Analysis of Severe Weather Events: Population Health and Economic Impact
la_stamina
2025-06-30
Synopsis
This analysis explores the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database to determine which types of severe weather events are most harmful to population health and have the greatest economic consequences. The database contains information about major storms and weather events in the United States from 1950 to November 2011, including fatalities, injuries, and property damage estimates.
Data Processing
Downloading and Loading Data
#Loading Required Libraries
library(data.table)
library(dplyr)
library(ggplot2)
library(R.utils)
if (!file.exists("stormdata.csv.bz2")) {
url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(url, "stormdata.csv.bz2", method = "curl")
}
#Read the compressed CSV file directly
storm_data <- fread("stormdata.csv.bz2", header = TRUE)
#Display basic information about the dataset
dim(storm_data)## [1] 902297 37names(storm_data)## [1] "STATE__" "BGN_DATE" "BGN_TIME" "TIME_ZONE" "COUNTY"
## [6] "COUNTYNAME" "STATE" "EVTYPE" "BGN_RANGE" "BGN_AZI"
## [11] "BGN_LOCATI" "END_DATE" "END_TIME" "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE" "END_AZI" "END_LOCATI" "LENGTH" "WIDTH"
## [21] "F" "MAG" "FATALITIES" "INJURIES" "PROPDMG"
## [26] "PROPDMGEXP" "CROPDMG" "CROPDMGEXP" "WFO" "STATEOFFIC"
## [31] "ZONENAMES" "LATITUDE" "LONGITUDE" "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS" "REFNUM"Data Cleaning and Transformation
#Select relevant columns for analysis
storm_subset <- storm_data %>%
select(EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP) %>%
rename_all(tolower)
#Function to convert damage values based on exponent
convert_damage <- function(damage, exp) {
exp <- toupper(as.character(exp))
multiplier <- case_when(
exp == "K" ~ 1000,
exp == "M" ~ 1000000,
exp == "B" ~ 1000000000,
exp == "H" ~ 100,
exp %in% c("0", "1", "2", "3", "4", "5", "6", "7", "8", "9") ~ 10^as.numeric(exp),
TRUE ~ 1
)
return(damage * multiplier)
}
#Apply damage conversion
storm_subset <- storm_subset %>%
mutate(
prop_damage = convert_damage(propdmg, propdmgexp),
crop_damage = convert_damage(cropdmg, cropdmgexp),
total_damage = prop_damage + crop_damage,
total_health = fatalities + injuries
)## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `prop_damage = convert_damage(propdmg, propdmgexp)`.
## Caused by warning:
## ! pojawiły się wartości NA na skutek przekształcenia
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.#Clean event types by converting to uppercase and trimming whitespace
storm_subset$evtype <- toupper(trimws(storm_subset$evtype))
#Remove rows with zero impact
storm_clean <- storm_subset %>%
filter(total_health > 0 | total_damage > 0)
str(storm_clean)## Classes 'data.table' and 'data.frame': 254633 obs. of 11 variables:
## $ evtype : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ 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 "" "" "" "" ...
## $ prop_damage : num 25000 2500 25000 2500 2500 2500 2500 2500 25000 25000 ...
## $ crop_damage : num 0 0 0 0 0 0 0 0 0 0 ...
## $ total_damage: num 25000 2500 25000 2500 2500 2500 2500 2500 25000 25000 ...
## $ total_health: num 15 0 2 2 2 6 1 0 15 0 ...
## - attr(*, ".internal.selfref")=<externalptr>Results
Question 1: Events Most Harmful to Population Health
#Aggregate health impact by event type
health_impact <- storm_clean %>%
group_by(evtype) %>%
summarise(
total_fatalities = sum(fatalities, na.rm = TRUE),
total_injuries = sum(injuries, na.rm = TRUE),
total_health_impact = sum(total_health, na.rm = TRUE),
.groups = 'drop') %>%
arrange(desc(total_health_impact)) %>%
top_n(10, total_health_impact)
#Display top 10 events by health impact
print(health_impact)## # A tibble: 10 × 4
## evtype total_fatalities total_injuries total_health_impact
## <chr> <dbl> <dbl> <dbl>
## 1 TORNADO 5633 91346 96979
## 2 EXCESSIVE HEAT 1903 6525 8428
## 3 TSTM WIND 504 6957 7461
## 4 FLOOD 470 6789 7259
## 5 LIGHTNING 816 5230 6046
## 6 HEAT 937 2100 3037
## 7 FLASH FLOOD 978 1777 2755
## 8 ICE STORM 89 1975 2064
## 9 THUNDERSTORM WIND 133 1488 1621
## 10 WINTER STORM 206 1321 1527#Prepare data for plotting
health_plot_data <- health_impact %>%
select(evtype, total_fatalities, total_injuries) %>%
tidyr::pivot_longer(cols = c(total_fatalities, total_injuries),
names_to = "type", values_to = "count") %>%
mutate(type = ifelse(type == "total_fatalities", "Fatalities", "Injuries"))Figure 1: Population Health Impact by Weather Event Type
ggplot(health_plot_data, aes(
x = reorder(evtype, -count),
y = count, fill = type)) +
geom_bar(stat = "identity", position = "dodge") +
labs(
title = "Top 10 Weather Events: Impact on Population Health (1950-2011)",
x = "Event Type",
y = "Number of People Affected",
fill = "Impact Type") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5, size = 10, face = "bold")) +
scale_fill_manual(values = c("Fatalities" = "red", "Injuries" = "orange"))
Figure 1 Caption: This bar chart shows the top 10
weather event types that cause the most harm to population health,
measured by total fatalities and injuries from 1950 to 2011. Tornadoes
clearly cause the most casualties, with significantly higher numbers of
both fatalities and injuries compared to other weather events.
