Health and Economic Impacts of Severe Weather Events in the United States
Mary Cao
2026-09-08
Synopsis
This analysis examines the health and economic consequences of severe weather events recorded in the U.S. National Oceanic and Atmospheric Administration Storm Database. Population-health impact is evaluated using the total numbers of fatalities and injuries associated with each event type. Economic impact is evaluated using inflation-unadjusted property and crop damage values after converting the magnitude codes into dollar amounts. The results identify the weather-event categories associated with the greatest recorded human and economic harm. These findings may help public officials understand which severe weather hazards have historically produced the largest consequences.
Data Processing
The analysis begins with the original compressed CSV file supplied for the assignment. R can read the bzip2-compressed file directly, so no external preprocessing or manual decompression is required.
library(dplyr)
library(ggplot2)
library(scales)
storm <- read.csv(
bzfile("repdata_data_StormData.csv.bz2"),
stringsAsFactors = FALSE
)
dim(storm)## [1] 902297 37Only the variables required for the health and economic analyses are retained. Event names are converted to uppercase and unnecessary spaces are removed so that capitalization differences do not create separate categories.
storm_analysis <- storm %>%
select(
EVTYPE,
FATALITIES,
INJURIES,
PROPDMG,
PROPDMGEXP,
CROPDMG,
CROPDMGEXP
) %>%
mutate(
EVTYPE = toupper(trimws(EVTYPE)),
PROPDMGEXP = toupper(trimws(PROPDMGEXP)),
CROPDMGEXP = toupper(trimws(CROPDMGEXP))
)
head(storm_analysis)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 0 15 25.0 K 0
## 2 TORNADO 0 0 2.5 K 0
## 3 TORNADO 0 2 25.0 K 0
## 4 TORNADO 0 2 2.5 K 0
## 5 TORNADO 0 2 2.5 K 0
## 6 TORNADO 0 6 2.5 K 0Converting damage values to dollars
The property- and crop-damage variables contain a numeric value and a
separate magnitude code. The codes H, K,
M, and B represent hundreds, thousands,
millions, and billions of dollars. Numeric exponent codes are
interpreted as powers of ten. Blank, unknown, and symbol codes are
conservatively assigned a multiplier of one.
damage_multiplier <- function(x) {
x <- toupper(trimws(as.character(x)))
multiplier <- rep(1, length(x))
multiplier[x == "H"] <- 10^2
multiplier[x == "K"] <- 10^3
multiplier[x == "M"] <- 10^6
multiplier[x == "B"] <- 10^9
numeric_code <- grepl("^[0-9]$", x)
multiplier[numeric_code] <- 10^as.numeric(x[numeric_code])
multiplier
}
storm_analysis <- storm_analysis %>%
mutate(
PROP_MULTIPLIER = damage_multiplier(PROPDMGEXP),
CROP_MULTIPLIER = damage_multiplier(CROPDMGEXP),
PROPERTY_DAMAGE = PROPDMG * PROP_MULTIPLIER,
CROP_DAMAGE = CROPDMG * CROP_MULTIPLIER,
ECONOMIC_DAMAGE = PROPERTY_DAMAGE + CROP_DAMAGE
)
head(storm_analysis)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 0 15 25.0 K 0
## 2 TORNADO 0 0 2.5 K 0
## 3 TORNADO 0 2 25.0 K 0
## 4 TORNADO 0 2 2.5 K 0
## 5 TORNADO 0 2 2.5 K 0
## 6 TORNADO 0 6 2.5 K 0
## PROP_MULTIPLIER CROP_MULTIPLIER PROPERTY_DAMAGE CROP_DAMAGE ECONOMIC_DAMAGE
## 1 1000 1 25000 0 25000
## 2 1000 1 2500 0 2500
## 3 1000 1 25000 0 25000
## 4 1000 1 2500 0 2500
## 5 1000 1 2500 0 2500
## 6 1000 1 2500 0 2500Results
Weather events most harmful to population health
Population-health impact is measured as the combined number of recorded fatalities and injuries. The totals are calculated for each event type, and the ten event types with the greatest combined impact are shown.
