Exercise 1: Disaster effects in different regions of Senegal#

Aim of the exercise#

Become familiar with different types of non-spatial analysis and geoprocessing tools. Understand the process of discovering relationships and connections between features in spatial data.

Data#

Download all datasets and save the folder on your computer and unzip the file. The zip folder includes:

Hint

All files still have their original names. However, feel free to modify their names if necessary to identify them more easily.

Task#

Create an overview of the effects of disasters in different regions of Senegal. Use non-spatial joins, table functions and different symbology.

Hint

The projected coordinate system for Senegal is EPSG:32628 WGS 84 / UTM zone 28N

  1. Load the Senegal administrative boundary layer (sen_admbnda_adm1_1m_gov_ocha_20190426.shp), as well as population per subnational unit (sen_admpop_adm1_2020.csv) and the Desinventar Sendai data of Senegal (DI_Stat924.xls) into QGIS.

  2. Make sure to reproject the dataset with the administrative boundaries into UTM zone 28N. See the Wiki entry on Projections for further information.

  3. Conduct non-spatial joins based on regions listed in two datasets and the PCODE listed in these same sets. See the Wiki entry on Non-spatial joins for further information.

../../_images/en_ex1_AT_admin_pop_sen.png

Fig. 135 Screenshot of the different attribute tables with the corresponding columns highlighted#

  1. First, add the total population of each administrative area to the shapefiles. Select the correct column that should be added (Hint: search for the column named Total).

  2. Then, add the number of directly and indirectly affected people. Also select the correct columns that should be added (Hint: search for the column names Directly affected and Indirectly Affected).

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Fig. 136 Screenshot of the tool Join Attributes by Field Value for the total population#

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Fig. 137 Screenshot of the tool Join Attributes by Field Value for the directly and indirectly affected people#

  1. Use the table functions to calculate the area of each region in square kilometers and the density of the population. See the Wiki entry on Table functions for further information.

    • Create a new column/field named "area_sqkm" using the field calculator. Ensure decimal numbers are used as the field type. For the calculation use the expression: $area / (1000 * 1000)

    • Create another column/field named "pop_per_sqkm" with decimal numbers as field type. Use the expression: "Total" / "area_sqkm" for the calculation.

You can access the field calculator through your attribute table by activating Toggle editing mode and clicking on this symbol to Open field calculator.

../../_images/en_ex1_area_sqkm.png

Fig. 138 Screenshot of the area calculation using the field calculator#

../../_images/en_ex1_pop_per_sqkm.png

Fig. 139 Screenshot of the population per square km calculation using the field calculator#

  1. Now, we need to rename the Indirectly Affected and Directly Affected columns so they don’t contain spaces. This ensures that the field calculator works properly. For this task we will use the tool Rename field.

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Fig. 140 Screenshot of the Rename field tool#

  1. Let us now determine the proportion of the population directly or indirectly affected in relation to the total population per region.

    • Use the field calculator. Use the expression: "Indirectly Affected" / ("Total"/100)

    • Use the field calculator. Use the expression: "Directly Affected" / ("Total"/100)

../../_images/en_ex1_per_indirect_affected.PNG

Fig. 141 Screenshot of the calculation of the proportion of the indirectly affected population relative to the total population within each region.#

../../_images/en_ex1_per_direct_affected.PNG

Fig. 142 Screenshot of the calculation of the proportion of the directly affected population relative to the total population within each region.#

  1. Select a color scheme using the Symbology to visualize the share of people directly affected in the different regions (Hint: Categorized).

    • Play around with different modes to find a useful color/categorization scheme for the visualization.

    • Which regions are more and which are less directly affected? Are there any data gaps?

  2. Export the map as a png (Hint: Project, Import/Export, Export map to Image).

  3. Repeat the previous steps, but this time visualize the indirectly affected people in each region (Hint: Categorized, column Indirectly affected).

  4. What differences can be observed between the regions directly affected and those indirectly affected?