Measure global hunger

Note:

This tutorial will be retired on April 30, 2026. We apologize for any inconvenience this may cause. You may view more tutorials in the gallery.

Hunger around the world is caused by many factors, from world policies to meteorological events. The global economy and the increasing globalization of trading goods is also a contributing factor. For example, the cultivation of cash crops, such as cotton and cacao, in several African countries supports international trade and may increase a country's ability to earn more and enter the global economy. However, it also incentivizes local farmers to exclusively grow export crops, resulting in less available land to grow food crops for local consumption and the local economy.

The Prevalence of Undernourishment measure was defined by the United Nations (UN) in 2024 as the "probability that a randomly selected individual from the reference population is found to consume less than his/her calorie requirement for an active and healthy life." The UN Sustainable Development Goal (SDG) 2: Zero Hunger aims to end hunger and ensure access by all people, especially people in vulnerable situations, to safe, nutritious, and sufficient food all year round by 2030. In this tutorial, you'll use SDG data to compare hunger in the forms of undernourishment and food insecurity for the world's population.

This tutorial was last tested on March 4, 2026.

Requirements

• ArcGIS organizational account with a Publisher, Facilitator, or Administrator role (see options for software access)
• 0.5 credits

Measure global hunger

Add data on undernourishment

The first measure of hunger that you'll map is the prevalence of undernourishment. When discussing global hunger, undernourishment is one of the most important measurements to consider.

1. Sign in to your ArcGIS organizational account.

   Note:

   If you don't have an organizational account, see options for software access.

2. On the ribbon, click the Map tab.

   

   A new map opens.

3. On the Contents (dark) toolbar, click Tables.

   

   The Tables pane appears.

4. In the Tables pane, click Add.

   

5. In the Add table pane, click the My content drop-down menu and choose ArcGIS Online.

   

6. In the search bar, type (or copy and paste) Indicator 2.1.1 owner:Esri_Tutorials.

   This table data was downloaded from the UN SDG Indicators Database and includes SDG Indicator 2.1.1: Prevalence of undernourishment (percent) data. This table contains the proportion of the population in each country whose habitual food consumption is inefficient.

7. For the Indicator 2.1.1: Prevalence of undernourishment (percent) table, click Add.

   

   The table is added and opens. Since this is a table and does not include a spatial component, the data does not appear on the map. Later, you'll join the table to a spatial layer so you can visualize the data on the map. For now, you'll explore the table's data.

8. In the Indicator 2.1.1: Prevalence of undernourishment (percent) table, scroll through the fields.

   The table's data contains the percent of the population undernourished for each country from the years 2001-2024.

   

9. Scroll to the end of the table until you see the Latest Value column.

   This data was downloaded from the UN SDG Data Download site on February 24, 2026. At the time, 2024 data was not released. Additionally, some countries do not have estimated percentage values for every year. The Latest Value field shows the most recent available value for each country.

   

   Any countries that had a recorded percentage of less than 2.5 percent were updated to display 0.00.

10. Close the table.

Join the data

Now that you understand the data, you'll add country boundary data. Then, you'll use the Join Features tool to join, or combine, the table data and the boundaries so you can visualize the prevalence of undernourishment on the map.

1. On the Contents toolbar, click the Layers button.
2. In the Layers pane, click Add.
3. Click My content and choose ArcGIS Online.
4. Search for UN Country Boundaries owner:sdgs.today. For the UN Country Boundaries layer, click Add.

   

   The layer appears on the map. It is a polygon layer showing country boundaries.

5. In the Add layer pane, click the Back button.

   

   To join two datasets, both datasets must have a field with identical information. Name or ID fields are usually used to perform joins. You'll look at the country boundaries layer's fields to see if there is a name field that matches the one in the table.

6. On the map, zoom out if necessary and click any country.

   A pop-up appears.

   

   The Name (En.) field labels countries using the same format as the GeoArea Name field in the Indicator 2.1.1: Prevalence of undernourishment (percent) table. You'll use this field to join these two datasets together.

