Analyzing the Impact of Severe Weather Events in the US
KV
12/23/2024
#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 report analyzes the U.S. National Oceanic and Atmospheric Administration’s (NOAA) Storm Database to determine which types of severe weather events have the greatest impact on public health and economic stability in the United States. The analysis focuses on data from 1950 to November 2011.
We first have to load the dataset into R and process the data for analysis.
# Load 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, unionlibrary(ggplot2)
library(readr)
# Load dataset
data <- read.csv("/home/rstudio/Reproducible Research/week2/repdata_data_StormData1.csv")
# Take a quick look at the dataset
head(data)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE EVTYPE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL TORNADO
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL TORNADO
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL TORNADO
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL TORNADO
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL TORNADO
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL TORNADO
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END COUNTYENDN
## 1 0 0 NA
## 2 0 0 NA
## 3 0 0 NA
## 4 0 0 NA
## 5 0 0 NA
## 6 0 0 NA
## END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES INJURIES PROPDMG
## 1 0 14.0 100 3 0 0 15 25.0
## 2 0 2.0 150 2 0 0 0 2.5
## 3 0 0.1 123 2 0 0 2 25.0
## 4 0 0.0 100 2 0 0 2 2.5
## 5 0 0.0 150 2 0 0 2 2.5
## 6 0 1.5 177 2 0 0 6 2.5
## PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES LATITUDE LONGITUDE
## 1 K 0 3040 8812
## 2 K 0 3042 8755
## 3 K 0 3340 8742
## 4 K 0 3458 8626
## 5 K 0 3412 8642
## 6 K 0 3450 8748
## LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3051 8806 1
## 2 0 0 2
## 3 0 0 3
## 4 0 0 4
## 5 0 0 5
## 6 0 0 6Results
Here, we will present the main results from our analysis of the NOAA database. We will investigate the impact of severe weather events on 1) public health and 2) economy.
Health impact
We aim to answer the question what types of events in the US are most harmful to the health of the population?
# Summarize health impact
health_impact <- data %>%
group_by(EVTYPE) %>%
summarise(
TotalFatalities = sum(FATALITIES, na.rm = TRUE),
TotalInjuries = sum(INJURIES, na.rm = TRUE),
TotalHealthImpact = TotalFatalities + TotalInjuries
) %>%
arrange(desc(TotalHealthImpact))## `summarise()` ungrouping output (override with `.groups` argument)# Plot the top 10 event types by health impact
top_health_impact <- head(health_impact, 10)
ggplot(top_health_impact, aes(x=reorder(EVTYPE, -TotalHealthImpact), y = TotalHealthImpact, fill = EVTYPE)) +
geom_bar(stat = "identity") +
labs(title = "Top 10 Event Types by Health Impact", x = "Event Type", y = "Total Health Impact") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) 
As shown in the plot, the most harmful event type by far is tornado, with a total health impact (total amount of injured people and deaths) of almost 100.000. Tornado is followed by excessive heat and TSTM wind, which both have a total health impact of about 10.000.
Economic impact
We aim to answer the question what types of events have the greatest economic consequences in the US?
# Normalize damage exponents
damage_exp <- function(x) {
if (x %in% c("H", "h")) return(100)
if (x %in% c("K", "k")) return(1000)
if (x %in% c("M", "m")) return(1e6)
if (x %in% c("B", "b")) return(1e9)
if (x %in% c("", "0")) return(1)
return(1) # Default to 1 for unknown values
}
data <- data %>%
mutate(
PROPDMGEXP = sapply(PROPDMGEXP, damage_exp),
CROPDMGEXP = sapply(CROPDMGEXP, damage_exp),
TotalPropDamage = PROPDMG * PROPDMGEXP,
TotalCropDamage = CROPDMG * CROPDMGEXP,
TotalEconomicDamage = TotalPropDamage + TotalCropDamage
)
# Summarize economic impact
economic_impact <- data %>%
group_by(EVTYPE) %>%
summarise(TotalEconomicDamage = sum(TotalEconomicDamage, na.rm = TRUE)) %>%
arrange(desc(TotalEconomicDamage))## `summarise()` ungrouping output (override with `.groups` argument)# Plot the top 10 event types by economic damage
top_economic_impact <- head(economic_impact, 10)
ggplot(top_economic_impact, aes(x = reorder(EVTYPE, -TotalEconomicDamage), y = TotalEconomicDamage / 1e9, fill = EVTYPE)) +
geom_bar(stat = "identity") +
labs(title = "Top 10 Event Types by Economic Damage", x = "Event Type", y = "Total Economic Damage (in Billions)") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
We show that the event with the highest economic impact by far is flooding, followed by hurricane/typhoon and tornado.