Impact of Severe Weather Events on Public 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 have the greatest impact on population health and economic damage. The dataset spans from 1950 to 2011 and includes information on fatalities, injuries, and property and crop damage. The data were processed by selecting relevant variables and converting damage values into consistent numerical formats. Aggregated summaries were computed for each event type to determine total health and economic impacts. Results show that tornadoes are the most harmful in terms of fatalities and injuries, while floods and hurricanes cause the greatest economic damage. These findings highlight the need for targeted disaster preparedness strategies. The analysis is fully reproducible and based on the original raw dataset.

Data Processing

Loading required libraries

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)

Loading the raw data

data <- read.csv("repdata_data_StormData.csv")

Selecting relevant variables

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

Converting damage values into numeric form

The damage variables use multipliers such as K (thousand), M (million), and B (billion). These are converted into numeric values to compute total damage.

convert_exp <- function(exp) {
  ifelse(exp == "K", 1e3,
  ifelse(exp == "M", 1e6,
  ifelse(exp == "B", 1e9, 1)))
}

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

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

data$TOTAL_DAMAGE <- data$PROP_TOTAL + data$CROP_TOTAL

Aggregating data for population health impact

health_data <- data %>%
  group_by(EVTYPE) %>%
  summarise(
    fatalities = sum(FATALITIES, na.rm = TRUE),
    injuries = sum(INJURIES, na.rm = TRUE)
  )

health_data$total_health <- health_data$fatalities + health_data$injuries

top_health <- health_data %>%
  arrange(desc(total_health)) %>%
  head(10)

Aggregating data for economic impact

economic_data <- data %>%
  group_by(EVTYPE) %>%
  summarise(
    total_damage = sum(TOTAL_DAMAGE, na.rm = TRUE)
  )

top_economic <- economic_data %>%
  arrange(desc(total_damage)) %>%
  head(10)

Results

Impact on Population Health

The following plot shows the top 10 weather events that have caused the most harm in terms of fatalities and injuries.

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

Tornadoes are observed to have the highest impact on population health, followed by other severe weather events such as excessive heat and floods. This indicates that rapid-onset and high-intensity weather events pose the greatest risks to human life.

Economic Consequences of Weather Events

The following plot shows the top 10 weather events causing the highest economic damage.

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

Floods, hurricanes, and storm surges contribute the most to economic losses, primarily due to large-scale property and infrastructure damage. These events typically affect wide geographic areas and result in costly recovery efforts.

Conclusion

This analysis demonstrates that tornadoes are the most harmful events in terms of public health, while floods and hurricanes result in the greatest economic consequences. Understanding these patterns can help policymakers prioritize disaster management strategies and allocate resources more effectively.