Synopsis

This analysis explores the NOAA Storm Database to identify which types of severe weather events have the greatest impacts on population health and the U.S. economy. Using the complete raw dataset provided by NOAA, we aggregate injuries, fatalities, and property and crop damages by event type. The results highlight a small number of event categories that account for the majority of harm. These findings can help government and municipal decision-makers prioritize preparedness and mitigation efforts.

Data Processing

library(dplyr)
library(ggplot2)
library(readr)

Load raw data

storm <- read_csv(
"repdata_data_StormData.csv",
col_types = cols()
)

Select relevant variables

storm2 <- storm %>%
select(EVTYPE, FATALITIES, INJURIES,
PROPDMG, PROPDMGEXP,
CROPDMG, CROPDMGEXP)

Convert damage exponents to numeric values

exp_map <- function(x) {
case_when(
x %in% c("K","k") ~ 1e3,
x %in% c("M","m") ~ 1e6,
x %in% c("B","b") ~ 1e9,
TRUE ~ 1
)
}

storm3 <- storm2 %>%
mutate(
prop_damage = PROPDMG * exp_map(PROPDMGEXP),
crop_damage = CROPDMG * exp_map(CROPDMGEXP),
health_impact = FATALITIES + INJURIES,
economic_impact = prop_damage + crop_damage
)

Results

Impact on Population Health

health_summary <- storm3 %>%
group_by(EVTYPE) %>%
summarise(total_health = sum(health_impact, na.rm = TRUE)) %>%
arrange(desc(total_health)) %>%
slice(1:10)

ggplot(health_summary,
aes(x = reorder(EVTYPE, total_health),
y = total_health)) +
geom_col(fill = "steelblue") +
coord_flip() +
labs(
title = "Top 10 Weather Events by Impact on Population Health",
x = "Event Type",
y = "Fatalities + Injuries"
)

Figure 1.Tornadoes dominate population health impacts in the United States, followed by excessive heat and flooding events.

Economic Consequences

economic_summary <- storm3 %>%
group_by(EVTYPE) %>%
summarise(total_damage = sum(economic_impact, na.rm = TRUE)) %>%
arrange(desc(total_damage)) %>%
slice(1:10)

ggplot(economic_summary,
aes(x = reorder(EVTYPE, total_damage),
y = total_damage / 1e9)) +
geom_col(fill = "darkred") +
coord_flip() +
labs(
title = "Top 10 Weather Events by Economic Damage",
x = "Event Type",
y = "Total Damage (Billion USD)"
)

Figure 2.Floods and hurricanes are responsible for the greatest economic losses, primarily due to widespread property and crop damage.

Conclusion

Severe weather events vary substantially in how they affect society. Tornadoes pose the greatest threat to population health, while floods and hurricanes cause the most economic damage. Understanding these distinctions is essential for allocating emergency preparedness resources and improving resilience strategies at the national and local levels.