Food Insecurity Analysis Dashboard

Published November 25, 2025

Data Analytics Software QA Janaka Alwis

Introduction

The Food Insecurity Analysis Dashboard with Dashtera is an interactive analytics platform designed to visualize and explore global trends in food insecurity across countries and regions. Built using Dashtera’s no-code environment, the dashboard leverages the Food and Agriculture Organization (FAO) dataset to highlight global and regional disparities in food security, changes over time, and the socioeconomic groups most affected.

Food insecurity is defined as the percentage of people in the population living in households classified as severely food insecure, based on the Food Insecurity Experience Scale (FIES). Such households experience the most severe food access challenges, including reduced meal size, skipped meals, or entire days without food due to lack of resources.

This dashboard helps researchers, policymakers, and humanitarian organizations to:

Explore regional and country-level patterns in food insecurity.
Identify positive and negative year-to-year changes.
Understand long-term global shifts in food security.
Compare food insecurity trends across income groups.

The dataset covers 2015 to 2022, capturing yearly prevalence of severe food insecurity (indicator code: SN.ITK.SVFI.ZS).

About Dashtera

What is Dashtera?

Dashtera is a no-code, cloud-based dashboarding platform that enables users to connect to datasets, transform them, and build interactive dashboards with ease. It empowers decision-makers to gain insights without requiring advanced technical skills.

Key Features

Connects to CSV, Excel, APIs, and other data sources
Wide variety of charts, including maps, regressions, and drill-down visuals
Interactive filters and regional drill-down capability
Shareable dashboards with flexible layouts
Calculated fields and regression analysis built-in
Intuitive drag-and-drop design

Advantages Over Similar Tools

Extremely easy to use with minimal training
Rapid creation and deployment of dashboards
Lightweight but powerful compared to Tableau or Power BI
Suitable for policy analysts, NGOs, and researchers

Dashboard

Food Insecurity Analysis 2015

This dashboard provides the first baseline view of 2015 food insecurity worldwide.

This visualization includes:

Region-wise country count (bar chart)
Country distribution of food insecurity (donut chart with region → country drill-down)
World map showing 2015 food insecurity percentage per country
World maps of positive vs. negative changes compared to the previous year
Change distribution (bar chart of category shifts)

2016 Change Distribution (compared to 2015):

Change Category	Country Count
(50 Up)	1
(20–50)	4
(5–20)	18
(0–5)	11
(0)	9
(-0–5)	9
(-5–20)	27
(-20–50)	14

Food Insecurity Analysis 2016

This dashboard replicates the structure of the previous Dashboard but for 2016. It highlights regions where food insecurity worsened or improved relative to 2015.

2016 Change Distribution:

Change Category	Country Count
(20–50)	7
(5–20)	20
(0–5)	11
(0)	16
(-0–5)	9
(-5–20)	20
(-20–50)	8
(-50 Down)	2

Food Insecurity Analysis 2017

The 2017 dashboard provides a continuation of trends with updated world maps, distributions.

2017 Change Distribution:

Change Category	Country Count
(50 Up)	6
(20–50)	4
(5–20)	20
(0–5)	19
(0)	18
(-0–5)	10
(-5–20)	25
(-20–50)	6
(-50 Down)	1

Food Insecurity Analysis 2021

The 2021 dashboard reflects the post-2019 shocks, including the pandemic, conflicts, and inflationary pressures, which reshaped food security worldwide.

2021 Change Distribution:

Change Category	Country Count
(50 Up)	2
(20–50)	11
(5–20)	20
(0–5)	16
(0)	37
(-0–5)	14
(-5–20)	25
(-20–50)	16
(-50 Down)	3

Yearly FI Regression (2015–2022)

Regression dashboards model the year-to-year relationships of food insecurity across countries.

Key Insights from Regression Equations:

Slopes ≈ 1 → countries’ rankings remained consistent over time.
Intercepts increasing → global baseline food insecurity rose steadily post-2019.
2015–2017 → stable patterns with minimal shifts.
2017–2018 → disruption suggests regional shocks.
2019–2022 → strong upward drift in baseline insecurity due to global crises.
2015 → 2022 overall trend:
- Equation: y = 1.04x + 1.5
- Meaning: food insecurity rose by ~1.5 percentage points globally in 7 years, with widening inequality.

Full Period Regression (2015–2022, by Income Group)

This regression dashboard compares trends across income groups.

Insights:

Global Trend: slope ≈ 1.01, small upward drift (+0.19).
High-Income: slope 0.96, stable, cushioned baseline.
Upper-Middle Income: slope ≈ 1.01, similar to global average.
Lower-Middle Income: slope ~1, higher intercept (+0.68), more stress.
Low-Income: slope 1, intercept +0.41, persistent and severe insecurity.

Summary:

High-income → stable and resilient.
Upper-middle → mirror global patterns.
Lower-middle → higher baseline stress.
Low-income → persistently high food insecurity.
Globally → insecurity is “sticky,” carrying forward year after year with small upward rises.

Overall Insights & Recommendations

Persistent Inequality: Food insecurity rankings are sticky—countries with high insecurity remain vulnerable year after year.
Post-2019 Shift: Global shocks (pandemic, conflict, inflation, climate change) accelerated food insecurity worldwide.
Income Group Patterns: Low-income countries remain structurally disadvantaged, while high-income countries show resilience.
Policy Relevance: Dashboards highlight where interventions are most needed, guiding international aid and local strategies.
Dashtera Utility: Interactive drill-downs, maps, and regressions make complex global datasets accessible to policymakers and NGOs.

Conclusion

The Food Insecurity Analysis Dashboard with Dashtera transforms a complex global dataset into an interactive, insightful tool for understanding food security dynamics. By integrating maps, drill-down distributions, and regression models, it reveals both short-term shocks and long-term structural patterns in global hunger.

With Dashtera’s no-code approach, stakeholders can explore trends across regions and income groups, monitor progress toward food security goals, and design data-driven interventions to combat hunger.

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