Economic and Population Health Impacts of Severe Weather Events in the United States

Synopsis:

This analysis explores the population health and economic impacts of severe weather events in the United States using data from the NOAA Storm Database. Population health impacts were measured using the total number of fatalities and injuries, while economic impacts were measured using the combination of property and crop damage estimates. Results show that a small number of event types account for the majority of both health-related and economic losses nationwide. In addition, the ranking of the top six event types differs between population health and economic impacts.

Setting Global Options

knitr::opts_chunk$set(echo = TRUE, 
                      warning = FALSE,
                      message = FALSE)

Loading the data

Importing tidyverse and dataset using read.csv().

library(tidyverse)

df <- read.csv("repdata_data_StormData.csv")

Data processing

Date was converted from character to Date class to aggregate correctly.
Column names in dataframe were converted to lower case as good practice.
Event type names were standardized by converting to lowercase and trimming white spaces to ensure consistent aggregation.
Property and crop damage values were converted to dollar amounts using corresponding exponent fields (propdmgexp and cropdmgexp), where k, m and b represent thousands, millions and billions respectively. All other exponents values were treated as having a multiplier of one, as their meaning is unspecified in the dataset documentation.

df$BGN_DATE <- as.Date(df$BGN_DATE, format = "%m/%d/%Y %H:%M:%S")

df <- df %>% 
    rename_with(tolower)

df <- df %>% 
    mutate(evtype = str_trim(tolower(evtype)),
           propdmgexp = str_trim(tolower(propdmgexp)),
           cropdmgexp = str_trim(tolower(cropdmgexp))
           )

df <- df %>% 
    mutate(propdmg_dollars = propdmg * 
               case_when(
                   propdmgexp == "k" ~ 1e3,
                   propdmgexp == "m" ~ 1e6,
                   propdmgexp == "b" ~ 1e9,
                   TRUE ~ 1),
           cropdmg_dollars = cropdmg * 
               case_when(
                   cropdmgexp == "k" ~ 1e3,
                   cropdmgexp == "m" ~ 1e6,
                   cropdmgexp == "b" ~ 1e9,
                   TRUE ~ 1)
           )

Exploratory Analysis (Year plot)

An exploratory analysis of event counts by year was conducted to illustrate increased data coverage in more recent decades.

df$year <- year(df$bgn_date)
df_year <- df %>% 
    group_by(year) %>% 
    summarise(total_events = n())

In the figure below, the number of recorded severe weather events increased substantially after 1990, indicating improved reporting practices in later years. The analysis focuses on aggregated impacts across the dataset but recognizes that earlier years may be under reported.

g_year <- ggplot(df_year, aes(x = year, y = total_events)) +
    geom_point(color = "steelblue",alpha = 0.6) +
    geom_vline(xintercept = 1990, linetype = "dashed") +
    annotate("text", x = 1990, y = max(df_year$total_events) * 0.8,
             label = "1990", hjust = -0.1) +
    labs(x = "Year",
         y = "Number of Recorded Events",
         title = "Number of Severe Weather Events Recorded per Year in the United States")
g_year

Aggregating the original dataframe for population health and economic impacts

Fatalities and injuries were the chosen variables to represent negative population health impacts
Property and crop damage were the chosen variables to represent economic losses.
Total population health impact is the sum of fatalities and injured expressed in thousands.
Total economic losses is the sum of property and crop damages in billions of dollars.

df_health <- df %>% 
    group_by(evtype) %>% 
    summarise(total_fatalities = sum(fatalities, na.rm = TRUE),
              total_injuries = sum(injuries, na.rm = TRUE)) %>% 
    mutate(total_health_impact = (total_fatalities + total_injuries) / 1e3 ) %>% 
    arrange(desc(total_health_impact))

df_econ <- df %>% 
    group_by(evtype) %>% 
    summarise(total_prop_dmg = sum(propdmg_dollars, na.rm = TRUE),
              total_crop_dmg = sum(cropdmg_dollars, na.rm = TRUE)
              ) %>% 
    mutate(total_econ_dmg_billions = (total_prop_dmg + total_crop_dmg) / 1e9)  %>% 
    arrange(desc(total_econ_dmg_billions))

Results

Population Health Impacts of Severe Weather Events

The horizontal bar plot shows the top six weather event types contributing the most fatalities and injuries, with counts expressed in thousands.

plot_df_health <- head(df_health)
g_health <- ggplot(plot_df_health, aes(x = reorder(evtype, total_health_impact), 
                                y = total_health_impact)) +
    geom_col(fill = "steelblue") +
    coord_flip() +
    labs(x = "Weather Event Type",
         y = "Number of Fatalities and Injuries (Thousands)",
         title = "Weather Events with Greatest Population Health Impacts 
         in the United States")
g_health

Economic impacts of Severe Weather Events

The horizontal bar plot shows the top six weather event types that contributed most to property and crop damages in billions of dollars.

plot_df_econ <- head(df_econ)
g_econ <- ggplot(plot_df_econ, aes(x = reorder(evtype, total_econ_dmg_billions),
                               y = total_econ_dmg_billions)) +
    geom_col(fill = "steelblue") +
    coord_flip() +
    labs(x = "Weather Event Type",
         y = "Property and Crop Damages (Billions of Dollars)",
         title = "Weather Events with Greatest Economic Impacts 
         in the United States")
g_econ

Summary

Population Health Impacts

Population health impacts were assessed by aggregating the total number of fatalities and injuries associated with each weather event type. Figure_2 shows the top six contributors to fatalities and injuries in the United States, tornado being the strongest contributor by far. These event types represent the greatest risk to population health in the United States when considering both fatal and non-fatal outcomes.

Economic Impacts

Economic impacts were evaluated using the combined cost of property and crop damage, expressed in billions of dollars. Figure_3 shows the top six event types that had the greatest contributions to economic losses. Flood had the strongest contribution of $150 billion in property and crop damages. These findings highlight the substantial financial burden associated with extreme weather events.