DATA 608 | Story 6 : What Is The State of Food Security & Nutrition in the US?

The United Nations Food and Agriculture Organization publication, The State of Food Security and Nutrition in the World 2022 (https://www.fao.org/documents/card/en/c/cc0639en) might lead one to the conclusion that it’s an elsewhere problem. That the people who are suffering malnutrition and starvation are “elsewhere”, not in our backyard. For this assignment you will need to take a closer look here at home (the US) Notes: You will need to locate and source data that reflects food security and nutrition by state broken down by men, women, children and by age groups

Your analysis should demonstrate correlations that exist between level of poverty and food insecurity, malnutrition and starvation.

Your data and analysis should also indicate what happens to the children as they mature into adults. Will they become fully functional citizens or will they require continued support?

You data visualizations need to tell the story for a political audience that you were lobbying to address the issue of food insecurity in the US

This assignment is due at the end of the week twelve of the semester.

The United Nations Food and Agriculture Organization’s report, “The State of Food Security and Nutrition in the World 2022,” often makes people think that food insecurity is a problem only in other parts of the world, not in the United States. However, this article takes a closer look at the situation here at home. It argues that malnutrition and hunger affect people in the United States, not just in other places. By focusing on the complexities of food security within the country, we aim to show that this issue isn’t only about faraway locations but also about challenges faced by people right here in the United States when it comes to having enough nutritious food 1.

The issue of food security is critical. Insufficient access to nutritious and balanced meals can lead to nutritional deficiencies during critical stages of growth and development. This may result in stunted growth, delayed cognitive development, and a higher susceptibility to health issues (Thomas, Miller, and Morrissey 2019). Let us look at these effects in turns. These deficiencies can lead to physical, social, and economic impacts on children now and in the future.

This analysis explores the landscape of food security in the United States, scrutinizing the accessibility, availability, and adequacy of food resources. By navigating this terrain, the aim is to contribute to the dialogue on food security in the United States and not just “elsewhere”. The end result are insights that can inform evidence-based strategies to address existing vulnerabilities and promote resilience within the nation’s food systems.

The analysis has the following specific aims;

1. Explore the state of food security and nutrition by state broken down by men, women, children and by age groups.

2. Demonstrate correlations that exist between level of poverty and food insecurity, malnutrition and starvation.

3. Implications of food insecurity for children as they mature into adults. (that is, Will they become fully functional citizens or will they require continued support?).

4. Your data visualizations need to tell the story for a political audience that you were lobbying to address the issue of food insecurity in the US.

The Load Data section introduces the datasets used to assess food security and nutrition across the United States. Because food insecurity is a multidimensional issue, this project integrates data from multiple authoritative sources.

#import data on Food insecurity in the US - 2022
Food_InSecurity <- read.csv('FoodInSecurity.csv')
glimpse(Food_InSecurity)

Rows: 52
Columns: 7
$ State                             <chr> "U.S.", "AK", "AL", "AR", "AZ", "CA"…
$ Number.of.households              <chr> "131,744,000", "273,000", "2,062,000…
$ Interviewed                       <chr> "96,426", "1,155", "1,806", "1,748",…
$ Food.insecurity.Prevalence        <dbl> 11.2, 9.5, 12.4, 16.6, 10.2, 10.3, 8…
$ Margin.of.error2                  <dbl> 0.20, 2.20, 2.02, 1.75, 1.62, 0.62, …
$ Very.low.food.security.Prevalence <dbl> 4.3, 4.2, 4.7, 6.5, 4.0, 3.8, 3.4, 3…
$ Margin.of.error2.1                <dbl> 0.13, 1.25, 1.07, 0.98, 0.95, 0.43, …

#head(Food_InSecurity)

kable(Food_InSecurity, format = "html") %>%
  kable_styling(full_width = TRUE) %>%
  scroll_box(width = "100%", height = "400px")

USA States are rank higher for food insecurity

The chart below ranks all U.S. states by their food insecurity prevalence. This view helps identify which states face the greatest struggle in ensuring consistent access to nutritious food for their residents.

