Merge pull request #155 from GIScience/dev

update to links to module 3

_toc.yml

@@ -27,13 +27,13 @@ parts:
           - file: content/Module_2/en_qgis_module_2_exercises.md
   - caption: Module 3
     chapters:
-      - file: content/Modul_3/en_module_3_overview.md
+      - file: content/Module_3/en_module_3_overview.md
         sections:
-          - file: content/Modul_3/en_qgis_digitalisation.md
-          - file: content/Modul_3/en_qgis_data_classification.md
-          - file: content/Modul_3/en_qgis_data_queries.md
-          - file: content/Modul_3/en_qgis_georeferencing.md
-          - file: content/Modul_3/en_qgis_modul_3_exercises.md
+          - file: content/Module_3/en_qgis_digitalisation.md
+          - file: content/Module_3/en_qgis_data_classification.md
+          - file: content/Module_3/en_qgis_data_queries.md
+          - file: content/Module_3/en_qgis_georeferencing.md
+          - file: content/Module_3/en_qgis_module_3_exercises.md
   - caption: Module 4 
     chapters:
       - file: content/Modul_4/en_module_4_overview.md

content/Modul_4/en_qgis_labels_vector.md

@@ -134,7 +134,7 @@ Numerical Labels
 ---
 name: graduated symbology instead numerical values
 ---
-[Graduated Symbology](https://giscience.github.io/gis-training-resource-center/content/Modul_3/en_qgis_data_classification.html#graduated-classification)
+[Graduated Symbology](https://giscience.github.io/gis-training-resource-center/content/Module_3/en_qgis_data_classification.html#graduated-classification)
 :::
 
 ::::

content/Modul_4/en_qgis_map_design_I.md

@@ -186,7 +186,7 @@ lower densities.
 different locations. The larger the circle, the higher the data value it represents. This makes it useful for showing quantities or comparing values across different 
 points on a map. 
 - For choropleth maps, colours or shades represent different values for each area. Usually, the darker or more intense colour signifies higher values. The effectiveness of a choropleth map is dependent on the __colouring scheme__. 
-- Choropleth maps are usually created by [classifying](/content/Modul_3/en_qgis_data_classification.md) geodata into 
+- Choropleth maps are usually created by [classifying](/content/Module_3/en_qgis_data_classification.md) geodata into 
 distinct groups, either using categorised or graduated classification. 
 - Graduated symbols maps are created by changing the size of a symbol in relation to a value in the attribute table. 
 

content/Modul_4/en_qgis_map_design_I_ex4.md

@@ -1,8 +1,8 @@
 # Map design Exercise : Creating a Map of Pakistan
 
 :::{card}
-:link: https://giscience.github.io/gis-training-resource-center/content/Modul_3/en_qgis_modul_3_exercises.html
-__Click here to return to the exercise overview page for module 3__ 
+:link: https://giscience.github.io/gis-training-resource-center/content/Modul_4/en_qgis_modul_4_exercises.html
+__Click here to return to the exercise overview page for module 4__ 
 :::
 ::::{grid} 2
 :::{grid-item-card}
@@ -51,8 +51,9 @@ In 2024, the provinces of Punjab, Sindh, and Balochistan in Pakistan experienced
 
 ### Available Data
 
-You have created the data for Larkana in [Module 3, Exercise 4](https://giscience.github.io/gis-training-resource-center/content/Modul_3/en_qgis_module_3_ex2.html). In order to conduct this exercise please create a folder on your computer and copy your entire folder structure of Exercise 4 in there. In case you did not do Module 3 - Exercise 4 you can download the data [here](https://nexus.heigit.org/repository/gis-training-resource-center/Module_4/Exercise_2/Module_4_Exercise_2_Larkana_flood_map.zip). Save the folder on your computer an unzip the file.
+You have created the data for Larkana in [Module 3, Exercise 4](https://giscience.github.io/gis-training-resource-center/content/Module_3/en_qgis_module_3_ex2.html). In order to conduct this exercise please create a folder on your computer and copy your entire folder structure of Exercise 4 in there. In case you did not do Module 3 - Exercise 4 you can download the data [here](https://nexus.heigit.org/repository/gis-training-resource-center/Module_4/Exercise_2/Module_4_Exercise_2_Larkana_flood_map.zip). Save the folder on your computer an unzip the file.
 
