The sample data provided with the Training Manual refers to the town of Swellendam and its surroundings. Swellendam is located about 2 hours’ east of Cape Town in the Western Cape of South Africa. The dataset contains feature names in both English and Afrikaans.
Anyone can use this dataset without difficulty, but you may prefer to use data from your own country or home town. If you choose to do so, your localised data will be used in all lessons from Module 3 to Module 7.2. Later modules use more complex data sources which may or may not be available for your region.
Muista
This process is intended for course conveners, or more experienced QGIS users who wish to create localised sample data sets for their course. Default data sets are provided with the Training Manual, but you may follow these instructions if you wish to replace the default data sets.
Muista
The sample data used throughout the manual can be downloaded here: http://qgis.org/downloads/data/training_manual_exercise_data.zip
Muista
These instructions assume you have a good knowledge of QGIS and are not intended to be used as teaching material.
If you wish to replace the default data set with localised data for your course, this can easily be done with tools built into QGIS. The region you choose to use should have a good mix of urban and rural areas, containing roads of differing significance, area boundaries (such as nature reserves or farms) and surface water, such as streams and rivers.
This will load four layers into your map which relate to OSM’s naming conventions (you may need to zoom in/out to see the vector data).
We need to extract the useful data from these layers, rename them and create corresponding shape files:
This layer contains three fields whose data we will need to extract for use throughout the Training Manual:
You can sample the data your region contains in order to see what kind of results your region will yield. If you find that “landuse” returns no results, then feel free to exclude it.
You’ll need to write filter expressions for each field to extract the data we need. We’ll use the “building” field as an example here:
We now need to save the resulting data as a shapefile for you to use during your course:
Once the buildings layer has been added to the map, you can repeat the process for the natural and landuse fields using the following expressions:
Muista
Make sure you clear the previous filter (via the Layer properties dialog) from the multipolygons layer before proceeding with the next filter expression!
Each resulting data set should be saved in the “epsg4326” directory in your new exercise_data directory (i.e. “water”, “landuse”).
You should then extract and save the following fields from the lines and points layers to their corresponding directories:
Once you have finished extracting the above data, you can remove the multipolygons, lines and points layers.
You should now have a map which looks something like this (the symbology will certainly be very different, but that is fine):
The important thing is that you have 6 layers matching those shown above and that all those layers have some data.
The last step is to create a spatiallite file from the landuse layer for use during the course:
For Module 6 (Creating Vector Data) and Module 8 (Rasters), you’ll also need raster images (SRTM DEM) which cover the region you have selected for your course.
SRTM DEM can be downloaded from the CGIAR-CGI: http://srtm.csi.cgiar.org/
You’ll need images which cover the entire region you have chosen to use.
Once you have downloaded the required file(s), they should be saved in the “exercise_data” directory under “raster/SRTM/”.
In Module 6, Lesson 1.2 shows close-up images of three school sports fields which students are asked to digitize. You’ll therefore need to reproduce these images using your new SRTM DEM tiff file(s). There is no obligation to use school sports fields: any three school land-use types can be used (e.g. different school buildings, playgrounds or car parks).
For reference, the images in the example data are:
Having created your localised dataset, the final step is to replace the tokens in the conf.py file so that the appropriate names will appear in your localised version of the Training Manual.
The tokens you need to replace are as follows: