Synopsis

This analysis examines the U.S. National Oceanic and Atmospheric Administration (NOAA) Storm Database to identify the types of severe weather events that have had the greatest impact on population health and the economy. Population health impact was evaluated using the total number of fatalities and injuries associated with each event type. Economic impact was evaluated using estimated property and crop damage after accounting for the magnitude codes reported in the database. The analysis shows which event types are associated with the largest health burden and economic losses across the United States.

Data Processing

The raw storm data were downloaded directly from the course website. The original compressed .csv.bz2 file was read into R without preprocessing outside the analysis.

url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"

file <- "StormData.csv.bz2"

if (!file.exists(file)) {
  download.file(url, file, mode = "wb")
}

storm <- read.csv(
  bzfile(file),
  stringsAsFactors = FALSE
)

dim(storm)

## [1] 902297     37

The variables FATALITIES and INJURIES were used to evaluate population health effects. For each event type, the total numbers of fatalities and injuries were summed.

health <- aggregate(
  cbind(FATALITIES, INJURIES) ~ EVTYPE,
  data = storm,
  FUN = sum,
  na.rm = TRUE
)

health$TOTAL_HEALTH <- health$FATALITIES + health$INJURIES

health <- health[
  order(health$TOTAL_HEALTH, decreasing = TRUE),
]

top_health <- head(health, 10)

top_health

##                EVTYPE FATALITIES INJURIES TOTAL_HEALTH
## 834           TORNADO       5633    91346        96979
## 130    EXCESSIVE HEAT       1903     6525         8428
## 856         TSTM WIND        504     6957         7461
## 170             FLOOD        470     6789         7259
## 464         LIGHTNING        816     5230         6046
## 275              HEAT        937     2100         3037
## 153       FLASH FLOOD        978     1777         2755
## 427         ICE STORM         89     1975         2064
## 760 THUNDERSTORM WIND        133     1488         1621
## 972      WINTER STORM        206     1321         1527

For the economic analysis, property damage (PROPDMG) and crop damage (CROPDMG) were converted to dollar values using the corresponding magnitude indicators.

damage_multiplier <- function(x) {
  x <- toupper(trimws(as.character(x)))

  mult <- rep(1, length(x))

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

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

  mult
}

storm$PROPDMG_DOLLARS <-
  storm$PROPDMG * damage_multiplier(storm$PROPDMGEXP)

storm$CROPDMG_DOLLARS <-
  storm$CROPDMG * damage_multiplier(storm$CROPDMGEXP)

storm$TOTAL_ECONOMIC <-
  storm$PROPDMG_DOLLARS + storm$CROPDMG_DOLLARS

economic <- aggregate(
  TOTAL_ECONOMIC ~ EVTYPE,
  data = storm,
  FUN = sum,
  na.rm = TRUE
)

economic <- economic[
  order(economic$TOTAL_ECONOMIC, decreasing = TRUE),
]

top_economic <- head(economic, 10)

top_economic

##                EVTYPE TOTAL_ECONOMIC
## 170             FLOOD   150319678257
## 411 HURRICANE/TYPHOON    71913712800
## 834           TORNADO    57362333946
## 670       STORM SURGE    43323541000
## 244              HAIL    18761221986
## 153       FLASH FLOOD    18243991078
## 95            DROUGHT    15018672000
## 402         HURRICANE    14610229010
## 590       RIVER FLOOD    10148404500
## 427         ICE STORM     8967041360

Results

Events Most Harmful to Population Health

The following figure shows the ten event types associated with the largest combined number of fatalities and injuries.

par(mar = c(5, 11, 4, 2))

barplot(
  rev(top_health$TOTAL_HEALTH),
  names.arg = rev(top_health$EVTYPE),
  horiz = TRUE,
  las = 1,
  xlab = "Total fatalities and injuries",
  main = "Weather Events Most Harmful to Population Health"
)

Figure 1. Total fatalities and injuries for the ten weather-event categories associated with the greatest population health impact in the NOAA Storm Database.

The results indicate that TORNADO produced the largest combined number of fatalities and injuries, with 96,979 recorded health outcomes.

Events with the Greatest Economic Consequences

The following figure shows the ten event types associated with the greatest combined property and crop damage.

par(mar = c(5, 11, 4, 2))

barplot(
  rev(top_economic$TOTAL_ECONOMIC / 1e9),
  names.arg = rev(top_economic$EVTYPE),
  horiz = TRUE,
  las = 1,
  xlab = "Total economic damage (billions of US dollars)",
  main = "Weather Events with the Greatest Economic Impact"
)

Figure 2. Estimated property and crop losses for the ten weather-event categories associated with the greatest economic consequences.

The largest estimated economic loss was associated with FLOOD, with total property and crop damage of approximately $150.3 billion.

Conclusion