21. Answer Sheet¶
21.1. Results For Adding Your First Layer¶
21.2. Results For An Overview of the Interface¶
21.2.1. Overview (Part 1)¶
Refer back to the image showing the interface layout and check that you remember the names and functions of the screen elements.
21.3. Results For Working with Vector Data¶
21.3.1. Attribute data¶
There should be 9 fields in the rivers layer:
Select the layer in the Layers panel.
Count the number of columns.
A quicker approach could be to double-click the rivers layer, open the tab, where you will find a numbered list of the table’s fields.
Information about towns is available in the places layer. Open its attribute table as you did with the rivers layer: there are two features whose place attribute is set to
town: Swellendam and Buffeljagsrivier. You can add comment on other fields from these two records, if you like.
21.4. Results For Symbology¶
Verify that the colors are changing as you expect them to change.
If you want to work on only one layer at a time and don’t want the other layers to distract you, you can hide a layer by clicking in the checkbox next to its name in the layers list. If the box is blank, then the layer is hidden.
21.4.2. Symbol Structure¶
Your map should now look like this:
If you are a Beginner-level user, you may stop here.
Use the method above to change the colors and styles for all the remaining layers.
Try using natural colors for the objects. For example, a road should not be red or blue, but can be gray or black.
Also feel free to experiment with different Fill style and Stroke style settings for the polygons.
21.4.3. Symbol Layers¶
Customize your buildings layer as you like, but remember that it has to be easy to tell different layers apart on the map.
Here’s an example:
21.4.4. Symbol Levels¶
To make the required symbol, you need three symbol layers:
The lowest symbol layer is a broad, solid gray line. On top of it there is a slightly thinner solid yellow line and finally another thinner solid black line.
If your symbol layers resemble the above but you’re not getting the result you want:
Check that your symbol levels look something like this:
Now your map should look like this:
21.5. Outline Markers¶
Here are examples of the symbol structure:
21.5.1. Geometry generator symbology¶
Choose a good color to fill the water polygons.
Click on Marker of the Geometry generator symbology and change the circle with another shape as your wish.
Try experimenting other options to get more useful results.
21.6. Results For Attribute Data¶
21.7. Results For The Label Tool¶
21.7.1. Label Customization (Part 1)¶
Your map should now show the marker points and the labels should be offset by 2.0 mm: The style of the markers and labels should allow both to be clearly visible on the map:
21.7.2. Label Customization (Part 2)¶
One possible solution has this final product:
To arrive at this result:
Use a font size of 10, a Label distance of 1,5 mm, Symbol width and Symbol size of 3.0 mm.
In addition, this example uses the Wrap label on character option:
Enter a space in this field and click Apply to achieve the same effect. In our case, some of the place names are very long, resulting in names with multiple lines which is not very user friendly. You might find this setting to be more appropriate for your map.
21.7.3. Using Data Defined Settings¶
Still in edit mode, set the FONT_SIZE values to whatever you prefer. The example uses 16 for towns, 14 for suburbs, 12 for localities and 10 for hamlets.
Remember to save changes and exit edit mode.
Return to the Text formatting options for the places layer and select FONT_SIZE in the Attribute field of the font size data override dropdown:
Your results, if using the above values, should be this:
21.8. Results For Classification¶
21.8.1. Refine the Classification¶
Use the same method as in the first exercise of the lesson to get rid of the borders:
The settings you used might not be the same, but with the values Classes = 6 and Mode = Natural Breaks (Jenks) (and using the same colors, of course), the map will look like this:
21.9. Results For Creating a New Vector Dataset¶
The symbology doesn’t matter, but the results should look more or less like this:
21.9.2. Topology: Add Ring Tool¶
The exact shape doesn’t matter, but you should be getting a hole in the middle of your feature, like this one:
Undo your edit before continuing with the exercise for the next tool.
