Impact of Severe Weather Events on Public Health and Economy in the United States

Synopsis

This analysis explores the NOAA Storm Database to determine which weather event types are most harmful to population health and which cause the greatest economic damage in the United States. Population health impact is measured using fatalities and injuries, while economic consequences are calculated from property and crop damage. The analysis starts from the raw compressed CSV file and performs necessary data transformations inside this document. Results show that tornadoes are the leading cause of fatalities and injuries. Floods and hurricanes cause the largest economic damage. These findings can help government officials prioritize preparedness and resource allocation.

---

Data Processing

# Load 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)

# Read raw compressed file (must be in working directory)
storm <- read.csv("repdata_data_StormData.csv.bz2")

# Keep only relevant columns
storm <- storm %>%
  select(EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP,
         CROPDMG, CROPDMGEXP)

# Convert damage exponents to numeric multipliers
convert_exp <- function(exp) {
  ifelse(exp %in% c("H", "h"), 1e2,
  ifelse(exp %in% c("K", "k"), 1e3,
  ifelse(exp %in% c("M", "m"), 1e6,
  ifelse(exp %in% c("B", "b"), 1e9, 1))))
}

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

# Calculate total damages
storm$PROP_TOTAL <- storm$PROPDMG * storm$PROP_MULT
storm$CROP_TOTAL <- storm$CROPDMG * storm$CROP_MULT
storm$TOTAL_DAMAGE <- storm$PROP_TOTAL + storm$CROP_TOTAL

---

Results

1️⃣ Events Most Harmful to Population Health

health <- storm %>%
  group_by(EVTYPE) %>%
  summarise(
    Total_Fatalities = sum(FATALITIES),
    Total_Injuries = sum(INJURIES)
  ) %>%
  mutate(Total_Health = Total_Fatalities + Total_Injuries) %>%
  arrange(desc(Total_Health))

top_health <- head(health, 10)
top_health

## # A tibble: 10 × 4
##    EVTYPE            Total_Fatalities Total_Injuries Total_Health
##    <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         1527

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

Conclusion: Tornadoes cause the highest number of combined fatalities and injuries.

---

2️⃣ Events with Greatest Economic Consequences

economic <- storm %>%
  group_by(EVTYPE) %>%
  summarise(Total_Damage = sum(TOTAL_DAMAGE)) %>%
  arrange(desc(Total_Damage))

top_economic <- head(economic, 10)
top_economic

## # A tibble: 10 × 2
##    EVTYPE             Total_Damage
##    <chr>                     <dbl>
##  1 FLOOD             150319678257 
##  2 HURRICANE/TYPHOON  71913712800 
##  3 TORNADO            57352114049.
##  4 STORM SURGE        43323541000 
##  5 HAIL               18758222016.
##  6 FLASH FLOOD        17562129167.
##  7 DROUGHT            15018672000 
##  8 HURRICANE          14610229010 
##  9 RIVER FLOOD        10148404500 
## 10 ICE STORM           8967041360

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

Conclusion: Floods and hurricanes generate the highest economic losses.