sorted_data <- Food_InSecurity[order(-Food_InSecurity$Food.insecurity.Prevalence), ]

fig <- plot_ly(sorted_data, x = ~State, y = ~Food.insecurity.Prevalence, type = "bar") %>%
  layout(title = "Food Insecurity Prevalence by State",
         xaxis = list(title = "State"),
         yaxis = list(title = "Food Insecurity Prevalence"))
fig

table_data <- head(sorted_data[, c("State", "Food.insecurity.Prevalence", "Very.low.food.security.Prevalence")], 5)
table_data

   State Food.insecurity.Prevalence Very.low.food.security.Prevalence
4     AR                       16.6                               6.5
45    TX                       15.5                               5.8
27    MS                       15.3                               5.3
20    LA                       15.2                               6.1
42    SC                       14.5                               6.8

The chart and table reveal that food insecurity is concentrated most heavily in Southern states, with Arkansas showing the highest levels, followed closely by Texas, Mississippi, Louisiana, and South Carolina. These states also exhibit elevated rates of very low food security, indicating more severe and persistent hardship. The heat map reinforces this pattern, clearly illustrating that the southern region carries a disproportionate burden of food insecurity. This trend is likely driven by factors such as higher poverty rates, limited access to affordable nutritious food, and higher unemployment in these areas.

The heat map below illustrates food insecurity prevalence across all U.S. states, allowing us to quickly spot regional patterns and disparities. By combining prevalence rates with state-level context—such as very low food security levels and household counts this visualization helps highlight the areas where residents face the greatest challenges in accessing consistent, nutritious food. This geographic perspective is especially important for policymakers, as it visually signals which regions require the most urgent support and targeted intervention.

Food_InSecurity$Full_State <- state.name[match(Food_InSecurity$State, state.abb)]

# Identify top 5 states by food insecurity
top5 <- Food_InSecurity %>%
  arrange(desc(Food.insecurity.Prevalence)) %>%
  slice(1:5)

# Add top 5 state labels using state abbreviations
Food_ggplot <- plot_geo(Food_InSecurity, locationmode = "USA-states") %>%
  add_trace(
    z = ~Food.insecurity.Prevalence,
    locations = ~State,
    text = ~paste(
      "<b>State:</b> ", Full_State,
      "<br><b>Food Insecurity Prevalence:</b> ", Food.insecurity.Prevalence, "%",
      "<br><b>Very Low Food Security:</b> ", Very.low.food.security.Prevalence, "%",
      "<br><b>Households:</b> ", Number.of.households
    ),
    hoverinfo = "text",
    colorscale = "Viridis",
    colorbar = list(title = "<b>Food Insecurity (%)</b>")
  ) %>%
  # Add top 5 state labels (abbreviations)
  add_trace(
    data = top5,
    type = 'scattergeo',
    locationmode = 'USA-states',
    locations = ~State,
    text = ~State,  # Use abbreviations here
    mode = 'text',
    textfont = list(
      size = 16,
      color = "black",
      family = "Arial Black"
    )
  ) %>%
  layout(
    title = list(
      text = paste0(
        "<b>Food Insecurity Across the United States (2022)</b>",
        "<br><sup>Top 5 states highlighted by abbreviation</sup>"
      ),
      x = 0.5,
      y = 0.95,
      font = list(size = 22)
    ),
    annotations = list(
      list(
        x = 0,
        y = -0.15,
        text = "Source: USDA, 2022",
        showarrow = FALSE,
        xref = "paper",
        yref = "paper",
        font = list(size = 12, color = "gray")
      )
    ),
    geo = list(
      scope = "usa",
      projection = list(type = "albers usa"),
      showlakes = TRUE,
      lakecolor = "white"
    ),
    margin = list(t = 90, b = 50)
  )

Food_ggplot

The map highlights a clear regional divide in food security across the United States. States in the South show the highest levels of food insecurity, while many northern and midwestern states experience much lower rates.