21.9.3. Topology: Add Part Tool¶
First select the Bontebok National Park:
Now add your new part:
Undo your edit before continuing with the exercise for the next tool.
21.9.4. Merge Features¶
Use the Merge Selected Features tool, making sure to first select both of the polygons you wish to merge.
Use the feature with the OGC_FID of 1 as the source of your attributes (click on its entry in the dialog, then click the Take attributes from selected feature button):
- If you’re using a different dataset, it is highly likely that your
original polygon’s OGC_FID will not be 1. Just choose the feature which has an OGC_FID.
Using the Merge Attributes of Selected Features tool will keep the geometries distinct, but give them the same attributes.
For the TYPE, there is obviously a limited amount of types that a road can be, and if you check the attribute table for this layer, you’ll see that they are predefined.
Set the widget to Value Map and click Load Data from Layer.
Select roads in the Label dropdown and highway for both the Value and Description options:
Click Ok three times.
If you use the Identify tool on a street now while edit mode is active, the dialog you get should look like this:
21.10. Results For Vector Analysis¶
21.10.1. Distance from High Schools¶
Your buffer dialog should look like this:
The Buffer distance is 1 kilometer.
The Segments to approximate value is set to 20. This is optional, but it’s recommended, because it makes the output buffers look smoother. Compare this:
The first image shows the buffer with the Segments to approximate value set to 5 and the second shows the value set to 20. In our example, the difference is subtle, but you can see that the buffer’s edges are smoother with the higher value.
21.10.2. Distance from Restaurants¶
To create the new houses_restaurants_500m layer, we go through a two step process:
First, create a buffer of 500m around the restaurants and add the layer to the map:
Next, extract buildings within that buffer area:
Your map should now show only those buildings which are within 50m of a road, 1km of a school and 500m of a restaurant:
21.11. Results For Network Analysis¶
21.12. Fastest path¶
Openand fill the dialog as:
Make sure that the Path type to calculate is
Click on Run and close the dialog.
Open now the attribute table of the output layer. The cost field contains the travel time between the two points (as fraction of hours):
21.13. Results For Raster Analysis¶
21.14. Results For Completing the Analysis¶
21.14.1. Raster to Vector¶
Open the Query Builder by right-clicking on the all_terrain layer in the Layers panel, and selecting the tab.
Then build the query "suitable" = 1.
Click OK to filter out all the polygons where this condition isn’t met.
When viewed over the original raster, the areas should overlap perfectly:
You can save this layer by right-clicking on the all_terrain layer in the Layers panel and choosing Save As…, then continue as per the instructions.
21.14.2. Inspecting the Results¶
You may notice that some of the buildings in your new_solution layer have been “sliced” by the Intersect tool. This shows that only part of the building - and therefore only part of the property - lies on suitable terrain. We can therefore sensibly eliminate those buildings from our dataset
21.14.3. Refining the Analysis¶
At the moment, your analysis should look something like this:
Consider a circular area, continuous for 100 meters in all directions.
If it is greater than 100 meters in radius, then subtracting 100 meters from its size (from all directions) will result in a part of it being left in the middle.
Therefore, you can run an interior buffer of 100 meters on your existing suitable_terrain vector layer. In the output of the buffer function, whatever remains of the original layer will represent areas where there is suitable terrain for 100 meters beyond.
Go toto open the Buffer(s) dialog.
Set it up like this:
Use the suitable_terrain layer with 10 segments and a buffer distance of -100. (The distance is automatically in meters because your map is using a projected CRS.)
Save the output in exercise_data/residential_development/ as suitable_terrain_continuous100m.shp.
If necessary, move the new layer above your original suitable_terrain layer.
Your results will look like something like this:
Now use the Select by Location tool ( ).
Set up like this:
Select features in new_solution that intersect features in suitable_terrain_continuous100m.shp.