7. Close the pop-up.
8. On the Settings (light) toolbar, click Analysis.

   

9. In the Analysis pane, click Tools.
10. In the Summarize Data group, click Join Features.

    

11. In the Join Features pane, for Target layer, click Layer. Choose the UN Country Boundaries layer.
12. For Join layer, click Layer and choose the Indicator 2.1.1: Prevalence of undernourishment (percent) table.

    

    Note:

    The tool displays the count of features for the target and join layers. It's likely the layers do not have the same number of features. The Join Features tool will join data only for features that match the Join settings parameters. Features that have no match will not be joined.

    Next, you'll choose the fields to join. You'll use the name fields in each layer.

13. In the Join settings section, for Target field, choose Name (En.).
14. For Join field, choose GeoArea Name.
15. For Join operation, confirm that Join one to one is chosen.

    This parameter specifies that there is only one record in the Indicator 2.1.1: prevalence of undernourishment (percent) table that needs to be joined to the UN Country Boundaries layer.

    

16. Scroll down to the Result layer section. For Output name, type Undernourishment, followed by your name or initials (for example Undernourishment (Your Name)).

    Note:

    You cannot create two layers in an ArcGIS organization with the same name. Adding your initials to a layer name ensures that other people in your organization can also complete this tutorial. Once a layer has been created, you can rename it in the map to remove your initials, which will not affect the name of the underlying data layer.

    Note:

    This tool consumes credits. Click Estimate credits to see how many the tool will consume.

17. Click Run.

    When the tool is finished running, the layer is added to the Layers pane and to the map.

18. In the Layers pane, point to the UN Country Boundaries layer and click the Hide button to turn it off.

    

    The map shows only the Undernourishment layer. Some countries are not included in the layer because they did not have data recorded in the undernourishment table.

    

Create a filter

You want to view the 2023 percentages for each country. However, some countries have a value of 0.00 in 2023. Since you want to identify where undernourishment is a major issue, you don't need to map those countries. You'll create a filter to prevent them from appearing.

1. In the Layers pane, ensure the Undernourishment layer is selected.

   Note:

   The blue line next to the layer name shows which layer is currently selected.

2. On the Settings toolbar, click Filter.

   

   The Filter pane appears. A filter is based on one or more expressions that indicate the features in a layer that are of interest. You want to show only the countries that have values for 2023 that are not blank or equal to 0.00.

   Tip:

   You can also perform this step and the remainder of the tutorial using the Latest Value field or any year between 2001 and 2023.

3. In the Filter pane, click Add new. For Condition, use the drop-down menus to build the expression 2023 is not blank.

   

4. Click Add new. Build the expression 2023 is not 0.

   

5. For Show features where, ensure that All of the following are true is chosen.
6. Click Save.

   Your map updates to show only the countries that match the conditions you specified.

   

Style the layer

Next, you'll style the countries with graduated colors based on their 2023 values.

1. On the Settings toolbar, click Styles.

   

   The Styles pane appears. Currently, the map shows only the location of the data. To map the amounts, you'll add an attribute to style. For this map, you'll use the 2023 field.

2. In the Styles pane, for Choose attributes, click Field.

   

3. In the Select fields window, search for and choose 2023.

   

4. Click Add. In the Styles pane, click Done.

   The map redraws to show default symbology for the Counts and Amounts (Color) style. The darkest blues show where undernourishment is most prevalent.

   

   To see the layer more clearly, you'll change the basemap.

5. On the Contents toolbar, click Basemap.
6. In the Basemap pane, choose Dark Gray Canvas.

   

   The dark basemap provides a better contrast to see the layer's symbology.

   

   Next, you'll take a look at the attribute table to find the countries with the highest rates of undernourishment.

