NOAA Storm Database Analysis

library(dplyr)
library(ggplot2)
library(knitr)

Synopsis

This analysis explores the U.S. National Oceanic and Atmospheric Administration (NOAA) Storm Database to determine which types of severe weather events have the greatest impact on population health and the economy. Population health impact is measured using the total number of fatalities and injuries, while economic impact is measured using the combined value of property and crop damage. The analysis begins with the original compressed storm data file and performs all data processing within this report. Results show that tornadoes are the leading cause of injuries and fatalities, while floods and related events account for the highest economic losses.

Data Processing

The data were read directly from the original compressed NOAA storm database file. Population health impact was calculated by summing fatalities and injuries for each event type. Economic impact was calculated by converting the property and crop damage exponents into numeric multipliers and summing the resulting dollar values.

storm <- read.csv(
  "repdata_data_StormData.csv.bz2",
  stringsAsFactors = FALSE
)

dim(storm)

## [1] 902297     37

Population Health Processing

health <- storm %>%
  group_by(EVTYPE) %>%
  summarise(
    Fatalities = sum(FATALITIES, na.rm = TRUE),
    Injuries = sum(INJURIES, na.rm = TRUE),
    Total = Fatalities + Injuries,
    .groups = "drop"
  ) %>%
  arrange(desc(Total))

kable(head(health, 10))

EVTYPE	Fatalities	Injuries	Total
TORNADO	5633	91346	96979
EXCESSIVE HEAT	1903	6525	8428
TSTM WIND	504	6957	7461
FLOOD	470	6789	7259
LIGHTNING	816	5230	6046
HEAT	937	2100	3037
FLASH FLOOD	978	1777	2755
ICE STORM	89	1975	2064
THUNDERSTORM WIND	133	1488	1621
WINTER STORM	206	1321	1527

Economic Damage Processing

convert_exp <- function(exp){

  exp <- toupper(exp)

  if(exp == "H") return(1e2)
  if(exp == "K") return(1e3)
  if(exp == "M") return(1e6)
  if(exp == "B") return(1e9)

  if(exp %in% c("0","1","2","3","4","5","6","7","8")) {
    return(10^as.numeric(exp))
  }

  return(1)
}

storm$PROP_MULT <- sapply(storm$PROPDMGEXP, convert_exp)
storm$CROP_MULT <- sapply(storm$CROPDMGEXP, convert_exp)

storm$PropertyDamage <- storm$PROPDMG * storm$PROP_MULT
storm$CropDamage <- storm$CROPDMG * storm$CROP_MULT

storm$EconomicDamage <- storm$PropertyDamage + storm$CropDamage

economic <- storm %>%
  group_by(EVTYPE) %>%
  summarise(
    TotalDamage = sum(EconomicDamage, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(desc(TotalDamage))

kable(head(economic,10))

EVTYPE	TotalDamage
FLOOD	150319678257
HURRICANE/TYPHOON	71913712800
TORNADO	57362333947
STORM SURGE	43323541000
HAIL	18761221986
FLASH FLOOD	18243991079
DROUGHT	15018672000
HURRICANE	14610229010
RIVER FLOOD	10148404500
ICE STORM	8967041360

Results

Events Most Harmful to Population Health

The table below shows the ten event types with the highest combined number of fatalities and injuries.

top_health <- head(health,10)

ggplot(top_health,
       aes(x = reorder(EVTYPE, Total),
           y = Total)) +
  geom_col(fill = "steelblue") +
  coord_flip() +
  labs(
    title = "Top 10 Weather Events Affecting Population Health",
    x = "Event Type",
    y = "Fatalities + Injuries"
  ) +
  theme_minimal()

The results indicate that tornadoes are responsible for the highest combined number of fatalities and injuries in the United States. Excessive heat, floods, lightning, and thunderstorms also contribute significantly to population health impacts.

Events with the Greatest Economic Consequences

The table below summarizes the ten weather event types with the greatest total economic damage.

top_damage <- head(economic,10)

ggplot(top_damage,
       aes(x = reorder(EVTYPE, TotalDamage),
           y = TotalDamage/1e9)) +
  geom_col(fill = "firebrick") +
  coord_flip() +
  labs(
    title = "Top 10 Weather Events by Economic Damage",
    x = "Event Type",
    y = "Damage (Billions of USD)"
  ) +
  theme_minimal()

Floods, hurricanes, storm surges, and tornadoes account for the greatest economic losses in the NOAA Storm Database. These events cause substantial property damage as well as significant agricultural losses.