This pattern lines up with broader economic challenges in the region, including higher poverty, limited access to affordable nutritious food, and structural barriers that affect household stability. The visual makes it easy to see where support is most needed and reinforces that food insecurity is not evenly distributed across the country. It is a concentrated problem that requires targeted policy attention and stronger local assistance programs.

To understand how poverty influences food-related outcomes, I’ve analyzed state level poverty rates alongside food insecurity indicators. The heat map visually highlights this relationship by showing that states with higher food insecurity are often the same states with higher poverty levels.

This visualization makes the correlation easy for policymakers to recognize, emphasizing that improving economic conditions is essential to reducing food insecurity and malnutrition in the U.S

Poverty_rate <- read.csv('Poverty.csv')
Poverty_rate <- Poverty_rate %>% mutate(State = state.abb[match(state, state.name)]) %>% select(State, everything())
Poverty_rate <- Poverty_rate %>% select(-state)

# Drop rows where the 'State' column is NA
Poverty_rate <- na.omit(Poverty_rate)
sorted_data <- Poverty_rate[order(-Poverty_rate$PovertyRatesPercentOfPopulationBelowPovertyLevel), ]
#head(sorted_data)
kable(sorted_data, format = "html") %>%
  kable_styling(full_width = TRUE) %>%
  scroll_box(width = "100%", height = "400px")

Display the data as a heat map

Poverty_rate$Full_State <- state.name[match(Poverty_rate$State, state.abb)]

# Add top 5 states by poverty (or use all states)
top_states <- Poverty_rate %>%
  arrange(desc(PovertyRatesPercentOfPopulationBelowPovertyLevel)) %>%
  slice(1:5)  # top 5

fig <- plot_geo(Poverty_rate, locationmode = "USA-states") %>%
  add_trace(
    z = ~PovertyRatesPercentOfPopulationBelowPovertyLevel,
    locations = ~State,
    text = ~paste(
      "<b>State:</b> ", Full_State,
      "<br><b>Poverty Rate (%):</b> ", PovertyRatesPercentOfPopulationBelowPovertyLevel
    ),
    hoverinfo = "text",
    colorscale = "Viridis",
    colorbar = list(title = "<b>Poverty Rate (%)</b>")
  ) %>%
  # Add labels for top 5 states
  add_trace(
    data = top_states,
    type = 'scattergeo',
    locationmode = 'USA-states',
    locations = ~State,
    text = ~State,  # show abbreviations
    mode = 'text',
    textfont = list(
      size = 16,
      color = "black",
      family = "Arial Black"
    )
  ) %>%
  layout(
    width = 1000,   # increase map width
    height = 700,   # increase map height
    title = list(
      text = paste0(
        "<b>Poverty Rate Across the United States (2022)</b>",
        "<br><sup>Top 5 states highlighted by abbreviation</sup>"
      ),
      x = 0.5,
      y = 0.95,
      font = list(size = 22)
    ),
    annotations = list(
      list(
        x = 0,
        y = -0.12,
        text = "Source: U.S. Census Bureau, 2022",
        showarrow = FALSE,
        xref = "paper",
        yref = "paper",
        font = list(size = 12, color = "gray")
      )
    ),
    geo = list(
      scope = "usa",
      projection = list(type = "albers usa"),
      showlakes = TRUE,
      lakecolor = "white"
    ),
    margin = list(t = 90, b = 50)
  )

fig

The map shows that poverty is highest in Southern and Southwestern states, with the top five states clearly labeled. These regions face significant economic challenges that contribute to issues like food insecurity. This visualization highlights where policymakers should prioritize interventions and allocate resources to support the populations most in need.

Malnutrition prevalence across age ranges shows how nutritional struggles differ as people move through different stages of life. By comparing children, adults, and seniors, we can see which groups face the highest risks and where support programs may need to focus. This helps identify vulnerabilities and guide policies aimed at improving long-term health and food access.

