Analysis of Severe Weather Events in the United States: Impact on Population Health and Economic Consequences
Monisha Mohan
May 27, 2024
Synopsis
This report analyzes the U.S. National Oceanic and Atmospheric Administration (NOAA) Storm Database to identify which types of severe weather events have the most significant impact on population health and the economy across the United States. Using data on fatalities, injuries, and property damage from various storm events, we identify the event types that are most harmful to human health and those that have the greatest economic consequences. The findings are presented with summaries and visualizations to aid government and municipal managers in resource allocation for disaster preparedness and response.
Data Processing
# Load necessary libraries
library(dplyr)
library(ggplot2)
library(knitr)
# Download and read the data
url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(url, destfile = "StormData.csv.bz2")
data <- read.csv(bzfile("StormData.csv.bz2"))
# Examine the structure of the data
str(data)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : chr "4/18/1950 0:00:00" "4/18/1950 0:00:00" "2/20/1951 0:00:00" "6/8/1951 0:00:00" ...
## $ BGN_TIME : chr "0130" "0145" "1600" "0900" ...
## $ TIME_ZONE : chr "CST" "CST" "CST" "CST" ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: chr "MOBILE" "BALDWIN" "FAYETTE" "MADISON" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : chr "" "" "" "" ...
## $ BGN_LOCATI: chr "" "" "" "" ...
## $ END_DATE : chr "" "" "" "" ...
## $ END_TIME : chr "" "" "" "" ...
## $ COUNTY_END: num 0 0 0 0 0 0 0 0 0 0 ...
## $ COUNTYENDN: logi NA NA NA NA NA NA ...
## $ END_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ END_AZI : chr "" "" "" "" ...
## $ END_LOCATI: chr "" "" "" "" ...
## $ LENGTH : num 14 2 0.1 0 0 1.5 1.5 0 3.3 2.3 ...
## $ WIDTH : num 100 150 123 100 150 177 33 33 100 100 ...
## $ F : int 3 2 2 2 2 2 2 1 3 3 ...
## $ MAG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ FATALITIES: num 0 0 0 0 0 0 0 0 1 0 ...
## $ INJURIES : num 15 0 2 2 2 6 1 0 14 0 ...
## $ PROPDMG : num 25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...
## $ PROPDMGEXP: chr "K" "K" "K" "K" ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr "" "" "" "" ...
## $ WFO : chr "" "" "" "" ...
## $ STATEOFFIC: chr "" "" "" "" ...
## $ ZONENAMES : chr "" "" "" "" ...
## $ LATITUDE : num 3040 3042 3340 3458 3412 ...
## $ LONGITUDE : num 8812 8755 8742 8626 8642 ...
## $ LATITUDE_E: num 3051 0 0 0 0 ...
## $ LONGITUDE_: num 8806 0 0 0 0 ...
## $ REMARKS : chr "" "" "" "" ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...# Select relevant columns for the analysis
data <- data %>%
select(EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP)
# Convert PROPDMGEXP and CROPDMGEXP to appropriate multipliers
data$PROPDMGEXP <- as.character(data$PROPDMGEXP)
data$CROPDMGEXP <- as.character(data$CROPDMGEXP)
# Define a function to convert exponents to numerical multipliers
convert_exp <- function(exp) {
if (exp %in% c("K", "k")) return(1e3)
if (exp %in% c("M", "m")) return(1e6)
if (exp %in% c("B", "b")) return(1e9)
if (exp %in% c("", "0")) return(1)
return(NA)
}
# Apply the function to the property and crop damage exponents
data$PROPDMGEXP <- sapply(data$PROPDMGEXP, convert_exp)
data$CROPDMGEXP <- sapply(data$CROPDMGEXP, convert_exp)
# Calculate the total damage
data <- data %>%
mutate(PROPDMG = PROPDMG * PROPDMGEXP,
CROPDMG = CROPDMG * CROPDMGEXP,
TOTALDMG = PROPDMG + CROPDMG)
# Summarize the data by event type
summary_data <- data %>%
group_by(EVTYPE) %>%
summarize(TotalFatalities = sum(FATALITIES, na.rm = TRUE),
TotalInjuries = sum(INJURIES, na.rm = TRUE),
TotalDamage = sum(TOTALDMG, na.rm = TRUE)) %>%
arrange(desc(TotalFatalities))
# Display the top event types by fatalities, injuries, and economic damage
top_fatalities <- summary_data %>% arrange(desc(TotalFatalities)) %>% head(10)
top_injuries <- summary_data %>% arrange(desc(TotalInjuries)) %>% head(10)
top_damage <- summary_data %>% arrange(desc(TotalDamage)) %>% head(10)
kable(top_fatalities, caption = "Top 10 Event Types by Fatalities")| EVTYPE | TotalFatalities | TotalInjuries | TotalDamage |
|---|---|---|---|
| TORNADO | 5633 | 91346 | 57352113887 |
| EXCESSIVE HEAT | 1903 | 6525 | 500155700 |
| FLASH FLOOD | 978 | 1777 | 17561699079 |