+<!--CHECK: Is the link to the nexus correct?-->
 
 | Dataset name| Original title|Publisher|Download from| 
 | :-------------------- | :----------------- |:----------------- |:----------------- |
@@ -165,7 +166,7 @@ Open the __Symbology Tab__ for the `PAK_flood_2024_blocked_road`-layer and choos
 
 __Airport__
 
-In the [previous exercise](/content/Modul_3/en_qgis_module_3_ex2.md) you found out that the Mohenjodaro Airport in the southwest of Larkana City is still accessible via the road network. Essential supplies could potentially be transported from the airport into the city without encountering any roadblocks. We want to point out this possibility. Let's mark the airport as a point and visualize it!
+In the [previous exercise](/content/Module_3/en_qgis_module_3_ex2.md) you found out that the Mohenjodaro Airport in the southwest of Larkana City is still accessible via the road network. Essential supplies could potentially be transported from the airport into the city without encountering any roadblocks. We want to point out this possibility. Let's mark the airport as a point and visualize it!
 
 To do so we will create an entirely new point dataset representing airports.
 * Click on  `Layer` --> `Create Layer` -> `New GeoPackage Layer`([Wiki Video](/content/Wiki/en_qgis_digitalization_wiki.md#create-a-new-layer)) 

content/Modul_4/en_qgis_styling_vector_data.md

@@ -58,7 +58,7 @@ Most simple markers consist of a __fill__ and an __outline__. Depending on the t
 
 - The fill determines the fill colour of the symbol. You can change the colour and transparency. You are also able to make more complex fills such as a line pattern fill, or an SVG-symbol fill.
 - The outline determines the colour, type, and thickness of the outline. Next to the colour and transparency, the outline is the most critical for distinguishing between different elements. For example, thicker lines for roads usually signify roads of a higher order (such as highways), while thin dashed lines might signify footpaths, inaccessible to road vehicles.
-- You can either style a single symbol for each layer or use different styles based on a [categorisation method](/content/Modul_3/en_qgis_data_classification.md). 
+- You can either style a single symbol for each layer or use different styles based on a [categorisation method](/content/Module_3/en_qgis_data_classification.md). 
 
 In the Symbology Tab, you can select between various symbolization 
 methods (see {numref}`symbolisation_methods_m4`). The most important ones are __Single Symbol__, __Categorised__, __Graduated__, and __Rule-based__. 
@@ -95,7 +95,7 @@ __For example__, assign a different symbol for each type of building (industrial
 - Creates classes for numerical data.
 - A colour gradient can be selected to represent the distribution of the data
 
-__For example__, create 6 classes of population sizes and assign a color gradient from white to red to indicate the population size in a district (see [Module 3: Geodata Classifification](/content/Modul_3/en_qgis_data_classification.md)).
+__For example__, create 6 classes of population sizes and assign a color gradient from white to red to indicate the population size in a district (see [Module 3: Geodata Classifification](/content/Module_3/en_qgis_data_classification.md)).
 
 :::
 

content/Modul_5/en_qgis_modul_5_ex3.md

@@ -2,7 +2,7 @@
 __🔙[Back to Homepage](/content/intro.md)__
 
 :::{card}
-:link: https://giscience.github.io/gis-training-resource-center/content/Modul_3/en_qgis_modul_3_exercises.html
+:link: https://giscience.github.io/gis-training-resource-center/content/Module_3/en_qgis_module_5_exercises.html
 __Click here to return to the exercise overview page for module 3__ 
 :::
 ::::{grid} 2

content/Modul_5/en_qgis_spatial_tools.md

@@ -4,7 +4,7 @@
 
 Spatial processing uses spatial information to extract new meaning from GIS data. It does so by using the __spatial relationship__ of different layers or features. Spatial relationships describe how things are located in relation to one another. In humanitarian work, this helps answer critical questions like “Which communities are near a water source?” or “Which areas are isolated from health services?”. Or, we might want to identify the best locations for distributing aid, assess flood risk areas, or plan evacuation routes.
 
