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
Null values are a particular type of values that are used to indicate cells where the value for a given layer is not defined. Understanding how to use them is important to avoid wrong results when performing analyses but also to use them as a tool to get better and more correct results. This recipe explains some of the fundamental ideas about null values in raster layers.
The watershed.tif layer contains the area of a watershed. Cells inside the watershed are cells from which water will eventually flow into the outlet point of the watershed. The remaining cells belong to a different watershed. To mask the DEM with the watershed mask, follow these steps:
- Open the
watershed.tiflayer. - Open the identify tool and check whether the cells that belong to the watershed have a value of 1, and the ones outside, have a value of
no data. - Try clicking inside and outside the watershed; in your Identify Results dialog, you will see the results, as shown in the following screenshot:
- Now, let's calculate some statistics of the raster layer. Open the Raster layer statistics algorithm in the Processing Toolbox menu.
- Select the watershed layer in the input layer field and click on OK to run the algorithm. The result is a short text output that looks like the following screenshot:
Only the cells with a value of 1 have been considered, and the average value in the layer is equal to 1.
The layer has 610 columns and 401 rows, but the total number of valid cells is much lower than 610 x 410. These are the cells that have been used to compute the statistics.
Raster layers always cover a rectangular region. However, in some circumstances, the land object that the layer represents might not be rectangular. This might be due to a purely geophysical reason (imagine a layer with water temperature that contains non-water cells), political ones (a layer with a DEM of a given country with no data available for a neighboring country), or many others. In any case, a value is needed for these cells to indicate that no data is available. An arbitrary value is selected and used. As such, this is usually a value that is not a logical and/or feasible value for the variable that is stored in the layer.
In the case of the example layer, the value used is -99999, which is the default value set for no-data values. This means that, when the identify tool shows no data, it has actually selected a value of -99999 in this case.
Algorithms in the Processing framework systematically ignore no-data cells, and do not use their values. You can clearly see this in the preceding example. A large part of the cells in the layer have a value of 1 (the ones that belong to the watershed), but many of them have a value of -99999. The average value of the cells should then be different from 1, but as -99999 is defined as the no-data value, all cells with this value are ignored. The average of the layer is, therefore, equal to 1.
Null values should be considered not only when performing an analysis, but also when we just want to render a layer that contains them.
Null values are also considered separately when rendering a raster layer. You can choose to select them using a given color (as set by the current color palette), or to not render them at all. To make all cells with null values transparent, open the layer properties and go to the Transparency section. Make sure that the No data value checkbox is checked, as shown in the following screenshot:
