Measure global hunger
Add data on undernourishment
The first measure of hunger that you'll map is the prevalence of undernourishment. When global discussing 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.
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You can also find this data by going the main hub site, SDG.org; clicking Zero Hunger; and searching for the dataset, Prevalence of undernourishment (percent) and open in Map Viewer from the site.The map's information pane appears. This page contains metadata about the map, including map description, data attributes, and more.
- Click the Open in Map Viewer button.
A map of the prevalence of undernourishment 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 for countries that have reported data. 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? What countries do not have data reported?
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 layer draws on top of the map.
- Close the Browse layers pane and the Properties pane.
The Layers pane appears with the two layers listed. Normally, you'd reorder the layers to show point data on top of polygon data, but you won't need the points much longer. Now that you have polygon data, 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 connect the data values from the undernourishment dataset 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 Indicator 2.1.1 layer.
- Under Join settings, turn on Use spatial relationship and then for Spatial relationship, choose Completely contains.
Choosing Completely contains as the spatial relationship ensures that none of the overlapping symbols are added to the wrong country. Because each point symbol originates in the centroid of the country, each country polygon will only completely contain one data point.
- For Join operation, confirm that Join one to one is chosen.
This parameter further specifies that there is only one record in the Indicator 2.1.1 layer that needs to be joined to the World Countries layer.
- For Output name, change the layer name to 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.
- In the Layers pane, click the Visibility button next to the World Countries and Indicator layers to turn them off.
Now it is more clear which countries do not have reported data. Countries without a polygon outline weren't added to the join output layer because they didn't have data in the Indicator layer. Many of the countries without reported data are in Africa, though there are several others. Now you'll change the symbology to show where undernourishment is most prevalent.
- Ensure the Undernourishment layer is selected and on the Settings toolbar, click Style.
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The blue line next to the layer name shows which layer is currently selected.
The Style 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. This field has the most current reported data value for each country, so the latest date of reporting may vary. 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 theLatest Value.
The map automatically redraws to show default symbology for the Counts and Amounts (Color) style. On this map, the darkest blues show where undernourishment is most prevalent. This symbology is also called graduated colors.
- 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?
- 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 ribbon, 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.
You can also find this data on the UN Sustainable Development Goals site at Indicator 2.1.2: Prevalence of moderate or severe food insecurity in the adult population (percent).
- In the ArcGIS Online pane, click the back arrow 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.
- In the Contents pane, uncheck the Undernourishment layer to turn it off.
- 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 Indicator 2.1.2 layer to the World Countries layer.
Hunger is a critical challenge all over the world, though it varies in both severity and the way it's measured. Undernourishment is undoubtedly the more urgent indicator to monitor and address, and is caused and exacerbated by different 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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