Table of Contents for
QGIS: Becoming a GIS Power User

Version ebook / Retour

Cover image for bash Cookbook, 2nd Edition QGIS: Becoming a GIS Power User by Alexander Bruy Published by Packt Publishing, 2017
  1. Cover
  2. Table of Contents
  3. QGIS: Becoming a GIS Power User
  4. QGIS: Becoming a GIS Power User
  5. QGIS: Becoming a GIS Power User
  6. Credits
  7. Preface
  8. What you need for this learning path
  9. Who this learning path is for
  10. Reader feedback
  11. Customer support
  12. 1. Module 1
  13. 1. Getting Started with QGIS
  14. Running QGIS for the first time
  15. Introducing the QGIS user interface
  16. Finding help and reporting issues
  17. Summary
  18. 2. Viewing Spatial Data
  19. Dealing with coordinate reference systems
  20. Loading raster files
  21. Loading data from databases
  22. Loading data from OGC web services
  23. Styling raster layers
  24. Styling vector layers
  25. Loading background maps
  26. Dealing with project files
  27. Summary
  28. 3. Data Creation and Editing
  29. Working with feature selection tools
  30. Editing vector geometries
  31. Using measuring tools
  32. Editing attributes
  33. Reprojecting and converting vector and raster data
  34. Joining tabular data
  35. Using temporary scratch layers
  36. Checking for topological errors and fixing them
  37. Adding data to spatial databases
  38. Summary
  39. 4. Spatial Analysis
  40. Combining raster and vector data
  41. Vector and raster analysis with Processing
  42. Leveraging the power of spatial databases
  43. Summary
  44. 5. Creating Great Maps
  45. Labeling
  46. Designing print maps
  47. Presenting your maps online
  48. Summary
  49. 6. Extending QGIS with Python
  50. Getting to know the Python Console
  51. Creating custom geoprocessing scripts using Python
  52. Developing your first plugin
  53. Summary
  54. 2. Module 2
  55. 1. Exploring Places – from Concept to Interface
  56. Acquiring data for geospatial applications
  57. Visualizing GIS data
  58. The basemap
  59. Summary
  60. 2. Identifying the Best Places
  61. Raster analysis
  62. Publishing the results as a web application
  63. Summary
  64. 3. Discovering Physical Relationships
  65. Spatial join for a performant operational layer interaction
  66. The CartoDB platform
  67. Leaflet and an external API: CartoDB SQL
  68. Summary
  69. 4. Finding the Best Way to Get There
  70. OpenStreetMap data for topology
  71. Database importing and topological relationships
  72. Creating the travel time isochron polygons
  73. Generating the shortest paths for all students
  74. Web applications – creating safe corridors
  75. Summary
  76. 5. Demonstrating Change
  77. TopoJSON
  78. The D3 data visualization library
  79. Summary
  80. 6. Estimating Unknown Values
  81. Interpolated model values
  82. A dynamic web application – OpenLayers AJAX with Python and SpatiaLite
  83. Summary
  84. 7. Mapping for Enterprises and Communities
  85. The cartographic rendering of geospatial data – MBTiles and UTFGrid
  86. Interacting with Mapbox services
  87. Putting it all together
  88. Going further – local MBTiles hosting with TileStream
  89. Summary
  90. 3. Module 3
  91. 1. Data Input and Output
  92. Finding geospatial data on your computer
  93. Describing data sources
  94. Importing data from text files
  95. Importing KML/KMZ files
  96. Importing DXF/DWG files
  97. Opening a NetCDF file
  98. Saving a vector layer
  99. Saving a raster layer
  100. Reprojecting a layer
  101. Batch format conversion
  102. Batch reprojection
  103. Loading vector layers into SpatiaLite
  104. Loading vector layers into PostGIS
  105. 2. Data Management
  106. Joining layer data
  107. Cleaning up the attribute table
  108. Configuring relations
  109. Joining tables in databases
  110. Creating views in SpatiaLite
  111. Creating views in PostGIS
  112. Creating spatial indexes
  113. Georeferencing rasters
  114. Georeferencing vector layers
  115. Creating raster overviews (pyramids)
  116. Building virtual rasters (catalogs)
  117. 3. Common Data Preprocessing Steps
  118. Converting points to lines to polygons and back – QGIS
  119. Converting points to lines to polygons and back – SpatiaLite
  120. Converting points to lines to polygons and back – PostGIS
  121. Cropping rasters
  122. Clipping vectors
  123. Extracting vectors
  124. Converting rasters to vectors
  125. Converting vectors to rasters
  126. Building DateTime strings
  127. Geotagging photos
  128. 4. Data Exploration
  129. Listing unique values in a column
  130. Exploring numeric value distribution in a column
  131. Exploring spatiotemporal vector data using Time Manager
  132. Creating animations using Time Manager
  133. Designing time-dependent styles
  134. Loading BaseMaps with the QuickMapServices plugin
  135. Loading BaseMaps with the OpenLayers plugin
  136. Viewing geotagged photos
  137. 5. Classic Vector Analysis
  138. Selecting optimum sites
  139. Dasymetric mapping
  140. Calculating regional statistics
  141. Estimating density heatmaps
  142. Estimating values based on samples
  143. 6. Network Analysis
  144. Creating a simple routing network
  145. Calculating the shortest paths using the Road graph plugin
  146. Routing with one-way streets in the Road graph plugin
  147. Calculating the shortest paths with the QGIS network analysis library
  148. Routing point sequences
  149. Automating multiple route computation using batch processing
  150. Matching points to the nearest line
  151. Creating a routing network for pgRouting
  152. Visualizing the pgRouting results in QGIS
  153. Using the pgRoutingLayer plugin for convenience
  154. Getting network data from the OSM
  155. 7. Raster Analysis I
  156. Using the raster calculator
  157. Preparing elevation data
  158. Calculating a slope
  159. Calculating a hillshade layer
  160. Analyzing hydrology
  161. Calculating a topographic index
  162. Automating analysis tasks using the graphical modeler
  163. 8. Raster Analysis II
  164. Calculating NDVI
  165. Handling null values
  166. Setting extents with masks
  167. Sampling a raster layer
  168. Visualizing multispectral layers
  169. Modifying and reclassifying values in raster layers
  170. Performing supervised classification of raster layers
  171. 9. QGIS and the Web
  172. Using web services
  173. Using WFS and WFS-T
  174. Searching CSW
  175. Using WMS and WMS Tiles
  176. Using WCS
  177. Using GDAL
  178. Serving web maps with the QGIS server
  179. Scale-dependent rendering
  180. Hooking up web clients
  181. Managing GeoServer from QGIS
  182. 10. Cartography Tips
  183. Using Rule Based Rendering
  184. Handling transparencies
  185. Understanding the feature and layer blending modes
  186. Saving and loading styles
  187. Configuring data-defined labels
  188. Creating custom SVG graphics
  189. Making pretty graticules in any projection
  190. Making useful graticules in printed maps
  191. Creating a map series using Atlas
  192. 11. Extending QGIS
  193. Defining custom projections
  194. Working near the dateline
  195. Working offline
  196. Using the QspatiaLite plugin
  197. Adding plugins with Python dependencies
  198. Using the Python console
  199. Writing Processing algorithms
  200. Writing QGIS plugins
  201. Using external tools
  202. 12. Up and Coming
  203. Preparing LiDAR data
  204. Opening File Geodatabases with the OpenFileGDB driver
  205. Using Geopackages
  206. The PostGIS Topology Editor plugin
  207. The Topology Checker plugin
  208. GRASS Topology tools
  209. Hunting for bugs
  210. Reporting bugs
  211. Bibliography
  212. Index