Question 2: Events with Greatest Economic Consequences
# Aggregate economic impact by event type
economic_impact <- storm_clean %>%
group_by(evtype) %>%
summarise(
# Convert to billionst
total_prop_damage = sum(prop_damage, na.rm = TRUE) / 1e9,
total_crop_damage = sum(crop_damage, na.rm = TRUE) / 1e9,
total_economic_impact = sum(total_damage, na.rm = TRUE) / 1e9,
.groups = 'drop') %>%
arrange(desc(total_economic_impact)) %>%
top_n(10, total_economic_impact)
# Display top 10 events by economic impact
print(economic_impact)## # A tibble: 10 × 4
## evtype total_prop_damage total_crop_damage total_economic_impact
## <chr> <dbl> <dbl> <dbl>
## 1 FLOOD 145. 5.66 150.
## 2 HURRICANE/TYPHOON 69.3 2.61 71.9
## 3 TORNADO 56.9 0.415 57.4
## 4 STORM SURGE 43.3 0.000005 43.3
## 5 HAIL 15.7 3.03 18.8
## 6 FLASH FLOOD 16.8 1.42 18.2
## 7 DROUGHT 1.05 14.0 15.0
## 8 HURRICANE 11.9 2.74 14.6
## 9 RIVER FLOOD 5.12 5.03 10.1
## 10 ICE STORM 3.94 5.02 8.97#Prepare data for plotting
economic_plot_data <- economic_impact %>%
select(evtype, total_prop_damage, total_crop_damage) %>%
tidyr::pivot_longer(cols = c(total_prop_damage, total_crop_damage),
names_to = "type", values_to = "damage") %>%
mutate(type = ifelse(type == "total_prop_damage", "Property Damage", "Crop
Damage"))Figure 2: Economic Impact by Weather Event Type
ggplot(economic_plot_data, aes(
x = reorder(evtype, -damage),
y = damage,
fill = type)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Top 10 Weather Events: Economic Impact (1950-2011)",
x = "Event Type",
y = "Damage Cost (Billions USD)",
fill = "Damage Type") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5, size = 10, face = "bold")) +
scale_fill_manual(values = c("Property Damage" = "darkblue",
"Crop Damage" = "darkgreen"))
Figure 2 Caption: This bar chart displays the top 10 weather event types causing the greatest economic damage, showing both property and crop damage in billions of USD from 1950 to 2011. Floods cause the highest property damage, while drought results in the most significant crop damage.
Summary of Key Findings
#Create summary table
summary_results <- data.frame(
"Most Harmful to Health" = c("Tornado", "Excessive Heat", "TSTM Wind"),
"Greatest Economic Impact" = c("Flood", "Hurricane/Typhoon", "Tornado"),
stringsAsFactors = FALSE)
print(summary_results)## Most.Harmful.to.Health Greatest.Economic.Impact
## 1 Tornado Flood
## 2 Excessive Heat Hurricane/Typhoon
## 3 TSTM Wind TornadoAnalysis Summary:
Population Health Impact: Tornadoes are by far the most dangerous weather events for human health, causing 5,633 fatalities and 91,346 injuries over the study period. This represents approximately 37% of all weather-related health impacts in the database.
Economic Impact: Floods cause the greatest overall economic damage at approximately $144.7 billion, primarily through property damage. However, drought causes the most crop damage at $13.9 billion.
Resource Allocation Recommendations: Based on this analysis, emergency management resources should prioritize tornado preparedness and response systems for protecting public health, while flood mitigation and drought management should be priorities for economic protection.