Synopsis

This report analyzes data from the U.S. NOAA Storm Database from 1950 to 2011 to identify which types of severe weather events are most harmful to population health and which have the greatest economic consequences. The analysis focuses on the total number of fatalities and injuries as a measure of public health impact, and the total property and crop damage as a measure of economic impact.

Tornadoes were found to be the most harmful to population health, causing more than 96,000 combined injuries and fatalities. Excessive heat and thunderstorm winds were also significant contributors to human harm. In terms of economic losses, floods caused the greatest total damage, followed by hurricanes/typhoons and tornadoes. These insights can help inform emergency preparedness and disaster resource planning for local and national policymakers.

Data Processing

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

data_file <- “C:/Users/Sky.Gonzalez/OneDrive - Dobson Davanzo & Associates/Desktop/repdata_data_StormData.csv.bz2” storm_data <- read_csv(data_file)

str(storm_data)

Select relevant columns

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

Preview first few rows

head(storm_subset)

Define a function to convert damage exponents

convert_exponent <- function(exp) { exp <- toupper(trimws(exp)) ifelse(exp == “K”, 1e3, ifelse(exp == “M”, 1e6, ifelse(exp == “B”, 1e9, 0))) }

Apply conversion

storm_subset <- storm_subset %>% mutate( prop_mult = convert_exponent(PROPDMGEXP), crop_mult = convert_exponent(CROPDMGEXP), prop_damage = PROPDMG * prop_mult, crop_damage = CROPDMG * crop_mult )

Summarize total injuries and fatalities by event type

health_impact <- storm_subset %>% group_by(EVTYPE) %>% summarise( total_fatalities = sum(FATALITIES, na.rm = TRUE), total_injuries = sum(INJURIES, na.rm = TRUE) ) %>% mutate(total_health_impact = total_fatalities + total_injuries) %>% arrange(desc(total_health_impact))

Top 10 most harmful event types

head(health_impact, 10)

Load top 10 events for plotting

top_health <- head(health_impact, 10)

Results

Most Harmful Events to Population Health

The chart below shows the top 10 weather events based on the total number of fatalities and injuries. Tornadoes are by far the most harmful to population health, causing over 96,000 combined injuries and fatalities.

Plot total health impact

ggplot(top_health, aes(x = reorder(EVTYPE, total_health_impact), y = total_health_impact)) + geom_bar(stat = “identity”, fill = “steelblue”) + coord_flip() + labs( title = “Top 10 Weather Events Most Harmful to Population Health (1950–2011)”, x = “Event Type”, y = “Total Fatalities + Injuries” ) + theme_minimal()

Summarize economic damage by event type

economic_impact <- storm_subset %>% group_by(EVTYPE) %>% summarise( total_property_damage = sum(prop_damage, na.rm = TRUE), total_crop_damage = sum(crop_damage, na.rm = TRUE) ) %>% mutate(total_economic_damage = total_property_damage + total_crop_damage) %>% arrange(desc(total_economic_damage))

Top 10 events by total economic damage

head(economic_impact, 10)

Load top 10 for plotting

top_econ <- head(economic_impact, 10)

Events with Greatest Economic Consequences

The next figure presents the top 10 event types by total estimated economic damage, including property and crop damage. Floods cause the most economic loss, followed by hurricanes/typhoons and tornadoes.

Plot total economic damage

ggplot(top_econ, aes(x = reorder(EVTYPE, total_economic_damage), y = total_economic_damage)) + geom_bar(stat = “identity”, fill = “darkred”) + coord_flip() + labs( title = “Top 10 Weather Events with Greatest Economic Impact (1950–2011)”, x = “Event Type”, y = “Total Economic Damage (USD)” ) + scale_y_continuous(labels = dollar) + theme_minimal()