This is the result:
The yellow buildings are selected. Although some of the buildings fall partly outside the new suitable_terrain_continuous100m layer, they lie well within the original suitable_terrain layer and therefore meet all of our requirements.
Save the selection under exercise_data/residential_development/ as final_answer.shp.
21.15. Results For WMS¶
21.15.1. Adding Another WMS Layer¶
Your map should look like this (you may need to re-order the layers):
21.15.2. Adding a New WMS Server¶
Use the same approach as before to add the new server and the appropriate layer as hosted on that server:
If you zoom into the Swellendam area, you’ll notice that this dataset has a low resolution:
Therefore, it’s better not to use this data for the current map. The Blue Marble data is more suitable at global or national scales.
21.15.3. Finding a WMS Server¶
You may notice that many WMS servers are not always available. Sometimes this is temporary, sometimes it is permanent. An example of a WMS server that worked at the time of writing is the World Mineral Deposits WMS at http://apps1.gdr.nrcan.gc.ca/cgi-bin/worldmin_en-ca_ows. It does not require fees or have access constraints, and it is global. Therefore, it does satisfy the requirements. Keep in mind, however, that this is merely an example. There are many other WMS servers to choose from.
21.16. Results For GRASS Integration¶
21.16.1. Add Layers to Mapset¶
You can add layers (both vector and raster) into a GRASS Mapset by drag and drop
them in the Browser (see Follow Along: Load data using the QGIS Browser) or by using the
for vector and
r.in.gdal.qgis for raster layers.
21.16.2. Reclassify raster layer¶
To discover the maximum value of the raster run the r.info tool: in the console you will see that the maximum value is 1699.
You are now ready to write the rules. Open a text editor and add the following rules:
0 thru 1000 = 1 1000 thru 1400 = 2 1400 thru 1699 = 3
save the file as a
my_rules.txt file and close the text editor.
Run the r.reclass tool, choose the g_dem layer and load the file containing the rules you just have saved.
Click on Run and then on View Output. You can change the colors and the final result should look like the following picture:
21.17. Results For Database Concepts¶
21.17.1. Address Table Properties¶
For our theoretical address table, we might want to store the following properties:
House Number Street Name Suburb Name City Name Postcode Country
When creating the table to represent an address object, we would create columns to represent each of these properties and we would name them with SQL-compliant and possibly shortened names:
house_number street_name suburb city postcode country
21.17.2. Normalising the People Table¶
The major problem with the people table is that there is a single address field which contains a person’s entire address. Thinking about our theoretical address table earlier in this lesson, we know that an address is made up of many different properties. By storing all these properties in one field, we make it much harder to update and query our data. We therefore need to split the address field into the various properties. This would give us a table which has the following structure:
id | name | house_no | street_name | city | phone_no --+---------------+----------+----------------+------------+----------------- 1 | Tim Sutton | 3 | Buirski Plein | Swellendam | 071 123 123 2 | Horst Duester | 4 | Avenue du Roix | Geneva | 072 121 122
In the next section, you will learn about Foreign Key relationships which could be used in this example to further improve our database’s structure.
21.17.3. Further Normalisation of the People Table¶
Our people table currently looks like this:
id | name | house_no | street_id | phone_no ---+--------------+----------+-----------+------------- 1 | Horst Duster | 4 | 1 | 072 121 122
The street_id column represents a ‘one to many’ relationship between the people object and the related street object, which is in the streets table.
