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# Exercises for Module 3 | ||
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__🔙[Back to Homepage](/content/intro.md)__ | ||
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These exercises cover all the content in Module 3. | ||
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| Exercise| Description |Focus Group|Estimated time| | ||
| :-------------------- | :----------------- |:----------------- |:----------------- | | ||
| __[Exercise 1: Digitisation](/content/Module_3/en_qgis_digitalisation_ex.md):__ | The objective of this exercise is to learn how to digitise points, lines, and polygons of real world objects in a settlements by creating a new dataset. The goal is to create data about the access to financial institutions in Abuja. | QGIS essentials, Knowledge about the QGIS interface and how QGIS handles geodata is required | 3 hours | | ||
| __[Exercise 2: Classification](/content/Module_3/en_qgis_classification_ex.md)__ | The objective of this exercise is to create an overview map of the prevalence of stunting in Sierra Leone at district level. To do this, we will visualise both the prevalence of stunting and key infrastructure such as hospitals, airports, and roads by classifying the data. The decision on which data to visualise and how to present it is up to you, the cartographer. | QGIS essentials; A basic understanding of Geodata and the attribute table is required. | 3 hours | | ||
| __[Exercise 3: Data queries](/content/Module_3/en_qgis_data_queries_ex.md)__ | The aim of this exercise is to learn how to select and extract geofeatures using different methods in QGIS (extract by location, extract by attributes, and extract by expression) | QGIS essentials, knowledge about geodata formats and the QGIS is required | 60 minutes | | ||
| __[Exercise 4: Flood in Nigeria](/content/Module_3/en_qgis_module_3_ex4.md)__| The exercise evolves around the scenario visualizing the situation during the flood in 2022 in one state of Nigeria. You will work with administrative borders and population data from HDX. Furthermore, you will create your own geodata based on information from the field. In the end, you need to use categorised classification to visualize different datasets. | QGIS essentials, knowledge about the attribute table and different categorisation methods is required | 4 hours | | ||
| __[Exercise 5: Larkana Flood response](/content/Module_3/en_qgis_module_3_ex5.md)__| In 2024, the provinces of Punjab, Sindh, and Balochistan in Pakistan experienced devastating floods due to intense and prolonged rainfall. The following analysis will utilize actual data from this natural disaster. The objective is to pinpoint the specific medical centers and healthcare facilities that were impacted by the flooding. Additionally, we will assess the viability of road access to the city of Larkana throughout the flood period. | QGIS essentials | 3 hours | | ||
| __[Exercise 6: Sava Flood response](/content/Module_3/en_qgis_module_3_ex6_madagascar.md)__ |In 2024, the north and northeast of Madagascar was hit by the Tropical Cyclone Gamane on the 27th of March. The following analysis will utilize actual data from this natural disaster. The objective is to pinpoint the specific medical centers and healthcare facilities that were impacted by the flooding. Additionally, we will assess the viability of road access to the populated places. | QGIS essentials | 3 hours | | ||
| __[Exercise 7: Georeferencing](/content/Module_3/en_module_3_ex_7_georeferencing.md)__ | The aim of this exercise is to learn how to georeference a map. In many cases, (governmental) institution might only publish data or maps in a pdf format. However, by georeferencing the map, we can import the map as a raster dataset into our QGIS-project. After that, we can either use it to digitise vector features, or use the raster values in raster data analysis. | QGIS Essentials | ~90 minutes | |
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