| HEAT | 937 | 2100 | 403258500 |
| LIGHTNING | 816 | 5230 | 940751520 |
| TSTM WIND | 504 | 6957 | 5038935845 |
| FLOOD | 470 | 6789 | 150319678257 |
| RIP CURRENT | 368 | 232 | 1000 |
| HIGH WIND | 248 | 1137 | 5908617560 |
| AVALANCHE | 224 | 170 | 3721800 |
kable(top_injuries, caption = "Top 10 Event Types by Injuries")| EVTYPE | TotalFatalities | TotalInjuries | TotalDamage |
|---|---|---|---|
| TORNADO | 5633 | 91346 | 57352113887 |
| TSTM WIND | 504 | 6957 | 5038935845 |
| FLOOD | 470 | 6789 | 150319678257 |
| EXCESSIVE HEAT | 1903 | 6525 | 500155700 |
| LIGHTNING | 816 | 5230 | 940751520 |
| HEAT | 937 | 2100 | 403258500 |
| ICE STORM | 89 | 1975 | 8967041360 |
| FLASH FLOOD | 978 | 1777 | 17561699079 |
| THUNDERSTORM WIND | 133 | 1488 | 3897964322 |
| HAIL | 15 | 1361 | 18733221486 |
kable(top_damage, caption = "Top 10 Event Types by Economic Damage")| EVTYPE | TotalFatalities | TotalInjuries | TotalDamage |
|---|---|---|---|
| FLOOD | 470 | 6789 | 150319678257 |
| HURRICANE/TYPHOON | 64 | 1275 | 71913712800 |
| TORNADO | 5633 | 91346 | 57352113887 |
| STORM SURGE | 13 | 38 | 43323541000 |
| HAIL | 15 | 1361 | 18733221486 |
| FLASH FLOOD | 978 | 1777 | 17561699079 |
| DROUGHT | 0 | 4 | 15018672000 |
| HURRICANE | 61 | 46 | 14610229010 |
| RIVER FLOOD | 2 | 2 | 10148404500 |
| ICE STORM | 89 | 1975 | 8967041360 |
Results
Event Types Most Harmful to Population Health
The following table lists the top 10 event types with the highest number of fatalities:
kable(top_fatalities, caption = "Top 10 Event Types by Fatalities")| EVTYPE | TotalFatalities | TotalInjuries | TotalDamage |
|---|---|---|---|
| TORNADO | 5633 | 91346 | 57352113887 |
| EXCESSIVE HEAT | 1903 | 6525 | 500155700 |
| FLASH FLOOD | 978 | 1777 | 17561699079 |
| HEAT | 937 | 2100 | 403258500 |
| LIGHTNING | 816 | 5230 | 940751520 |
| TSTM WIND | 504 | 6957 | 5038935845 |
| FLOOD | 470 | 6789 | 150319678257 |
| RIP CURRENT | 368 | 232 | 1000 |
| HIGH WIND | 248 | 1137 | 5908617560 |
| AVALANCHE | 224 | 170 | 3721800 |
The following table lists the top 10 event types with the highest number of injuries:
kable(top_injuries, caption = "Top 10 Event Types by Injuries")| EVTYPE | TotalFatalities | TotalInjuries | TotalDamage |
|---|---|---|---|
| TORNADO | 5633 | 91346 | 57352113887 |
| TSTM WIND | 504 | 6957 | 5038935845 |
| FLOOD | 470 | 6789 | 150319678257 |
| EXCESSIVE HEAT | 1903 | 6525 | 500155700 |
| LIGHTNING | 816 | 5230 | 940751520 |
| HEAT | 937 | 2100 | 403258500 |
| ICE STORM | 89 | 1975 | 8967041360 |
| FLASH FLOOD | 978 | 1777 | 17561699079 |
| THUNDERSTORM WIND | 133 | 1488 | 3897964322 |
| HAIL | 15 | 1361 | 18733221486 |
Event Types with Greatest Economic Consequences
The following table lists the top 10 event types with the highest economic damage:
kable(top_damage, caption = "Top 10 Event Types by Economic Damage")| EVTYPE | TotalFatalities | TotalInjuries | TotalDamage |
|---|---|---|---|
| FLOOD | 470 | 6789 | 150319678257 |
| HURRICANE/TYPHOON | 64 | 1275 | 71913712800 |
| TORNADO | 5633 | 91346 | 57352113887 |
| STORM SURGE | 13 | 38 | 43323541000 |
| HAIL | 15 | 1361 | 18733221486 |
| FLASH FLOOD | 978 | 1777 | 17561699079 |
| DROUGHT | 0 | 4 | 15018672000 |
| HURRICANE | 61 | 46 | 14610229010 |
| RIVER FLOOD | 2 | 2 | 10148404500 |
| ICE STORM | 89 | 1975 | 8967041360 |
Visualizations
- Fatalities and Injuries by Event Type
plot1 <-ggplot(top_fatalities, aes(x = reorder(EVTYPE, -TotalFatalities), y = TotalFatalities)) +
geom_bar(stat = "identity") +
coord_flip() +
labs(title = "Top 10 Event Types by Fatalities",
x = "Event Type", y = "Total Fatalities")
print(plot1)
plot2<-ggplot(top_injuries, aes(x = reorder(EVTYPE, -TotalInjuries), y = TotalInjuries)) +
geom_bar(stat = "identity") +
coord_flip() +
labs(title = "Top 10 Event Types by Injuries",
x = "Event Type", y = "Total Injuries")
print(plot2)
- Economic Damage by Event Type
ggplot(top_damage, aes(x = reorder(EVTYPE, -TotalDamage), y = TotalDamage)) +
geom_bar(stat = "identity") +
coord_flip() +
labs(title = "Top 10 Event Types by Economic Damage",
x = "Event Type", y = "Total Damage")
Conclusion
This analysis of the NOAA Storm Database highlights that tornadoes are the most harmful event type concerning both fatalities and injuries, while floods cause the greatest economic damage. These insights can help prioritize resources for disaster preparedness and response.