One way to further normalise the table is to split the name field into first_name and last_name:
id | first_name | last_name | house_no | street_id | phone_no ---+------------+------------+----------+-----------+------------ 1 | Horst | Duster | 4 | 1 | 072 121 122
We can also create separate tables for the town or city name and country, linking them to our people table via ‘one to many’ relationships:
id | first_name | last_name | house_no | street_id | town_id | country_id ---+------------+-----------+----------+-----------+---------+------------ 1 | Horst | Duster | 4 | 1 | 2 | 1
An ER Diagram to represent this would look like this:
21.17.4. Create a People Table¶
The SQL required to create the correct people table is:
create table people (id serial not null primary key, name varchar(50), house_no int not null, street_id int not null, phone_no varchar null );
The schema for the table (enter \d people) looks like this:
Table "public.people" Column | Type | Modifiers -----------+-----------------------+------------------------------------- id | integer | not null default | | nextval('people_id_seq'::regclass) name | character varying(50) | house_no | integer | not null street_id | integer | not null phone_no | character varying | Indexes: "people_pkey" PRIMARY KEY, btree (id)
For illustration purposes, we have purposely omitted the fkey constraint.
21.17.5. The DROP Command¶
The reason the DROP command would not work in this case is because the people table has a Foreign Key constraint to the streets table. This means that dropping (or deleting) the streets table would leave the people table with references to non-existent streets data.
It is possible to ‘force’ the streets table to be deleted by using the CASCADE command, but this would also delete the people and any other table which had a relationship to the streets table. Use with caution!
21.17.6. Insert a New Street¶
The SQL command you should use looks like this (you can replace the street name with a name of your choice):
insert into streets (name) values ('Low Road');
21.17.7. Add a New Person With Foreign Key Relationship¶
Here is the correct SQL statement:
insert into streets (name) values('Main Road'); insert into people (name,house_no, street_id, phone_no) values ('Joe Smith',55,2,'072 882 33 21');
If you look at the streets table again (using a select statement as before), you’ll see that the id for the Main Road entry is 2.
That’s why we could merely enter the number 2 above. Even though we’re not seeing Main Road written out fully in the entry above, the database will be able to associate that with the street_id value of 2.
If you have already added a new street object, you might find that the new Main Road has an ID of 3 not 2.
21.17.8. Return Street Names¶
Here is the correct SQL statement you should use:
select count(people.name), streets.name from people, streets where people.street_id=streets.id group by streets.name;
count | name ------+------------- 1 | Low Street 2 | High street 1 | Main Road (3 rows)
You will notice that we have prefixed field names with table names (e.g. people.name and streets.name). This needs to be done whenever the field name is ambiguous (i.e. not unique across all tables in the database).
21.18. Results For Spatial Queries¶
21.18.1. The Units Used in Spatial Queries¶
The units being used by the example query are degrees, because the CRS that the layer is using is WGS 84. This is a Geographic CRS, which means that its units are in degrees. A Projected CRS, like the UTM projections, is in meters.
Remember that when you write a query, you need to know which units the layer’s CRS is in. This will allow you to write a query that will return the results that you expect.
21.19. Results For Geometry Construction¶
21.19.1. Creating Linestrings¶
alter table streets add column the_geom geometry; alter table streets add constraint streets_geom_point_chk check (st_geometrytype(the_geom) = 'ST_LineString'::text OR the_geom IS NULL); insert into geometry_columns values ('','public','streets','the_geom',2,4326, 'LINESTRING'); create index streets_geo_idx on streets using gist (the_geom);
21.20. Results For Simple Feature Model¶
21.20.1. Populating Tables¶
create table cities (id serial not null primary key, name varchar(50), the_geom geometry not null); alter table cities add constraint cities_geom_point_chk check (st_geometrytype(the_geom) = 'ST_Polygon'::text );
21.20.2. Populate the Geometry_Columns Table¶
insert into geometry_columns values ('','public','cities','the_geom',2,4326,'POLYGON');
21.20.3. Adding Geometry¶
select people.name, streets.name as street_name, st_astext(people.the_geom) as geometry from streets, people where people.street_id=streets.id;
name | street_name | geometry --------------+-------------+--------------- Roger Jones | High street | Sally Norman | High street | Jane Smith | Main Road | Joe Bloggs | Low Street | Fault Towers | Main Road | POINT(33 -33) (5 rows)
As you can see, our constraint allows nulls to be added into the database.