Analysis of Severe Weather Events in the United States
Jacob Robinson
2025-05-08
Synopsis
This analysis examines the NOAA Storm Database to identify the most harmful weather events in the United States with respect to population health and economic impact. The data shows that tornadoes cause the most fatalities and injuries, while floods and hurricanes cause the greatest economic damage. The analysis processes raw data from the NOAA database, focusing on fatalities, injuries, property damage, and crop damage. The results are presented through summary tables and visualizations to help government and municipal managers prioritize resources for severe weather event preparation.
Data Processing
First, we’ll load and process the raw data from the compressed CSV file:
# Read the compressed data file
storm_data <- read.csv("repdata_data_StormData.csv.bz2")
# Convert property and crop damage values to actual dollar amounts
storm_data <- storm_data %>%
mutate(
# Convert property damage
prop_damage = case_when(
PROPDMGEXP == "K" ~ PROPDMG * 1000,
PROPDMGEXP == "M" ~ PROPDMG * 1000000,
PROPDMGEXP == "B" ~ PROPDMG * 1000000000,
TRUE ~ PROPDMG
),
# Convert crop damage
crop_damage = case_when(
CROPDMGEXP == "K" ~ CROPDMG * 1000,
CROPDMGEXP == "M" ~ CROPDMG * 1000000,
CROPDMGEXP == "B" ~ CROPDMG * 1000000000,
TRUE ~ CROPDMG
)
)
# Clean up event types
storm_data$EVTYPE <- str_to_title(storm_data$EVTYPE)Results
Impact on Population Health
Let’s analyze which events are most harmful to population health by examining fatalities and injuries:
# Calculate total fatalities and injuries by event type
health_impact <- storm_data %>%
group_by(EVTYPE) %>%
summarise(
total_fatalities = sum(FATALITIES),
total_injuries = sum(INJURIES),
total_health_impact = total_fatalities + total_injuries
) %>%
arrange(desc(total_health_impact)) %>%
head(10)
# Create a plot of the top 10 events by health impact
health_plot <- health_impact %>%
pivot_longer(
cols = c(total_fatalities, total_injuries),
names_to = "impact_type",
values_to = "count"
) %>%
ggplot(aes(x = reorder(EVTYPE, count), y = count, fill = impact_type)) +
geom_bar(stat = "identity", position = "stack") +
coord_flip() +
labs(
title = "Top 10 Weather Events by Health Impact",
x = "Event Type",
y = "Number of People Affected",
fill = "Impact Type"
) +
theme_minimal() +
scale_fill_manual(
values = c("total_fatalities" = "red", "total_injuries" = "orange"),
labels = c("Fatalities", "Injuries")
)
print(health_plot)
(Caption: This plot shows the top 10 weather events by health impact, where the total bar is the total number affected, and the % orange shows how much of that total was injuries, vice versa for red/fatalities.)
Economic Impact
Now let’s analyze which events have the greatest economic consequences:
# Calculate total economic damage by event type
economic_impact <- storm_data %>%
group_by(EVTYPE) %>%
summarise(
total_prop_damage = sum(prop_damage, na.rm = TRUE),
total_crop_damage = sum(crop_damage, na.rm = TRUE),
total_damage = total_prop_damage + total_crop_damage
) %>%
arrange(desc(total_damage)) %>%
head(10)
# Create a plot of the top 10 events by economic impact
economic_plot <- economic_impact %>%
pivot_longer(
cols = c(total_prop_damage, total_crop_damage),
names_to = "damage_type",
values_to = "amount"
) %>%
ggplot(aes(x = reorder(EVTYPE, amount), y = amount/1e9, fill = damage_type)) +
geom_bar(stat = "identity", position = "stack") +
coord_flip() +
labs(
title = "Top 10 Weather Events by Economic Impact",
x = "Event Type",
y = "Damage (Billions of Dollars)",
fill = "Damage Type"
) +
theme_minimal() +
scale_fill_manual(
values = c("total_prop_damage" = "blue", "total_crop_damage" = "green"),
labels = c("Property Damage", "Crop Damage")
)
print(economic_plot)
(Caption: This plot shows the top 10 weather events by economic impact, where the total bar is the total damage in USD billions, and the % blue shows how much of that total was crop damage, vice versa for green/property damage.)
Summary Tables
Here are the summary tables for both health and economic impacts:
# Health impact summary
print("Top 10 Events by Health Impact:")## [1] "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# Economic impact summary
print("Top 10 Events by Economic Impact:")## [1] "Top 10 Events by Economic Impact:"print(economic_impact)## # A tibble: 10 × 4
## EVTYPE total_prop_damage total_crop_damage total_damage
## <chr> <dbl> <dbl> <dbl>
## 1 Flood 144657709807 5661968450 150319678257
## 2 Hurricane/Typhoon 69305840000 2607872800 71913712800
## 3 Tornado 56925660790. 414953270 57340614060.
## 4 Storm Surge 43323536000 5000 43323541000
## 5 Hail 15727367053. 3025537890 18752904943.
## 6 Flash Flood 16140812067. 1421317100 17562129167.
## 7 Drought 1046106000 13972566000 15018672000
## 8 Hurricane 11868319010 2741910000 14610229010
## 9 River Flood 5118945500 5029459000 10148404500
## 10 Ice Storm 3944927860 5022113500 8967041360The analysis reveals that tornadoes are the most harmful weather events with respect to population health, causing both the highest number of fatalities and injuries. In terms of economic impact, floods and hurricanes cause the most significant damage, affecting both property and crops. These findings can help government and municipal managers prioritize resources for severe weather event preparation and response.