# Load data
Food_by_age <- read.csv("FoodByAge.csv", stringsAsFactors = FALSE)

# Clean column names
colnames(Food_by_age) <- NULL
colnames(Food_by_age) <- Food_by_age[1, ]
Food_by_age <- Food_by_age[-1, ]

# Set clear names
colnames(Food_by_age)[1] <- "categories"
colnames(Food_by_age)[2] <- "total"

# Clean numeric values by removing commas
Food_by_age$total <- gsub(",", "", Food_by_age$total)
Food_by_age$total <- as.numeric(Food_by_age$total)

# Keep only age categories
Food_age_clean <- Food_by_age %>%
  filter(
    grepl("18|24|25|39|40|54|55|64|65|above|Under|0-17", categories)
  )

# Remove leading spaces
Food_age_clean$categories <- trimws(Food_age_clean$categories)

# Set age order
Food_age_clean$categories <- factor(
  Food_age_clean$categories,
  levels = unique(Food_age_clean$categories)
)

# Create line chart
fig_malnutrition <- plot_ly(
  Food_age_clean,
  x = ~categories,
  y = ~total,
  type = "scatter",
  mode = "lines+markers",
  line = list(width = 3),
  marker = list(size = 8),
  text = ~paste("<b>Age Range:</b>", categories,
                "<br><b>Total:</b>", total),
  hoverinfo = "text"
) %>%
  layout(
    title = list(
      text = "<b>Malnutrition Prevalence Across Age Ranges</b>",
      x = 0.5,
      font = list(size = 20)
    ),
    xaxis = list(title = "Age Range", type = "category"),
    yaxis = list(title = "Affected Population"),
    margin = list(t = 100)
  )

fig_malnutrition

This chart shows that malnutrition prevalence varies across different age groups. Young children show higher vulnerability, indicating the critical need for early nutritional interventions.

Seniors also experience elevated prevalence, reflecting age-related health and dietary challenges. Adults have comparatively lower rates, but disparities remain. These insights emphasize the importance of targeted policies and programs to address nutritional risks at each stage of life.

Food insecurity and poverty are closely linked socioeconomic issues. Higher poverty levels often limit access to sufficient and nutritious food, increasing the risk of food insecurity. Analyzing the correlation between state-level poverty rates and food insecurity prevalence helps identify regions where economic hardship directly impacts access to food, guiding targeted policy interventions.

Lets use a scatter plot to better visualize the correlation by combining both the Poverty_rate data frame and the Food_InSecurity dataframe first.

# Merge datasets
merged_data <- merge(Poverty_rate, Food_InSecurity, by = "State", all = TRUE)

# Identify top 5 states by Food Insecurity
top5 <- merged_data %>%
  arrange(desc(Food.insecurity.Prevalence)) %>%
  slice(1:5)

# Fit linear regression for trend line
fit <- lm(Food.insecurity.Prevalence ~ PovertyRatesPercentOfPopulationBelowPovertyLevel, data = merged_data)

# Add predicted trend line
merged_data <- merged_data %>%
  mutate(trend = predict(fit, newdata = merged_data))

# Scatter plot
fig <- plot_ly(data = merged_data,
               x = ~PovertyRatesPercentOfPopulationBelowPovertyLevel,
               y = ~Food.insecurity.Prevalence,
               type = "scatter",
               mode = "markers",
               marker = list(color = "blue", size = 10),
               text = ~paste("<b>State:</b>", State,
                             "<br><b>Poverty Rate:</b>", PovertyRatesPercentOfPopulationBelowPovertyLevel, "%",
                             "<br><b>Food Insecurity:</b>", Food.insecurity.Prevalence, "%"),
               hoverinfo = "text") %>%
  # Add top 5 state labels
  add_trace(data = top5,
            x = ~PovertyRatesPercentOfPopulationBelowPovertyLevel,
            y = ~Food.insecurity.Prevalence,
            type = "scatter",
            mode = "text",
            text = ~State,  # state abbreviation
            textposition = "top center",
            textfont = list(size = 12, color = "black", family = "Arial Black")) %>%
  # Add linear trend line
  add_trace(data = merged_data,
            x = ~PovertyRatesPercentOfPopulationBelowPovertyLevel,
            y = ~trend,
            type = "scatter",
            mode = "lines",
            line = list(color = "darkgreen", dash = "dash"),
            name = "Trend Line") %>%
  layout(
    title = list(
      text = "<b>Poverty vs. Food Insecurity Across U.S. States (2022)</b><br><sup>Top 5 states with highest food insecurity highlighted in red</sup>",
      x = 0.5,
      font = list(size = 20)
    ),
    xaxis = list(title = "<b>Poverty Rate (% of Population)</b>"),
    yaxis = list(title = "<b>Food Insecurity Prevalence (%)</b>"),
    annotations = list(
      list(
        x = 0,
        y = -0.15,
        text = "Source: USDA & U.S. Census Bureau, 2022",
        showarrow = FALSE,
        xref = "paper",
        yref = "paper",
        font = list(size = 12, color = "gray")
      )
    ),
    margin = list(t = 100, b = 80)
  )

