Health and Economic Impact of Weather Events in the US

Synopsis

Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severe events can result in fatalities, injuries, and property damage, and preventing such outcomes to the extent possible is a key concern.

This project involves exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. This database tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage.

Data Processing

The analysis was performed on Storm Events Database, provided by National Climatic Data Center. The data is from a comma-separated-value file available here. There is also some documentation of the data available here.

The first step is to read the data into a data frame.

storm <- read.csv(bzfile("repdata-data-StormData.csv.bz2"))

According to the documentation, the PROPDMG and CROPDMG variables are encoded by PROPDMGEXP and CROPDMGEXP variables, respectively. They represent magnitude values, including “H” for hundreds, “K” for thousands, “M”" for millions and “B” for billions. In order to decode PROPDMG and CROPDMG, we created new numeric variables.

storm$PROPMULT <- 1
storm$PROPMULT[storm$PROPDMGEXP =="H"] <- 100
storm$PROPMULT[storm$PROPDMGEXP =="K"] <- 1000
storm$PROPMULT[storm$PROPDMGEXP =="M"] <- 1000000
storm$PROPMULT[storm$PROPDMGEXP =="B"] <- 1000000000

storm$CROPMULT <- 1
storm$CROPMULT[storm$CROPDMGEXP =="H"] <- 100
storm$CROPMULT[storm$CROPDMGEXP =="K"] <- 1000
storm$CROPMULT[storm$CROPDMGEXP =="M"] <- 1000000
storm$CROPMULT[storm$CROPDMGEXP =="B"] <- 1000000000

Results

To find the event types that are most harmful to population health, the number of casualties are aggregated by the event type.

library(plyr)
aggregate_data <- ddply(.data = storm, .variables = .(EVTYPE), fatalities = sum(FATALITIES), injuries = sum(INJURIES), property_damage = sum(PROPDMG * PROPMULT), crop_damage = sum(CROPDMG * CROPMULT), summarize)

population_data <- arrange(aggregate_data, desc(fatalities + injuries))
damage_data <- arrange(aggregate_data, desc(property_damage + crop_damage))

Health impact of weather events

The following plot shows top dangerous weather event types.

library(ggplot2)
ggplot(data = head(population_data, 15), aes(x = factor(EVTYPE), y = (fatalities), fill = EVTYPE)) + geom_bar(stat="identity") + coord_flip() + labs(y = "Fatalities", x = "Event type", title = "Fatalites per event type across US")

ggplot(data = head(population_data, 15), aes(x = factor(EVTYPE), y = (injuries), fill = EVTYPE)) + geom_bar(stat="identity") + coord_flip() + labs(y = "Injuries", x = "Event type", title = "Injuries per event type across US")

Tornadoes cause most number of deaths and injuries among all event types. There are more than 5,000 deaths and more than 10,000 injuries in the last 60 years in US, due to tornadoes. The other event types that are most dangerous with respect to population health are excessive heat and flash floods.

Economic impact of weather events

The following plot shows the most severe weather event types with respect to economic cost that they have costed since 1950s.

ggplot(data = head(damage_data, 15), aes(x = factor(EVTYPE), y = (property_damage + crop_damage), fill = EVTYPE)) + geom_bar(stat="identity") + coord_flip() + labs(y = "Property and crop damage", x = "Event type", title = "Property and crop damage by event type accross the US")

The data shows that floods and hurricane/typhoon cost the largest property damages among weather-related natural diseasters. The most severe weather event in terms of crop damage is the drought. In the last half century, the drought has caused more than 10 billion dollars damage. Other severe crop-damage-causing event types are floods and hails.