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.
- Go to Sustainable Development Goal 2.1.1: Prevalence of undernourishment (percent) item details page.
The layer's information pane appears. This page contains metadata about the layer, including a description, data attributes, and more.
Note:
You can also find this data by going to the SDG Data Hubs site; scrolling down to the section Data to Advance the SDGs; clicking Zero Hunger; and searching for Prevalence of undernourishment. - Click the Open in Map Viewer button.
A map, showing the Sustainable Development Goal 2.1.1: Prevalence of undernourishment (percent) layer, appears. This map shows the proportion of the population whose habitual food consumption is insufficient.
On this map, the percentage of the population that is undernourished is shown with graduated symbols. The larger the circle, the more prevalent undernourishment is in the country. Based on a visual analysis, what countries have the highest rates of undernourishment (large circles)? What countries do not have data reported (small gray circles)? What countries have rates of nourishment of 0 (smallest yellow circles)?
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To see all the data provided for a country, zoom in and click on a circle. The value that's mapped is in the field Latest Value.
There are lots of overlapping points on the map, so it's difficult to tell what each is showing. Because this data is normalized, given in the form of a percentage, you can also choose to show it as polygon features with graduated colors. First, you'll need to sign in to your ArcGIS organizational account.
- On the ribbon, click Sign in and enter the credentials for your ArcGIS Online account.
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If you don't have an organizational account, see options for software access.
The map reloads. Next, you'll add polygon data to use as each country's boundary. The layer you're looking for is maintained by Esri and stored in ArcGIS Living Atlas. ArcGIS Living Atlas of the World is an authoritative collection of geographic data, including layers, maps, and apps.
- On the Contents (dark) toolbar, click Add and choose Browse layers.
- Above the search bar, click the drop down and choose Living Atlas.
- In the search bar type countries. For the layer World Countries Generalized, click the Add button.
The World Countries Generalized layer draws on top of the map.
- Close the Browse layers pane and the Properties pane. On the Contents toolbar, click Layers.
The Layers pane appears with the two layers listed. Normally, you'd reorder the layers to show point data, the Prevalence of undernourishment layer, on top of polygon data, the World Countries Generalized layer, but you won't need the points much longer. Now you'll use the Join Features tool to add the data from the Prevalence of undernourishment layer to the country polygon layer.
- On the Settings (light) toolbar, click Analysis.
- In the Analysis pane, click Tools.
- Expand the Summarize Data group and click Join Features.
A join is a way to combine your data, based on either shared attributes or location. You'll use location to connect the data values from the undernourishment layer to the polygon shapes of the country layer. To do this, you'll use a spatial join, which means that the data for each point will be added to the polygon it overlaps.
- For Target layer, click the Layer button and click the World Countries Generalized layer.
- For Join layer, click the Layer button and choose the Prevalence of undernourishment layer.
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After you select the Target and Join layers you'll see how many features are in each one. It's likely the layers do not have the same number of features. The Join Features command will join data only for features that match the Join settings parameters. You may want to take note of how many features are in the input layers and how many are in the output layer.
- Under Join settings, turn on Use spatial relationship and then for Spatial relationship, choose Completely contains.
Choosing Completely contains ensures that data from points fully inside of a country polygon are added to that country's polygon.
- For Join operation, confirm that Join one to one is chosen.
This parameter further specifies that there is only one record in the Prevalence of undernourishment layer that needs to be joined to the World Countries Generalized layer.
- For Output name, type Undernourishment and add your initials.
Each layer must have a unique name. Adding your initials is a way of making your result layer name unique.
- Click Run.
When the tool is finished running, the layer is added to the Layers pane and to the map.
- In the Layers pane, click the Visibility button next to the World Countries Generalized and Prevalence of undernourishment layers to turn them off.
The map shows the Undernourishment layer. Some of the countries had no data value and others had 0 for Latest Value. You don't need to map those countries, so next you'll create a filter to prevent them from appearing when the Undernourishment layer is drawn.
Create a filter
You'll create a filter for the layer Undernourishment to show only the countries of interest.
- Ensure the Undernourishment layer is selected and on the Settings toolbar, click Filter.
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The blue line next to the layer name shows which layer is currently selected.
The Filter pane appears. A filter is one or more expressions that let you show only the features in a layer that are of interest. You want to show only the countries in the Undernourishment layer that have Latest Value not blank and not equal to 0. You'll build the expression.
- In the Filter pane, click Add expression. Under Expression, use the pull-down menus to build: Latest Value is not blank.
- On the filter pane, opposite Set, click the Options button and choose Add condition. Build: Latest Value is not 0.
- For Set, leave the default of Match either conditions.
Your map updates to show only the countries that match these conditions.
- Click Save to save the filter.
Next, you'll style the Undernourishment layer.
Style the layer
The Undernourishment layer includes country polygon features with non-blank and non-zero percentage values for Latest Value. You'll now style the countries with graduated colors based on those values.
- On the Contents toolbar, ensure the Undernourishment layer is selected. On the Settings toolbar, click Styles.
The Styles pane appears. Currently, the map shows just the location of the data. To map the amounts, you'll change the attribute to symbolize. For this map, you'll use the Latest Value field, the most recent data for the country. If you want to explore the reported data values for individual countries, you can open the attribute table and select the country.
- In the Styles pane, for Choose attribute, click Field and choose the Latest Value. Click Add.
The map automatically redraws to show default symbology for the Counts and Amounts (Color) style. The darkest blues show where undernourishment is most prevalent.
- Click Done.
What do you see on this map? Which countries have the highest rates of undernourishment? Are you able to see any patterns in the data that weren't as clear before? Why do you think the unstyled counties had no value or a value of 0 for their rates of undernourishment?
- Click different countries on the map to read information about them, including the Latest Value attribute.
According to the data, the top five countries whose populations are experiencing undernourishment are Haiti (48.2 percent), North Korea (47.6 percent), Madagascar (41.7 percent), Chad (39.6 percent), and Liberia (37.5 percent). These places 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?
Often when discussing hunger, the conversation focuses on undernourishment and similar measures of severity. But food access isn't just a problem across the Southern Hemisphere, where it appears most prevalent. In the next section, you'll map a different metric, food insecurity.
Compare food insecurity
To get another perspective on where people are experiencing hunger, you'll also map food insecurity. As defined by the UN, 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.
- On the Contents toolbar, click Add and choose Browse layers.
- Click My Content and choose ArcGIS Online.
This layer is published to ArcGIS Online.
- On the search bar type severe food insecurity and add the Indicator 2.1.2: Prevalence of moderate or severe food insecurity in the adult population (percent) layer owned by unstats_admin.
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You can also find this data by going to the SDG Data Hubs site; scrolling down to the section Data to Advance the SDGs; clicking Zero Hunger; and searching for Prevalence of moderate or severe food insecurity. - Close the Browse layers pane. On the Contents toolbar click Layers and turn off the Visibility button for the Undernourishment layer.
How does the prevalence of severe food insecurity layer compare to the prevalence of undernourishment layer in each country? Notice that some countries have reported food insecurity data and not undernourishment—why do you think this is?
Where data is available, many countries disaggregate the data by sex, so many countries have three data points. For the attribute Sex Desc, the values Female and Male account for people over the age of 15 of each sex. For the value Both Sexes, data is reported as a percentage of the population, regardless of age.
- Click the points for different countries to explore the data. Read the pop-ups, and if necessary, click the arrows to see how the data varies by sex.
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?
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To continue this analysis, you can follow the same process to join the Prevalence of moderate or severe food insecurity in the adult population (percent) layer to the World Countries Generalized layer.
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.
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