fig

This scatter plot demonstrates a strong association between poverty and food insecurity in the United States. States with higher poverty rates generally exhibit higher food insecurity prevalence, as highlighted by the top five most affected states. The linear trend line confirms this positive correlation, emphasizing that efforts to reduce poverty could directly help improve food access and security. The visualization makes it clear where policymakers should focus resources and interventions.

Food_by_age <- read.csv('FoodByAge.csv')
Increasing_age <- read.csv('Age.csv')

colnames(Food_by_age) <- NULL
# Assign the first row as the new header
colnames(Food_by_age) <- Food_by_age[1, ]

# Remove the first row (since it's now the header)
Food_by_age <- Food_by_age[-1, ]
colnames(Food_by_age)[1] <- "categories"
colnames(Food_by_age)[2] <- "total"

male_data <- subset(Food_by_age, categories == "Male")
female_data <- subset(Food_by_age, categories == "Female")

#Trend of SNAP Participants by Age
Increasing_age <- Increasing_age[Increasing_age$Age != "Total", ]

Increasing_age$Age <- factor(Increasing_age$Age, levels = unique(Increasing_age$Age))

fig <- plot_ly(Increasing_age, x = ~Age, y = ~Number.of.SNAP.participants..Millions., type = 'scatter', mode = 'line')

fig <- fig %>% layout(
  xaxis = list(title = 'Age', type = 'category'),  
  yaxis = list(title = 'Number of SNAP participants (Millions)'),  
  title = 'Trend of SNAP Participants by Age'  
)

fig

This graph illustrates the age distribution of individuals participating in the Supplemental Nutrition Assistance Program (SNAP), providing a valuable benchmark for assessing support needs as individuals age.

Based on the upward trend depicted by the positive linear line in the graph, we observe a steady increase in the number of SNAP participants as ages progress from younger than 5 to 59. However, beyond the age of 59, there is a notable decline in participation rates. This decrease may be attributed to factors such as retirement, changes in financial circumstances, or shifts in eligibility criteria. Further exploration of these factors could provide insights into the observed trend.

Food insecurity remains a critical issue in the United States, disproportionately affecting children, low-income adults, and residents of high-poverty regions.

This analysis shows that children in food-insecure households are more likely to rely on SNAP benefits as they grow into adulthood. Age and gender-specific breakdowns highlight that both boys and girls are at risk, and malnutrition during formative years can reduce long-term educational attainment, workforce productivity, and overall societal health.

Immediate policy interventions are essential to break the cycle of food insecurity. Without action, children growing up without adequate nutrition face higher risks of poor health, reduced cognitive development, and continued dependence on public assistance programs.

Expand Nutrition Programs: Increase access to school meals, summer feeding programs, and child nutrition initiatives in high-risk areas.

Enhance SNAP Support: Broaden eligibility and benefits for young adults transitioning from food-insecure households.

Target Regional Investments: Prioritize funding and community resources for Southern and Southwestern states where food insecurity is highest.

Monitor Outcomes: Implement robust program evaluation to track reductions in food insecurity and improvements in long-term educational and employment outcomes

USDA.gov

World Population Reveiw

Distirbution of SNAP participants by age