Health and Economic Impact of Weather Events in the US
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.
Synopsis
The analysis on the storm event database revealed that tornadoes are the most dangerous weather event to the population health. The second most dangerous event type is the excessive heat. The economic impact of weather events was also analyzed. Flash floods and thunderstorm winds caused billions of dollars in property damages between 1950 and 2011. The largest crop damage caused by drought, followed by flood and hails.
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.
load packages and read the data into a data frame
library(R.utils)## Loading required package: R.oo## Loading required package: R.methodsS3## R.methodsS3 v1.8.2 (2022-06-13 22:00:14 UTC) successfully loaded. See ?R.methodsS3 for help.## R.oo v1.27.1 (2025-05-02 21:00:05 UTC) successfully loaded. See ?R.oo for help.##
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## cat, commandArgs, getOption, isOpen, nullfile, parse, use, warningslibrary(dplyr)##
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## intersect, setdiff, setequal, unionlibrary(tidyr)##
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## extractlibrary(ggplot2)
library(gridExtra)##
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## combinelibrary(xtable)URL <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
if(!file.exists("repdata_data_StormData.csv.bz2")){
download.file(URL, destfile = "repdata_data_StormData.csv.bz2")
}
if(file.exists("repdata_data_StormData.csv.bz2")){
bunzip2("repdata_data_StormData.csv.bz2", overwrite = TRUE)
}
raw.data <- read.csv("/Users/huangkeyi/Desktop/r-coursera/repdata_data_StormData.csv.bz2")
names_1 <- names(raw.data)The names of the raw data columns are STATE_, BGN_DATE, BGN_TIME, TIME_ZONE, COUNTY, COUNTYNAME, STATE, EVTYPE, BGN_RANGE, BGN_AZI, BGN_LOCATI, END_DATE, END_TIME, COUNTY_END, COUNTYENDN, END_RANGE, END_AZI, END_LOCATI, LENGTH, WIDTH, F, MAG, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP, WFO, STATEOFFIC, ZONENAMES, LATITUDE, LONGITUDE, LATITUDE_E, LONGITUDE, REMARKS, REFNUM
Processing code
grouping and summarising the data by the event type
cleaned_data <- raw.data %>%
group_by(EVTYPE) %>%
summarize(
Total_Fatalities = sum(FATALITIES, na.rm = TRUE),
Total_PropDmg = sum(PROPDMG, na.rm = TRUE),
Total_CropDmg = sum(CROPDMG, na.rm = TRUE),
Total_Injuries = sum(INJURIES, na.rm = TRUE)
) Results
top_fatalities <- cleaned_data %>%
arrange(desc(Total_Fatalities))
top_injures <- cleaned_data %>%
arrange(desc(Total_Injuries))
top_cropdmg <- cleaned_data %>%
arrange(desc(Total_CropDmg))
top_propdmg <- cleaned_data %>%
arrange(desc(Total_PropDmg))
head(top_fatalities)## # A tibble: 6 × 5
## EVTYPE Total_Fatalities Total_PropDmg Total_CropDmg Total_Injuries
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 TORNADO 5633 3212258. 100019. 91346
## 2 EXCESSIVE HEAT 1903 1460 494. 6525
## 3 FLASH FLOOD 978 1420125. 179200. 1777
## 4 HEAT 937 298. 663. 2100
## 5 LIGHTNING 816 603352. 3581. 5230
## 6 TSTM WIND 504 1335966. 109203. 6957head(top_propdmg)## # A tibble: 6 × 5
## EVTYPE Total_Fatalities Total_PropDmg Total_CropDmg Total_Injuries
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 TORNADO 5633 3212258. 100019. 91346
## 2 FLASH FLOOD 978 1420125. 179200. 1777
## 3 TSTM WIND 504 1335966. 109203. 6957
## 4 FLOOD 470 899938. 168038. 6789
## 5 THUNDERSTORM WIND 133 876844. 66791. 1488
## 6 HAIL 15 688693. 579596. 1361from analyzing the data, the results showed that Tornadoes result in the highest fatalities with estimate count of 5633, additionally, it results in the highest injuries count and property damage with counts of 91346 and 3212258.2 respectively.
However, hails resulted in the highest crop damage among all events across US with estimate count of 579596.28.
Which type of events are most harmful to population health?
The following plots shows the top five events that are most harmful to population health and economy.
plot_1 <- ggplot(top_fatalities[1:5, ], aes(x = reorder(EVTYPE, -Total_Fatalities), y = Total_Fatalities)) +
geom_point(stat = "identity", color = "red", size=3) +
scale_y_continuous(limits = range(top_fatalities$Total_Fatalities+200)) +
geom_text(aes(label = Total_Fatalities), vjust = -0.5, size = 3) + # Add values on top of bars
labs(x = "Event Type", y = "Total Fatalities", title = "Top five fatal events", size=10) +
theme(axis.text.x = element_text(size = 6))
plot_2 <- ggplot(top_injures[1:5, ], aes(x = reorder(EVTYPE, -Total_Injuries), y = Total_Injuries)) +
geom_point(stat = "identity", color = "skyblue", size= 3) +
geom_text(aes(label = Total_Injuries), vjust = -0.5, size = 3) + # Add values on top of bars
scale_y_continuous(limits = range(top_injures$Total_Injuries)) +
labs(x = "Event Type", y = "Total Injuries", title = "Top five injury-causing events") +
theme(axis.text.x = element_text(size = 6)) # Rotate x-axis labels for better readability
grid.arrange(plot_1, plot_2, ncol = 2)
which type of events have the greatest economic consequences?
plot_3 <- ggplot(top_cropdmg[1:5, ], aes(x = reorder(EVTYPE, -Total_CropDmg), y = Total_CropDmg)) +
geom_point(stat = "identity", color = "red") +
scale_y_continuous(limits = range(top_cropdmg$Total_CropDmg+1000)) +
geom_text(aes(label = Total_CropDmg), vjust = -0.5, size = 3) + # Add values on top of bars
labs(x = "Event Type", y = "Total crop damage", title = "Top five crop-damaging events", size=10) +
theme(axis.text.x = element_text(size = 6))
plot_4 <- ggplot(top_propdmg[1:5, ], aes(x = reorder(EVTYPE, -Total_PropDmg), y = Total_PropDmg)) +
geom_point(stat = "identity", color = "skyblue") +
geom_text(aes(label = Total_PropDmg), vjust = -0.5, size = 3) + # Add values on top of bars
labs(x = "Event Type", y = "Total Property Damage", title = "Top five property damaging events") +
theme(axis.text.x = element_text(size = 6)) # Rotate x-axis labels for better readability
grid.arrange(plot_3, plot_4, ncol = 2)