Required Packages

Synopsis

Severe weather events pose significant risks to both population health and economic stability in the United States. This analysis explores data from the NOAA Storm Database to identify which types of weather events cause the most harm to human health and which result in the greatest economic losses.Population health impacts are measured using the total number of injuries and fatalities, while economic consequences are assessed through property and crop damage costs.

The analysis aggregates data across all U.S. states and territories.Results show that tornadoes are the most harmful event type with respect to injuries and fatalities.Flood-related events account for the largest economic losses nationwide. These findings highlight the importance of prioritizing preparedness and mitigation strategies for high-impact event types.The results can help government and municipal managers better allocate resources for disaster preparedness and response.

Data Processing

The data for this analysis come from the NOAA Storm Database, which contains records of major weather events in the United States from 1950 onward.
The analysis begins with the raw compressed CSV file and performs all preprocessing within this document.

Loading the data

Define the data URL and local file name

storm_data <- read.csv("C:/Users/apekshyag/Documents/repdata_data_StormData.csv")

Selecting Relevant Variables

For this analysis, we focus on:

EVTYPE: Type of weather event

FATALITIES: Number of deaths

INJURIES: Number of injuries

PROPDMG and PROPDMGEXP: Property damage

CROPDMG and CROPDMGEXP: Crop damage

storm_data <- storm_data %>% select(EVTYPE, FATALITIES, INJURIES,PROPDMG, PROPDMGEXP,CROPDMG, CROPDMGEXP)

Converting Damage Exponents

Damage values use letter-based exponents. These are converted to numeric multipliers.

exp_to_multiplier <- function(exp) {
ifelse(exp == "H", 1e2,
ifelse(exp == "K", 1e3,
ifelse(exp == "M", 1e6,
ifelse(exp == "B", 1e9, 1))))
}

storm_data <- storm_data %>%
mutate(
PROP_MULT = exp_to_multiplier(PROPDMGEXP),
CROP_MULT = exp_to_multiplier(CROPDMGEXP),
PROP_DAMAGE = PROPDMG * PROP_MULT,
CROP_DAMAGE = CROPDMG * CROP_MULT
)

Results

Impact on Population Health

Population health impact is measured as the sum of fatalities and injuries.

health_impact <- storm_data %>%
group_by(EVTYPE) %>%
summarize(
Fatalities = sum(FATALITIES, na.rm = TRUE),
Injuries = sum(INJURIES, na.rm = TRUE),
Total_Harm = Fatalities + Injuries
) %>%
arrange(desc(Total_Harm)) %>%
slice(1:10)

health_impact
## # A tibble: 10 x 4
##    EVTYPE            Fatalities Injuries Total_Harm
##    <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

Figure 1: Top Weather Events Affecting Population Health

ggplot(health_impact,
aes(x = reorder(EVTYPE, Total_Harm), y = Total_Harm)) +
geom_col(fill = "steelblue") +
coord_flip() +
labs(
title = "Top 10 Weather Events by Population Health Impact",
x = "Event Type",
y = "Total Injuries and Fatalities"
)

Finding: Tornadoes are by far the most harmful weather events in terms of injuries and fatalities, followed by excessive heat and flooding.

Economic Consequences

Economic impact is calculated as the sum of property and crop damage.

economic_impact <- storm_data %>%
group_by(EVTYPE) %>%
summarize(
Property_Damage = sum(PROP_DAMAGE, na.rm = TRUE),
Crop_Damage = sum(CROP_DAMAGE, na.rm = TRUE),
Total_Damage = Property_Damage + Crop_Damage
) %>%
arrange(desc(Total_Damage)) %>%
slice(1:10)

economic_impact
## # A tibble: 10 x 4
##    EVTYPE            Property_Damage 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                 15727367548.  3025537890  18752905438.
##  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   8967041360

Figure 2: Top Weather Events by Economic Impact

ggplot(economic_impact,
aes(x = reorder(EVTYPE, Total_Damage), y = Total_Damage / 1e9)) +
geom_col(fill = "darkred") +
coord_flip() +
labs(
title = "Top 10 Weather Events by Economic Damage",
x = "Event Type",
y = "Total Damage (Billion USD)"
)

Finding: Floods cause the greatest economic damage in the United States, followed by hurricanes/typhoons and tornadoes.

Conclusion

This analysis demonstrates that different types of severe weather events dominate different dimensions of impact. Tornadoes pose the greatest threat to population health, while floods are the most costly in economic terms. Understanding these distinctions is essential for emergency planning, infrastructure investment, and disaster preparedness at the local and national levels.