health_summary <- storm_analysis %>%
group_by(EVTYPE) %>%
summarise(
Fatalities = sum(FATALITIES, na.rm = TRUE),
Injuries = sum(INJURIES, na.rm = TRUE),
Total_Health_Impact = Fatalities + Injuries,
.groups = "drop"
) %>%
arrange(desc(Total_Health_Impact))
top_health <- head(health_summary, 10)
top_health## # A tibble: 10 × 4
## EVTYPE Fatalities Injuries Total_Health_Impact
## <chr> <dbl> <dbl> <dbl>
## 1 TORNADO 5633 91346 96979
## 2 EXCESSIVE HEAT 1903 6525 8428
## 3 TSTM WIND 504 6957 7461
## 4 FLOOD 470 6789 7259
## 5 LIGHTNING 816 5230 6046
## 6 HEAT 937 2100 3037
## 7 FLASH FLOOD 978 1777 2755
## 8 ICE STORM 89 1975 2064
## 9 THUNDERSTORM WIND 133 1488 1621
## 10 WINTER STORM 206 1321 1527ggplot(
top_health,
aes(
x = reorder(EVTYPE, Total_Health_Impact),
y = Total_Health_Impact
)
) +
geom_col(fill = "firebrick") +
coord_flip() +
scale_y_continuous(labels = comma) +
labs(
title = "Weather Events with the Greatest Health Impact",
x = "Event type",
y = "Total fatalities and injuries"
) +
theme_minimal()
Figure 1. The ten weather-event types associated with the greatest combined number of fatalities and injuries.
The event type associated with the greatest combined health impact is TORNADO, with 96,979 recorded fatalities and injuries. Overall, tornadoes produced the largest combined health burden in the database. Excessive heat, thunderstorm wind, floods, and lightning also produced substantial population-health consequences.
Weather events with the greatest economic consequences
Economic impact is measured as the sum of inflation-unadjusted property and crop damage. The total economic damage is calculated for every event type.
economic_summary <- storm_analysis %>%
group_by(EVTYPE) %>%
summarise(
Property_Damage = sum(PROPERTY_DAMAGE, na.rm = TRUE),
Crop_Damage = sum(CROP_DAMAGE, na.rm = TRUE),
Total_Economic_Damage =
Property_Damage + Crop_Damage,
.groups = "drop"
) %>%
arrange(desc(Total_Economic_Damage))
top_economic <- head(economic_summary, 10)
top_economic## # A tibble: 10 × 4
## EVTYPE Property_Damage Crop_Damage Total_Economic_Damage
## <chr> <dbl> <dbl> <dbl>
## 1 FLOOD 144657709807 5661968450 150319678257
## 2 HURRICANE/TYPHOON 69305840000 2607872800 71913712800
## 3 TORNADO 56947380676. 414953270 57362333946.
## 4 STORM SURGE 43323536000 5000 43323541000
## 5 HAIL 15735267513. 3025954473 18761221986.
## 6 FLASH FLOOD 16822723978. 1421317100 18244041078.
## 7 DROUGHT 1046106000 13972566000 15018672000
## 8 HURRICANE 11868319010 2741910000 14610229010
## 9 RIVER FLOOD 5118945500 5029459000 10148404500
## 10 ICE STORM 3944927860 5022113500 8967041360ggplot(
top_economic,
aes(
x = reorder(EVTYPE, Total_Economic_Damage),
y = Total_Economic_Damage / 10^9
)
) +
geom_col(fill = "steelblue") +
coord_flip() +
scale_y_continuous(labels = comma) +
labs(
title = "Weather Events with the Greatest Economic Impact",
x = "Event type",
y = "Total economic damage (billions of dollars)"
) +
theme_minimal()
Figure 2. The ten weather-event types associated with the greatest combined property and crop damage.
The event type associated with the greatest economic impact is FLOOD, with approximately $150.32 billion in combined property and crop damage. Floods produced the greatest overall economic loss, followed by other destructive events such as hurricanes, tornadoes, and storm surges.
Conclusion
The NOAA Storm Database indicates that the event types producing the largest population-health burden are not necessarily identical to those producing the greatest economic losses. Tornadoes caused the greatest combined number of fatalities and injuries, while floods caused the greatest combined property and crop damage. These results demonstrate the importance of considering both human-health and economic outcomes when evaluating severe-weather hazards.