.
- Enter the formula, int((a-514)/(2410-514) * 5).
The reclassified layer will look like the following:
The numerical values in the formula correspond to the minimum and maximum values of the layer. You can find these values in the properties window of the layer.
To create a different number of classes, just use another value instead of 5 in the formula.
There is no tool to reclassify into a set of n classes, as each of them occupies the same area, but a similar result can be obtained using some other algorithms. To show you how this is done, let's reclassify the DEM into five classes of the same area:
- Open the Sort grid algorithm and enter the DEM as the input layer. Click on Run to execute the algorithm.
The resulting layer has the cells ordered according to their value in the DEM, so the cell with a value of 1 represents the cell with the lowest elevation value, 2 is the second lowest, and so on.
- Reclassify the ordered layer into five classes of equal amplitude using the procedure described earlier. The final layer should look like the following:
- The Processing Toolbox menu contains additional classification algorithms, most of them based on SAGA. One algorithm that has a different approach is Cluster analysis for grids. This will create a given number of classes in such a way that it minimize the variances in the groups, trying to make them as homogeneous as possible. This is also known as unsupervised classification.