Exercises 1: Automatisation

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Characteristics of the exercise

Type of trainings exercise:

• This exercise can be used in online and presence training.

• It can be done as a follow-along exercise or individually as a self-study.

Estimated time demand for the exercise

Relevant Wiki Articles

• Zonal Statistics

• Intersection

• Projections

• Buffer

• Clip

• Automatisation

Aim of the exercise:

Instructions for the trainers

Trainers Corner

Prepare the training

• Take the time to familiarise yourself with the exercise and the provided material.

• Prepare a white-board. It can be either a physical whiteboard, a flip-chart, or a digital whiteboard (e.g. Miro board) where the participants can add their findings and questions.

• Before starting the exercise, make sure everybody has installed QGIS and has downloaded and unzipped the data folder.

• Check out How to do trainings? for some general tips on training conduction

Conduct the training

Introduction:

• Introduce the idea and aim of the exercise.

• Provide the download link and make sure everybody has unzipped the folder before beginning the tasks.

Follow-along:

• Show and explain each step yourself at least twice and slow enough so everybody can see what you are doing, and follow along in their own QGIS-project.

• Make sure that everybody is following along and doing the steps themselves by periodically asking if anybody needs help or if everybody is still following.

• Be open and patient to every question or problem that might come up. Your participants are essentially multitasking by paying attention to your instructions and orienting themselves in their own QGIS-project.

Wrap up:

• Leave time for any issues or questions concerning the tasks at the end of the exercise.

• Leave some time for open questions.

Available Data

Download all datasets here, save the folder on your computer and unzip the file.

The folder is called “ and contains the whole standard folder structure with all data in the input folder and the additional documentation in the documentation folder.

Dataset	Source	Descriptions
Administrative Boundaries	HDX	The administrative boundaries on level 0-4 for Madagascar can be accessed via HDX provided by OCHA. For this trigger mechanism we provide the administrative boundaries on level 1 (regional level) and 2 (district level) as a shapefile.
Cyclone Tracks	International Best Track Archive for Climate Stewardship (IBTrACS)	IBTrACS project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons.
education facilities and health sites	HOT Export Tool	The POI data (education facilities and health sites) is downloaded using the HOT Export Tool based on OpenStreetMap data.
Population	WorldPop	The worldpop dataset in raster format provides the estimated total number of people per grid-cell for the year 2020. We will be working with the Constrained Individual countries 2020 dataset at a resolution of 100m.

Context

Aina is the GIS expert at the Croix-Rouge Malagasy (CRM). With the cyclone season approaching, she knows that time is of the essence once a storm is forecasted. Every hour counts when it comes to protecting communities at risk.

This year, Aina wants to be one step ahead. Instead of manually analyzing cyclone data under pressure, she decides to prepare an automated QGIS model that will help her respond quickly and efficiently.

Her goal:

Build a workflow that automatically estimates exposed populations and infrastructure at risk.

Tasks: Estimating Exposed Population – Aina’s Manual Approach

Before developing the automated model, Aina used to estimate the exposed population manually whenever a cyclone approached Madagascar. In this task, you will follow the steps she used in the past by working with the historical track of Cyclone Harald, WorldPop raster data, and administrative boundaries.

You will manually buffer the cyclone track, clip the population raster, and calculate exposed population using zonal statistics.

1. Open QGIS and create a new project by clicking on Project -> New

2. Save the project in the “project” folder. To do that click on Project -> Save as and navigate to the folder. Name the project “Cyclon_Harald_Exposure”.

3. Load the GeoJOSN file “Harald_2025_Track.geojson” in your project by drag and drop (Wiki Video) .

4. Reproject the cyclone track to use meters instead of degrees (important for accurate buffering):

   • In the Processing Toolbox, search for Reproject Layer.

   • Input: Harald_2025_Track

   • Target CRS: EPSG:29738 or another meter-based CRS appropriate for Madagascar.

   • Save the result in the temp folder as: Harald_Track_Reprojected

Attention

Buffer distances must be calculated in meters. Many datasets (like GeoJSON cyclone tracks) use geographic coordinate systems like EPSG:4326, which measure in degrees — not meters. To correctly calculate a 200 km buffer, we must first reproject the track into a projected CRS that uses meters.

5. Buffer the cyclone track:

   • In the Processing Toolbox, search for Buffer.

   • Input: Harald_Track_Reprojected

   • Buffer distance: 200000 (meters)

   • Segments: Leave default (5)

   • Dissolve: Yes

   • Save output in the temp folder as: Harald_Buffer_200km

6. Reproject the buffer back to EPSG:4326 (to match the raster’s CRS):

   • In the Processing Toolbox, search for Reproject Layer.

   • Input: Harald_Buffer_200km_29738

   • Target CRS: EPSG:4326 – WGS 84

   • Save the output in the temp folder as: Harald_Buffer_200km_4326

7. Load the administrative boundaries:

   • File: MDG_adm2.gpkg

   • Add using drag and drop or Add Vector Layer.

8. Load the population raster:

   • File: MDG_WorldPop_2020_constrained.tif

   • Add using Layer → Add Raster Layer.

9. Clip the population raster using the buffered impact zone:

   • In the Processing Toolbox, search for Clip Raster by Mask Layer.

   • Input raster: MDG_WorldPop_2020_constrained.tif

   • Mask layer: Harald_Buffer_200km

   • Save output in the temp folder as: Harald_Pop_Clip.tif

10. Calculate total exposed population:

• In the Processing Toolbox, search for Zonal Statistics.

• Input vector layer: MDG_adm2.gpkg

• Raster layer: Harald_Pop_Clip.tif

• Statistic to calculate: Sum

• Field prefix: e.g., pop_

• Save the updated vector layer in the result folder as: Harald_Exposed_Population.gpkg

• The result will be a new column in the attribute table of the MDG_adm2.gpkg layer, showing the total population within the cyclone buffer per district.

11. Visualise the affected population by classifying the results: Now that Aina has estimated the exposed population in each district, she wants to clearly show the differences across regions on the map. To do this, we’ll apply a graduated classification to the Harald_Exposed_Population.gpkg layer using the new population field created by the Zonal Statistics tool.

• In the Layers panel, right-click on the layer Harald_Exposed_Population and choose Properties.

• Go to the Symbology tab on the left.

• At the top of the window, change the style from Single Symbol to Graduated.

• In the Value drop-down menu, select the field that contains the population sum. It typically starts with the prefix you defined earlier, e.g. pop_sum.

• Set the color ramp to one that suits your map (e.g. Reds).

• Choose a classification mode (e.g. Quantile, Natural Breaks, or Equal Interval) and select the number of classes (e.g. 5).

• Click Classify to generate the classification.

• Click Apply and then OK to display the classified map.

Tip

You can adjust class boundaries or labels by double-clicking on each class entry.

Your results should look something like this:

Task 2: Auto