-We have already encountered spatial relationships in module 3 in the subchapter on __[geometrical operators](https://giscience.github.io/gis-training-resource-center/content/Modul_3/en_qgis_data_queries.html#geometric-operators)__— also called geometrical predicates in QGIS. 
+We have already encountered spatial relationships in module 3 in the subchapter on __[geometrical operators](https://giscience.github.io/gis-training-resource-center/content/Module_3/en_qgis_data_queries.html#geometric-operators)__— also called geometrical predicates in QGIS. 
 The table below describes spatial relationships and gives examples when these spatial relationships are relevant in humanitarian aid. 
 
 | __Spatial Relationship__ | __Description__ | __*Humanitarian Example*__ |

content/Module_2/en_qgis_geodata_concept.md

@@ -140,7 +140,7 @@ __SHP, SHX__ and __DBF__ are the __mandatory__ files that every shapefile must c
 Another type of geospatial data is raster data. Raster data consists of cells that are organized into a grid with 
 rows and columns, thus forming a raster. Each cell, or pixel, contains a value which holds information (for 
 example, temperature, or population density). Since raster data consists of pixels, aerial photographs or satellite 
-imagery can also be used as raster data, if they have geographical coordinates (see [georeferencing](/content/Modul_3/en_qgis_georeferencing.md)).
+imagery can also be used as raster data, if they have geographical coordinates (see [georeferencing](/content/Module_3/en_qgis_georeferencing.md)).
 
 Typical uses for raster data are: 
 

content/Module_3/en_qgis_module_3_ex4.md

@@ -143,16 +143,16 @@ Open the Excel or pdf file “Nigeria_flood_2022_affacted_population” and open
     * When you are done, click ![](/fig/mActionSaveEdits.png) to save your edits and switch off the editing mode by again clicking on ![](/fig/mActionToggleEditing.png)([Wiki Video](/content/Wiki/en_qgis_attribute_table_wiki.md#attribute-table-data-editing)). 
 8. To visualise the enriched data set, we use the function "Categorized Classification" function. This means that we select a column from the attribute table and use the content as categories to sort and display the data ([Wiki Video](/content/Wiki/en_qgis_categorized_wiki.md)).
     * Right-click on the layer “Borno_admin2_pop” in the `Layer Panel` -> `Properties`. A new window will open up with a vertical tab section on the left. Navigate to the `Symbology` tab.
-    * On the top you find a dropdown menu. Open it and choose `Categorized`. Under `Value` select “Flood_affacted”.
-    * Further down the window, click on `Classify`.  Now you should see all unique values or attributes of the selected “Flood_affacted” column.  You can adjust the colours by double-clicking on one row in the central field. Once you are done, click `Apply` and `OK` to close the symbology window.
+    * On the top you find a dropdown menu. Open it and choose `Categorized`. Under `Value` select “Flood_affected”.
+    * Further down the window, click on `Classify`.  Now you should see all unique values or attributes of the selected “Flood_affected” column.  You can adjust the colours by double-clicking on one row in the central field. Once you are done, click `Apply` and `OK` to close the symbology window.
 ```{figure} /fig/en_qgis_categorized_classification_nigeria_flood_exercise.png
 ---
 width: 600px
 name: 
 align: center
 ---
 ```
-9. Next, we want to visualise the affected communities which are listed in the Nigeria_flood_2022_affacted_population table. To find these communities in QGIS, we need two things. An OpenStreetMap base map and the plugin `OSM Place Search`. 
+9. Next, we want to visualise the affected communities which are listed in the Nigeria_flood_2022_affected_population table. To find these communities in QGIS, we need two things. An OpenStreetMap base map and the plugin `OSM Place Search`. 
     * To add the OSM as a base map click on `Layer` -> `Add Layer` -> `Add XYZ Layer…`. Choose `OpenStreetMap` and click `Add`. 
     Arrange your layer in the `Layer Panel` so the OSM is at the bottom ([Wiki Video](/content/Wiki/en_qgis_basemaps_wiki.md))
 . 

content/Trainers_corner/en_how_to_teach_GIS.md

@@ -92,7 +92,7 @@ Group exercises rely more on the independent work of the trainees. This is great
 Independent from the type of exercise, you should briefly go over the following points with the trainees at the beginning of the exercise:
 
