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Exploring Food Insecurity via Geospatial Data Reveals Critical Local Differences

By Guest Writer on December 23, 2019

food insecurity map uganda

When making strategic and tactical decisions, donors, development implementers, and governments want to rely on the best available data. Until now, this data often amounts to aggregated national statistics, or generalized sub-national data that masks critical local differences.

Thanks to recent technological advancements, it is now possible to understand populations at the neighborhood level, even in places where data has been traditionally hard to access.

Satellite imagery, geo-tagged surveys, and cloud computing can be used to reveal population characteristics in countries, cities, and communities down to the 1km2 level. These insights add an entirely new dimension to strategic planning and can answer questions like:

  • Where are my target constituents?
  • What type of aid should be prioritized?
  • What is the best way to disseminate our message?

For example, Fraym’s recent report, Exploring Food Insecurity Outcomes: Analysis Reveals Critical Local Differences shows how geospatial data can add a new level of granularity to commonly used aggregated datasets—in this case Integrated Phase Classification (IPC) for food insecurity.

IPC is meant to help governments and humanitarian agencies understand a crisis and take action by classify households and areas according to a five-phase food security stress scale, ranging from ‘minimal’ to the most severe, ‘emergency’. These classifications are used to make programmatic and resource allocation decisions by multiple development organizations.

The IPC categories were the starting point for Fraym’s analysis of at-risk communities in Uganda across a variety of indicators, allows for a new level of specificity when planning interventions.

In the chart below, you’ll notice that despite higher child mortality rates in Losongolo, the Phase 3 community also has high vaccination levels. This insight suggests leveraging vaccination campaigns as a periodic channels for food distribution and ongoing monitoring.

food insecurity map uganda

The report includes maps that highlight the high variability in stunting rates across and within IPC categories. Only by understanding these spatial dynamics can development actors make truly informed strategic decisions about where to focus scarce resources, and how to maximize impact.

Geospatial data no longer restricts us to viewing areas with pre-defined borders. We can now accommodate requests for data that cross national boundaries, combine multiple regions, or subdivide what is currently the smallest unit of measurement—like IPCs.

For example, inside a Phase 3 area in northwest Uganda, community characteristics for Naponga, Lokayana, and Oreta showed that areas near the Kaabong District experience particularly high stunting levels.

food insecurity map uganda

Geospatial data increases the value of other information sources, and helps provide a framework for analysis. By incorporating information about people’s needs, behaviors, and access, we can now tailor interventions to the unique settings in which we work and empower decision-makers to

  • Identify underserved communities,
  • Understand important local characteristics,
  • Develop micro-targeted strategies to expand impact

As we set out to tackle dynamic challenges like food insecurity, we can and should leverage cutting-edge technology to empower our decisions on a granular basis.

By Christina Paton, Anna Vasylysya, Kathy Quintero at Fryam

Filed Under: Agriculture
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