7. On the Contents toolbar, click Layers. For Undernourishment, click the Options button and choose Show table.

   

8. In the Undernourishment table, scroll to the 2023 field. Click the Menu button and choose Sort descending.

   

   According to the data, the top five countries whose populations experienced undernourishment in 2023 were Haiti (54.20 percent), Somalia (53.20), Madagascar (39.50 percent), Syrian Arab Republic (39.00 percent), and Democratic Republic of the Congo (38.50 percent). These countries are located all over the world and have varying reasons for the high prevalence of undernourishment.

   Why do you think these countries might be facing high percentages of undernourishment? Are there specific events in recent history that might be tied to these rates?

9. Close the Undernourishment table.

Compare food insecurity

To get another perspective on where people are experiencing hunger, you'll also map food insecurity. As defined by the UN in 2024, food insecurity is "the extent to which people have difficulties in accessing food of adequate quality and/or quantity due to lack of money or other resources. Difficulties include also psychological concerns associated with the struggle in accessing food." Access to food can be limited by many social and economic factors, from not having enough money to buy food to social unrest and famine causing a lack of food availability.

1. In the Layers pane, click Add.
2. Click My content and choose ArcGIS Online.
3. In the search bar, type (or copy and paste) Indicator 2.1.2 owner:Esri_Tutorials. For the Indicator 2.1.2: Prevalence of moderate or severe food insecurity (percent) layer, click Add.

   

   The layer is added to the map.

4. In the Add layer pane, click the Back button. For the Undernourishment layer, click the Hide button to turn off the layer.

   Now, only the new layer appears on the map.

   

   This layer represents UN SDG Indicator 2.1.2: Prevalence of moderate or severe food insecurity in the population, based on the Food Insecurity Experience scale in 2023. Similar to the dataset you worked with earlier, it was sourced from the same UN SDG Indicators Database, joined to the UN Country Boundaries layer, and filtered and styled using the same steps described previously in this tutorial.

   How does the prevalence of severe food insecurity compare to the prevalence of undernourishment in each country? Some countries have reported food insecurity data and not undernourishment—why do you think this is?

   This layer displays percentages in terms of the whole population, regardless of gender and age. You'll remove a filter to see data based on age and gender.

5. On the Settings toolbar, click Filter. In the Filter pane, click Clear all.

   

6. On the map, click different countries to explore the data. If necessary, in the pop-ups, click the Next arrow to see how the data varies by sex.

   

   Where the data is available, many countries disaggregate the data by sex, so many countries have three data points. For the attribute Sex, the values Female and Male account for people over the age of 15 for each sex. For the value Both Sexes, data is reported as a percentage of the population, regardless of age.

   While the percentage of people experiencing food insecurity is relatively low in many countries in the Northern Hemisphere, there is more variety in this data. Why do you think food insecurity is important to pay attention to?

   Note:

   You can change the Filter conditions and Style attributes to view the data from any year between 2015 and 2023.

7. Close any open pop-ups.
8. On the Contents toolbar, click Save and open and choose Save as.

   

9. In the Save map window, for Title, type Measuring global hunger. Click Save.

Hunger is a critical challenge all over the world, though it varies in both severity and the way it's measured. Undernourishment is a more urgent indicator to monitor and address. Its causes may be exacerbated by factors that should be addressed differently than food insecurity. As the UN works toward the goal of ending hunger and achieving food security, both of these indicators are necessary to factor into creating sustainable, long-term solutions.

You can find more tutorials in the tutorial gallery.

Acknowledgements

• Indicator 2.1.1: Prevalence of undernourishment (percent) and Indicator 2.1.2: Prevalence of moderate or severe food insecurity (percent) source: UN SDG data
• UN Country Boundaries source: United Nations Geospatial
• World Topographic Map sources: Esri, TomTom, Garmin, FAO, NOAA, USGS, OpenStreetMap contributors, and the GIS User Community
• Dark Gray Canvas map sources: Esri, TomTom, Garmin, FAO, NOAA, USGS, OpenStreetMap contributors, and the GIS User Community
• Banner image: Green grass fields by Tom Fisk via Pexels
• Card image: green plants by Ashim D’Silva via Unsplash