 1. __Aim of the exercise:__ In general, a hands-on exercise should start with explaining the goal of the exercise. For example: _„This exercise aims to teach the process of basic spatial data processing using the tools Clip, Merge and dissolve."_ This is a good opportunity to highlight the practical use of the tools the participants will learn to boost motivation.
-2. __Background:__ Ideally, the exercise is built around a real-world example or a fictional scenario within the humanitarian work. In this case, you should quickly explain the background and story. An example of this is the exercise in module 3 ([Exercise: Nigeria Floods](https://giscience.github.io/gis-training-resource-center/content/Modul_3/en_qgis_modul_3_ex1.html)).
+2. __Background:__ Ideally, the exercise is built around a real-world example or a fictional scenario within the humanitarian work. In this case, you should quickly explain the background and story. An example of this is the exercise in module 3 ([Exercise 4: Nigeria Floods](https://giscience.github.io/gis-training-resource-center/content/Module_3/en_qgis_module_3_ex4.html)).
 3. __Exercise Data:__  Most hands-on exercises use real-world data (from [HDX](https://data.humdata.org), for example). Each dataset used in the exercise should be explained shortly. This information can be found on each exercise on the platform. Make sure that everybody has downloaded the exercise data before you start. Note that some trainees are not familiar with `zip.` files. Make sure the trainees unzip the folders before trying to import them into QGIS and take the time to solve any other issues concerning the exercise data.
 
 ### During the exercise: Follow-along

content/Trainers_corner/en_how_to_training.md

@@ -85,7 +85,7 @@ Keep in mind that you don't need to follow the module structure. It is there to
         QGIS installation  
         QGIS interface  
     - 
-        [Exercise 1: Understanding the Interface](https://giscience.github.io/gis-training-resource-center/content/Modul_1/en_qgis_interface_ex2.html)
+        [Exercise 1: Understanding the Interface](https://giscience.github.io/gis-training-resource-center/content/Module_1/en_qgis_interface_ex1.html)
     - 
         [QGIS Basics](https://giscience.github.io/gis-training-resource-center/content/Wiki/en_qgis_qgis_basics_wiki.html)
 *   - __Module 2: Working with Geodata__
@@ -98,7 +98,7 @@ Keep in mind that you don't need to follow the module structure. It is there to
         Data sources  
         Attribute table
     - 
-        [Exercise 1: Geodata Concept](https://giscience.github.io/gis-training-resource-center/content/Modul_2/en_qgis_geodata_concept_ex1.html)  
+        [Exercise 1: Geodata Concept](https://giscience.github.io/gis-training-resource-center/content/Module_2/en_qgis_geodata_concept_ex1.html)  
         [Exercise 2: The World](https://giscience.github.io/gis-training-resource-center/content/Modul_2/en_qgis_modul_2_ex_1.html)  
     - 
         [Geodata](https://giscience.github.io/gis-training-resource-center/content/Wiki/en_qgis_geodata_wiki.html)  
@@ -112,10 +112,10 @@ Keep in mind that you don't need to follow the module structure. It is there to
         Geodata selection and queries  
         Geodata classification  
     - 
-        [Exercise 1: Access to financial institutions](https://giscience.github.io/gis-training-resource-center/content/Modul_3/en_qgis_digitalisation_ex2.html)  
-        [Exercise 2: Overview map of the prevalence of stunting in Sierra Leone](https://giscience.github.io/gis-training-resource-center/content/Modul_3/en_qgis_classification_ex1.html)  
+        [Exercise 1: Access to financial institutions](https://giscience.github.io/gis-training-resource-center/content/Module_3/en_qgis_digitalisation_ex.html)  
+        [Exercise 2: Overview map of the prevalence of stunting in Sierra Leone](https://giscience.github.io/gis-training-resource-center/content/Module_3/en_qgis_classification_exe.html)  
         Big Exercise: 
-        [Exercise 3: Nigeria Floods](https://giscience.github.io/gis-training-resource-center/content/Modul_3/en_qgis_modul_3_ex1.html)  
+        [Exercise 3: Nigeria Floods](https://giscience.github.io/gis-training-resource-center/content/Module_3/en_qgis_module_3_ex4.html)  
     - 
         [Digitization](https://giscience.github.io/gis-training-resource-center/content/Wiki/en_qgis_digitalization_wiki.html)  
         [Geodata classification](https://giscience.github.io/gis-training-resource-center/content/Wiki/en_qgis_data_classification_wiki.html)