Impact of Severe Weather Events on Public Health and Economy

Synopsis

This report analyzes the U.S. National Oceanic and Atmospheric Administration (NOAA) Storm Database to determine which severe weather events are most harmful to population health and which have the greatest economic consequences across the United States. The raw dataset spans from 1950 to November 2011. Population health impact is measured using total fatalities and injuries. Economic impact is measured using total property and crop damage. Damage exponent values were converted into numeric multipliers before calculating losses. Results show that tornadoes are the most harmful to population health, while floods create the greatest economic damage. These findings may help guide disaster planning and emergency resource allocation.

Data Processing

library(dplyr)
library(ggplot2)

Load Raw Data

storm <- read.csv("repdata_data_StormData.csv.bz2", stringsAsFactors = FALSE)

Keep Only Required Variables

storm <- storm[, c("EVTYPE",
                   "FATALITIES",
                   "INJURIES",
                   "PROPDMG",
                   "PROPDMGEXP",
                   "CROPDMG",
                   "CROPDMGEXP")]

Convert Damage Exponents

convert_exp <- function(x) {
  x <- toupper(x)

  ifelse(x == "H", 1e2,
  ifelse(x == "K", 1e3,
  ifelse(x == "M", 1e6,
  ifelse(x == "B", 1e9, 1))))
}

Calculate Total Damage

storm$PROP_MULT <- convert_exp(storm$PROPDMGEXP)
storm$CROP_MULT <- convert_exp(storm$CROPDMGEXP)

storm$PROP_TOTAL <- storm$PROPDMG * storm$PROP_MULT
storm$CROP_TOTAL <- storm$CROPDMG * storm$CROP_MULT

storm$HEALTH_TOTAL <- storm$FATALITIES + storm$INJURIES
storm$ECON_TOTAL <- storm$PROP_TOTAL + storm$CROP_TOTAL

Summarize by Event Type

health <- storm %>%
  group_by(EVTYPE) %>%
  summarise(total_health = sum(HEALTH_TOTAL, na.rm = TRUE)) %>%
  arrange(desc(total_health))

economic <- storm %>%
  group_by(EVTYPE) %>%
  summarise(total_economic = sum(ECON_TOTAL, na.rm = TRUE)) %>%
  arrange(desc(total_economic))

Results

Which Events Are Most Harmful to Population Health?

top_health <- head(health, 10)

ggplot(top_health,
       aes(x = reorder(EVTYPE, total_health),
           y = total_health)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(title = "Top 10 Most Harmful Weather Events",
       x = "Event Type",
       y = "Fatalities + Injuries")

Top 10 weather events causing the greatest number of combined injuries and fatalities in the United States.

Tornadoes are the most harmful weather events to population health, causing the highest combined total of injuries and fatalities.

Which Events Have the Greatest Economic Consequences?

top_economic <- head(economic, 10)

ggplot(top_economic,
       aes(x = reorder(EVTYPE, total_economic),
           y = total_economic)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(title = "Top 10 Costliest Weather Events",
       x = "Event Type",
       y = "Economic Damage (USD)")

Top 10 weather events causing the greatest total property and crop damage in the United States.

Floods have the greatest economic consequences, followed by hurricanes, storm surge events, and tornadoes.

Conclusion

Across the United States, tornadoes are the most harmful severe weather events with respect to population health. Floods cause the greatest economic damage. These findings suggest that preparedness efforts should prioritize tornado warning systems, emergency medical response, and flood prevention infrastructure.