Using the Python console

QGIS has a built-in Python console, where you can enter commands in the Python programming language and get results. This is very useful for quick data processing.

Getting ready

To follow this recipe, you should be familiar with the Python programming language. You can find a small but detailed tutorial in the official Python documentation at https://docs.python.org/2.7/tutorial/index.html.

Also load the poi_names_wake.shp file from the sample data.

How to do it…

QGIS Python console can be opened by clicking on the Python Console button at toolbar or by navigating to Plugins | Python Console. The console opens as a non-modal floating window, as shown in the following screenshot:

How to do it…

Let's take a look at how to perform some data exploration with the QGIS Python console:

  1. First, it is necessary to get a reference to the active (selected in the layers tree) layer and store it in the variable for further use by running this command:
    layer = iface.activeLayer()
  2. After acquiring a reference to the layer, we can examine some of its properties. For example, to get the number of features in the layer, execute the following command:
    layer.featureCount()

    Tip

    At any time, you can use the dir() function to list all the available methods of the object. Try to execute dir(layer) or dir(QgsFeature).

  3. You can also loop over layer features and print their attributes using the following code snippet:
    for f in layer.getFeatures():
  print f["featurenam"], f["elev_m"]

Tip

Note that you need to press Enter twice after entering this code to exit the loop definition and start executing commands.

You can also use the Python console for more complex tasks, such as exporting features with some attributes to a text file. Here is how to do this:

  1. Open the Python Console editor using the Show editor button on the left-hand side of the Python console.
  2. Paste the following code into the editor (make sure to change path to file according to your system):
    import csv
layer = iface.activeLayer()
with open('c:\\temp\\export.csv', 'wb') as outputFile:
  writer = csv.writer(outputFile)
  for feature in layer.getFeatures():
    geom = feature.geometry().exportToWkt()
    writer.writerow([geom, feature["featurenam"], feature['elev_m"]])
  3. If you are using your own vector layer instead of poi_names_wake.shp, which is provided with this book, adjust attribute names in line 8.
  4. Change the file paths for the result file in line 4 depending on your operating system.
  5. Save the script and run it. Don't forget to select the vector layer in the QGIS layer tree before running the script.

How it works…

In line 1, we imported the csv module from the standard Python library. This module provides a convenient way to read and write comma-separated files. In line 3, we obtained a reference to the currently selected layer, which will be used later to access layer features.

In line 3, an output file opened. Note that here we use the with statement so that later there is no need to close the file explicitly, context manager will do this work for us. In line 5, we set up the so-called writer—an object that will write data to the CSV file using specified format settings.

In line 6, we started iterating over features of the active layer. For each feature, we extracted its geometry and converted it into a Well-Known Text (WKT) format (line 7). We then wrote this text representation of the feature geometry with some attributes to the output file (line 8).

It is necessary to mention that our script is very simple and will work only with attributes that have ASCII encoding. To handle non-Latin characters, it is necessary to convert the output data to the unicode before writing it to file.

There's more…

Using the Python console, you also can invoke Processing algorithms to create complex scripts for automated analysis and/or data preparation.

To make the Python console even more useful, take a look at the Script Runner plugin. Detailed information about this plugin with some usage examples can be found at http://spatialgalaxy.net/2012/01/29/script-runner-a-plugin-to-run-python-scripts-in-qgis/.

See also