Impact of Severe Weather Events on Health and Economy in the U.S.

Synopsis

This analysis explores the U.S. NOAA Storm Database to identify which types of severe weather events are most harmful to population health and which result in the greatest economic damage. Population health impact is measured using fatalities and injuries, while economic consequences are assessed using property and crop damage. The results show that a small number of event types account for the majority of health and economic losses across the United States.

Data Processing

library(dplyr)

## Warning: package 'dplyr' was built under R version 4.4.3

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.4.3

# Load data (raw file)
storm <- read.csv("repdata_data_StormData.csv")

# Select relevant variables
storm_sub <- storm %>%
  select(EVTYPE, FATALITIES, INJURIES,
         PROPDMG, PROPDMGEXP,
         CROPDMG, CROPDMGEXP)

# Function to convert exponent
convert_exp <- function(exp) {
  ifelse(exp == "K", 1e3,
  ifelse(exp == "M", 1e6,
  ifelse(exp == "B", 1e9, 0)))
}

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

storm_sub$PROP_COST <- storm_sub$PROPDMG * storm_sub$PROP_MULT
storm_sub$CROP_COST <- storm_sub$CROPDMG * storm_sub$CROP_MULT

health_impact <- storm_sub %>%
  group_by(EVTYPE) %>%
  summarise(Health = sum(FATALITIES + INJURIES, na.rm = TRUE)) %>%
  arrange(desc(Health)) %>%
  slice(1:10)

Results

The analysis shows that tornado-related events cause the greatest harm to population health, as measured by the combined number of fatalities and injuries. In contrast, floods and related water events are responsible for the largest economic losses, driven primarily by property damage. These results indicate that different types of severe weather events dominate health and economic impacts in the United States.

Question 1. Which types of events are most harmful with respect to population health?

Tornado-related events are the most harmful to population health, based on the combined total of fatalities and injuries.

health_impact <- storm_sub %>%
  group_by(EVTYPE) %>%
  summarise(Health = sum(FATALITIES + INJURIES, na.rm = TRUE)) %>%
  arrange(desc(Health)) %>%
  slice(1:10)

Figure 1: Population Health Impact

ggplot(health_impact,
       aes(x = reorder(EVTYPE, Health), y = Health)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(title = "Top 10 Weather Events Harmful to Population Health",
       x = "Event Type",
       y = "Total Fatalities and Injuries")

Question 2. Which types of events have the greatest economic consequences? Flood-related events have the greatest economic consequences, based on the combined total of property and crop damage (after converting damage exponents).

economic_impact <- storm_sub %>%
  group_by(EVTYPE) %>%
  summarise(Economic = sum(PROP_COST + CROP_COST, na.rm = TRUE)) %>%
  arrange(desc(Economic)) %>%
  slice(1:10)

Figure 2: Economic Consequences

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