NOAA Storm Database: Impact of Severe Weather Events

Synopsis

This analysis explores the U.S. NOAA Storm Database to determine which weather events are most harmful to population health and which have the greatest economic consequences. Population health impact is measured using fatalities and injuries, while economic consequences are measured using property and crop damage. The results show that tornadoes are the most harmful to population health, while floods and hurricanes cause the greatest economic damage.

Data Processing

library(dplyr)
library(ggplot2)

Load Data

if(!file.exists("StormData.csv.bz2")) {
  download.file(
    "https://d396qusza40orc.cloudfront.net/repdata/data/StormData.csv.bz2",
    "StormData.csv.bz2"
  )
}

data <- read.csv("StormData.csv.bz2", stringsAsFactors = FALSE)

## Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec,
## : EOF within quoted string

Select Relevant Variables

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

Convert Damage Exponents

convert_exp <- function(exp) {
  if (exp %in% c("H","h")) return(1e2)
  if (exp %in% c("K","k")) return(1e3)
  if (exp %in% c("M","m")) return(1e6)
  if (exp %in% c("B","b")) return(1e9)
  return(1)
}

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

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

Aggregate Data

health <- storm %>%
  group_by(EVTYPE) %>%
  summarise(
    fatalities = sum(FATALITIES, na.rm = TRUE),
    injuries = sum(INJURIES, na.rm = TRUE)
  ) %>%
  mutate(total_health = fatalities + injuries) %>%
  arrange(desc(total_health))

economic <- storm %>%
  group_by(EVTYPE) %>%
  summarise(
    property = sum(PROP_TOTAL, na.rm = TRUE),
    crop = sum(CROP_TOTAL, na.rm = TRUE)
  ) %>%
  mutate(total_damage = property + crop) %>%
  arrange(desc(total_damage))

Results

Most Harmful Events to Population Health

top_health <- head(health, 10)

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

Tornadoes are the most harmful events to population health.

---

Events with Greatest Economic Consequences

top_economic <- head(economic, 10)

ggplot(top_economic, aes(x = reorder(EVTYPE, total_damage), y = total_damage/1e9)) +
  geom_bar(stat = "identity", fill = "darkred") +
  coord_flip() +
  labs(
    title = "Top 10 Weather Events (Economic Damage)",
    x = "Event Type",
    y = "Damage (Billion USD)"
  )

Floods and hurricanes cause the greatest economic damage.

Conclusion

Tornadoes have the highest impact on human health, while floods and hurricanes cause the largest economic losses. These insights are useful for disaster preparedness and resource allocation.