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

Synopsis

This report explores the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database to assess the impact of severe weather events on population health and the economy across the United States. The data span from 1950 to 2011 and include information on fatalities, injuries, property damage, and crop damage. This analysis identifies the types of events that are most harmful to population health and have the greatest economic consequences. The findings are intended to support decision-makers in allocating resources for disaster preparedness.

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)
install.packages("readr")

## package 'readr' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\Mensch\AppData\Local\Temp\RtmpkFe1nn\downloaded_packages

library(readr)

Load the data

storm_data <- read.csv("repdata_data_StormData.csv", stringsAsFactors = FALSE)

Select relevant columns

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

Convert exponent columns to actual multipliers

exp_convert <- function(exp) {
  if (exp %in% c("h", "H")) return(100)
  else if (exp %in% c("k", "K")) return(1000)
  else if (exp %in% c("m", "M")) return(1e6)
  else if (exp %in% c("b", "B")) return(1e9)
  else return(1)
}

storm_data$PROPDMGEXP <- sapply(storm_data$PROPDMGEXP, exp_convert)
storm_data$CROPDMGEXP <- sapply(storm_data$CROPDMGEXP, exp_convert)

Calculate actual damage

storm_data <- storm_data %>%
  mutate(property_damage = PROPDMG * PROPDMGEXP,
         crop_damage = CROPDMG * CROPDMGEXP)

Results

1. Events Most Harmful to Population Health

health_impact <- storm_data %>%
  group_by(EVTYPE) %>%
  summarise(Fatalities = sum(FATALITIES, na.rm = TRUE),
            Injuries = sum(INJURIES, na.rm = TRUE)) %>%
  mutate(Total = Fatalities + Injuries) %>%
  arrange(desc(Total)) %>%
  head(10)

Plot

ggplot(health_impact, aes(x = reorder(EVTYPE, Total), y = Total)) +
  geom_col(fill = "firebrick") +
  coord_flip() +
  labs(title = "Top 10 Most Harmful Weather Events to Population Health",
       x = "Event Type", y = "Total (Fatalities + Injuries)")

2. Events with the Greatest Economic Consequences

economic_impact <- storm_data %>%
  group_by(EVTYPE) %>%
  summarise(Property = sum(property_damage, na.rm = TRUE),
            Crop = sum(crop_damage, na.rm = TRUE)) %>%
  mutate(Total = Property + Crop) %>%
  arrange(desc(Total)) %>%
  head(10)

Plot

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

Conclusion

Because they cause the greatest injuries and fatalities, tornadoes are the most detrimental natural disasters to public health. Hurricanes, droughts, and floods cause the most financial damage from an economic perspective. These findings give emergency planners and policymakers valuable information to help them concentrate their resources on reducing the impact of these severe weather occurrences.