Merge pull request #152 from GIScience/dev

main update

_config.yml

@@ -3,7 +3,7 @@
 
 title: GIS Resource Training Center
 author: HeiGIT gGmbH
-copyright: "2023"
+copyright: "2024 CC BY-NC-SA 4.0"
 logo: IFRC_Logo_Horizontal_RGB.svg
 
 # Force re-execution of notebooks on each build.

_toc.yml

@@ -127,6 +127,7 @@ parts:
           - file: content/Wiki/en_qgis_visualisation_wiki.md
           - file: content/Wiki/en_qgis_map_making_wiki.md
       - file: content/Wiki/en_qgis_common_errors_and_Issues.md   
+      - file: content/Wiki/en_web_and_mobile_apps_wiki.md
   - caption: GIS in Anticipatory Action
     chapters:
       - file: content/GIS_AA/en_qgis_drought_trigger_somalia.md

content/License.txt

@@ -0,0 +1,5 @@
+This project is released under a Creative Commons BY-NC-SA license. 
+
+For more information, please visit:
+
+https://creativecommons.org/licenses/by-nc-sa/4.0/

content/Modul_5/en_qgis_module_5_ex1.md

@@ -85,7 +85,7 @@ All files still have their original names. However, feel free to modify their na
 
 
 
-1. Load the Somalia district boundaries (admin level 2) (`som_admbnda_adm2_ocha_20230308.shp`) and Healhsites Somalia (`WHO_health_sites.shp)  into QGIS.
+1. Load the Somalia district boundaries (admin level 2) (`som_admbnda_adm2_ocha_20230308.shp`) and Healthsites Somalia (`WHO_health_sites.shp)  into QGIS.
 
 2. Make sure to that both datasets are in the same projection. In this case we have two different projections and we will reproject  __Healthsites Somalia__ into EPSG 4326. Use the tool `Reproject layer` for this process. See the Wiki entry on __Projections__ for further information.
 

content/Modul_5/en_qgis_spatial_tools.md

@@ -247,17 +247,17 @@ __"Join attributes by nearest"__.
 :::{tab-item} Join attributes by location
 
 This tool takes two input layers and creates a new vector layer which has the attributes of both layers in its attribute table.
-The first input layer (see "Join to features in" in {numref}`join_attribute_by_location`) dictates which geometric features will be copied to the new 
-layer. The second input layer (see "By comparing to" in {numref}`join_attribute_by_location`) dictates the attributes that will be added to the new 
+The first input layer (see "Join to features in" in {numref}`join_attributes_by_location`) dictates which geometric features will be copied to the new 
+layer. The second input layer (see "By comparing to" in {numref}`join_attributes_by_location`) dictates the attributes that will be added to the new 
 layer on top of the attributes of the first input layer. You can select which of these attributes should be transferred to the new layer. 
 
 
 ```{figure} /fig/en_join_attributes_by_location.png
 ---
 width: 400 px
-name: join_attribute_by_location
+name: join_attributes_by_location
 ---
-The "Join Attributes by Location"-tool in QGIS 3.36
+The "Join Attributes by Location"-tool in QGIS 3.36.
 ```
 
 :::
@@ -271,9 +271,9 @@ __minimum and maximum values__, __mean values__, as well as __counts__, __sums__
 ```{figure} /fig/en_join_attributes_by_location_summary.png
 ---
 width: 400 px
-name: join_attribute_by_location
+name: join_attribute_by_location_summary
 ---
-Screenshot of the tool Join attributes by location (summary)
+Screenshot of the tool Join attributes by location (summary) in QGIS 3.36
 ```
 
 :::
@@ -289,9 +289,9 @@ suitable matches for the joining process.
 ```{figure} /fig/en_join_attributes_by_nearest.PNG
 ---
 width: 400 px
-name: join_attribute_by_location
+name: join_attribute_by_location_neares
 ---
-Screenshot of the tool Join attributes by nearest
+Screenshot of the tool Join attributes by nearest in QGIS 3.36
 ```
 
 :::

content/Modul_6/en_qgis_non_spatial_tools_ex2.md

@@ -2,12 +2,16 @@
 
 ## Characteristics of the exercise
 
+:::{card}
+__Aim of the exercise:__
+^^^
+We want to create an overview of different vulnerability indicators. From the Covid-19 risk indicators dataset we take `% permanent wall type`, `% permanent roof type` and `poverty incidence`. From the Uganda population statistics we calculate the `% of under fives` and `% of elderly`. By combining the data, we are able to visualize the areas in Uganda that are most vulnerable.
+
+:::
+
 ::::{grid} 2
 :::{grid-item-card}
 
-### Aim of the exercise
-We want to create an overview of different vulnerability indicators. From the Covid-19 risk indicators dataset we take `% permanent wall type`, `% permanent roof type` and `poverty incidence`. From the Uganda population statistics we calculate the `% of under fives` and `% of elderly`. By combining the data, we are able to visualize the areas in Uganda that are most vulnerable.
-
 #### Type of trainings exercise:
 
 - This exercise can be used in online and presence training. 
@@ -81,6 +85,7 @@ __Wrap up:__
 
 
 ### Available Data
+
 Download all datasets and save the folder on your computer and unzip the file. The zip folder includes:
 - `uga_adm2_covid_risk.shp`: Output from Part 1 of the exercise
 - `uga_admpop_adm2_2020proj_1y.csv`: [Uganda population statistics](https://data.humdata.org/dataset/cod-ps-uga)

content/Modul_6/en_qgis_spatial_tools_ex2.md

@@ -160,4 +160,4 @@ name: join_attribute_by_location_2
 Screenshot of the second Join attribute by location operation.
 ```
 
-The next steps of the vulnerability index calculation will be completed in the second part of this exercise, the Non-spatial Geodataprocessing section. Please refer to the [provided link](/content/Modul_5/en_qgis_non_spatial_tools_ex2.md) for this exercise.
+The next steps of the vulnerability index calculation will be completed in the second part of this exercise, the Non-spatial Geodataprocessing section. Please refer to the [provided link](/content/Modul_6/en_qgis_non_spatial_tools_ex2.md) for this exercise.