Health and Economic Effects of Severe Weather Events in the United States

Synopsis

This analysis uses the NOAA Storm Database to determine which severe weather events have had the greatest effects on population health and the economy in the United States. Population-health impact is measured as the combined number of fatalities and injuries. Economic impact is measured as the sum of reported property and crop damage after the damage exponent codes are converted into dollar multipliers. Event names are converted to uppercase and extra spaces are removed before totals are calculated. The results show that tornadoes have caused the greatest combined health impact, while floods have produced the greatest total economic damage in the dataset.

Data Processing

The analysis begins with the original compressed CSV file supplied for the assignment.

data_file <- "repdata_data_StormData.csv.bz2"

storm <- read.csv(
  bzfile(data_file),
  stringsAsFactors = FALSE,
  fileEncoding = "latin1"
)

dim(storm)

## [1] 902297     37

Only the variables required for the analysis are retained.

storm_analysis <- storm[, c(
  "EVTYPE",
  "FATALITIES",
  "INJURIES",
  "PROPDMG",
  "PROPDMGEXP",
  "CROPDMG",
  "CROPDMGEXP"
)]

Event names are standardized by converting them to uppercase, trimming leading and trailing spaces, and replacing repeated spaces with one space. This addresses minor formatting differences while avoiding subjective recoding of historical event categories.

storm_analysis$EVTYPE <- toupper(trimws(storm_analysis$EVTYPE))
storm_analysis$EVTYPE <- gsub(
  "[[:space:]]+",
  " ",
  storm_analysis$EVTYPE
)

Population-health impact is defined as fatalities plus injuries.

storm_analysis$HEALTH_IMPACT <-
  storm_analysis$FATALITIES +
  storm_analysis$INJURIES

Property and crop damage are recorded using numeric values together with exponent codes. The function below converts the exponent codes into multipliers. The principal codes are H for hundreds, K for thousands, M for millions, and B for billions. Numeric codes are treated as powers of ten. Blank or unrecognized codes are assigned a multiplier of one.

damage_multiplier <- function(x) {
  x <- toupper(trimws(as.character(x)))
  multiplier <- rep(1, length(x))

  multiplier[x == "H"] <- 1e2
  multiplier[x == "K"] <- 1e3
  multiplier[x == "M"] <- 1e6
  multiplier[x == "B"] <- 1e9

  numeric_codes <- grepl("^[0-9]$", x)
  multiplier[numeric_codes] <-
    10 ^ as.numeric(x[numeric_codes])

  multiplier
}

The property and crop damage variables are converted to dollars and combined.

storm_analysis$PROPERTY_DAMAGE <-
  storm_analysis$PROPDMG *
  damage_multiplier(storm_analysis$PROPDMGEXP)

storm_analysis$CROP_DAMAGE <-
  storm_analysis$CROPDMG *
  damage_multiplier(storm_analysis$CROPDMGEXP)

storm_analysis$ECONOMIC_DAMAGE <-
  storm_analysis$PROPERTY_DAMAGE +
  storm_analysis$CROP_DAMAGE

Results

Events most harmful to population health

The combined number of fatalities and injuries is summed for each event type.

health_summary <- aggregate(
  HEALTH_IMPACT ~ EVTYPE,
  data = storm_analysis,
  FUN = sum,
  na.rm = TRUE
)

health_summary <- health_summary[
  order(health_summary$HEALTH_IMPACT, decreasing = TRUE),
]

top_health <- head(health_summary, 10)
top_health

##                EVTYPE HEALTH_IMPACT
## 745           TORNADO         96979
## 107    EXCESSIVE HEAT          8428
## 766         TSTM WIND          7461
## 145             FLOOD          7259
## 407         LIGHTNING          6046
## 234              HEAT          3037
## 129       FLASH FLOOD          2755
## 376         ICE STORM          2064
## 672 THUNDERSTORM WIND          1621
## 873      WINTER STORM          1527

health_plot <- top_health[
  order(top_health$HEALTH_IMPACT),
]

par(mar = c(5, 12, 4, 2))

barplot(
  health_plot$HEALTH_IMPACT,
  names.arg = health_plot$EVTYPE,
  horiz = TRUE,
  las = 1,
  cex.names = 0.8,
  main = "Weather Events Most Harmful to Population Health",
  xlab = "Combined Fatalities and Injuries"
)

Figure 1. The ten weather-event types with the largest combined number of fatalities and injuries in the United States.

Tornadoes caused the greatest combined population-health impact, with 96,979 recorded fatalities and injuries. Excessive heat, thunderstorm wind, floods, and lightning were the next most harmful event types.

Events with the greatest economic consequences

Reported property and crop damages are summed for each event type.

economic_summary <- aggregate(
  ECONOMIC_DAMAGE ~ EVTYPE,
  data = storm_analysis,
  FUN = sum,
  na.rm = TRUE
)

economic_summary <- economic_summary[
  order(economic_summary$ECONOMIC_DAMAGE, decreasing = TRUE),
]

top_economic <- head(economic_summary, 10)
top_economic

##                EVTYPE ECONOMIC_DAMAGE
## 145             FLOOD    150319678257
## 361 HURRICANE/TYPHOON     71913712800
## 745           TORNADO     57362333946
## 587       STORM SURGE     43323541000
## 203              HAIL     18761221986
## 129       FLASH FLOOD     18244041078
## 75            DROUGHT     15018672000
## 352         HURRICANE     14610229010
## 517       RIVER FLOOD     10148404500
## 376         ICE STORM      8967041360

economic_plot <- top_economic[
  order(top_economic$ECONOMIC_DAMAGE),
]

par(mar = c(5, 12, 4, 2))

barplot(
  economic_plot$ECONOMIC_DAMAGE / 1e9,
  names.arg = economic_plot$EVTYPE,
  horiz = TRUE,
  las = 1,
  cex.names = 0.8,
  main = "Weather Events with the Greatest Economic Consequences",
  xlab = "Combined Property and Crop Damage (Billions of Dollars)"
)

Figure 2. The ten weather-event types with the greatest combined property and crop damage in the United States.

Floods caused the greatest total economic damage, at approximately $150.32 billion. Hurricane/typhoon events, tornadoes, storm surges, and hail were also among the event types with the largest economic consequences.

Conclusion

The NOAA Storm Database indicates that tornadoes have been the most harmful event type with respect to combined fatalities and injuries. Floods have produced the greatest combined property and crop damage. Therefore, the event types responsible for the greatest population-health effects are not identical to those responsible for the greatest economic losses.