NOAA Storm Data Analysis: Health and Economic Impact of Severe Weather Events

Synopsis

This report analyzes the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database, which covers major weather events from 1950 to November 2011. The goal is to identify which types of events are most harmful to population health and which have the greatest economic consequences. Health impact was measured by combining fatalities and injuries per event type. Economic impact was estimated by converting property and crop damage values to a common dollar scale using their respective magnitude codes. Results show that tornadoes cause the most harm to human health, while floods generate the greatest overall economic damage across the United States. These findings can help government and municipal managers prioritize resource allocation when preparing for severe weather events.

Data Processing

Loading the data

The data is downloaded directly from the course website and read from the compressed .csv.bz2 file.

# url <- "https://d396qusza40orc.frontcdn.net/repdata%2Fdata%2FStormData.csv.bz2"
# destfile <- "StormData.csv.bz2"
# 
# if (!file.exists(destfile)) {
#   download.file(url, destfile, method = "curl")
# }

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

dim(storm)

## [1] 902297     37

names(storm)

##  [1] "STATE__"    "BGN_DATE"   "BGN_TIME"   "TIME_ZONE"  "COUNTY"    
##  [6] "COUNTYNAME" "STATE"      "EVTYPE"     "BGN_RANGE"  "BGN_AZI"   
## [11] "BGN_LOCATI" "END_DATE"   "END_TIME"   "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE"  "END_AZI"    "END_LOCATI" "LENGTH"     "WIDTH"     
## [21] "F"          "MAG"        "FATALITIES" "INJURIES"   "PROPDMG"   
## [26] "PROPDMGEXP" "CROPDMG"    "CROPDMGEXP" "WFO"        "STATEOFFIC"
## [31] "ZONENAMES"  "LATITUDE"   "LONGITUDE"  "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS"    "REFNUM"

Subsetting relevant variables

We keep only the columns needed to answer both questions.

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

Processing health data

We aggregate total fatalities and injuries by event type.

library(dplyr)

health <- storm %>%
  group_by(EVTYPE) %>%
  summarise(
    Fatalities = sum(FATALITIES, na.rm = TRUE),
    Injuries   = sum(INJURIES,   na.rm = TRUE),
    Total      = Fatalities + Injuries
  ) %>%
  arrange(desc(Total)) %>%
  head(10)

Processing economic data

The PROPDMGEXP and CROPDMGEXP columns encode magnitude multipliers. We convert them to numeric values (K = 1,000 | M = 1,000,000 | B = 1,000,000,000).

# Function to convert EXP codes to numeric multipliers
exp_to_num <- function(exp) {
  exp <- toupper(trimws(exp))
  dplyr::case_when(
    exp == "K" ~ 1e3,
    exp == "M" ~ 1e6,
    exp == "B" ~ 1e9,
    exp == "H" ~ 1e2,
    exp %in% as.character(0:9) ~ 10^as.numeric(exp),
    TRUE ~ 1
  )
}

storm$PropDmgValue <- storm$PROPDMG * exp_to_num(storm$PROPDMGEXP)
storm$CropDmgValue <- storm$CROPDMG * exp_to_num(storm$CROPDMGEXP)

economic <- storm %>%
  group_by(EVTYPE) %>%
  summarise(
    PropertyDamage = sum(PropDmgValue, na.rm = TRUE),
    CropDamage     = sum(CropDmgValue, na.rm = TRUE),
    TotalDamage    = PropertyDamage + CropDamage
  ) %>%
  arrange(desc(TotalDamage)) %>%
  head(10)

Results

Question 1: Which events are most harmful to population health?

library(ggplot2)
library(tidyr)

health_long <- health %>%
  select(EVTYPE, Fatalities, Injuries) %>%
  pivot_longer(cols = c(Fatalities, Injuries), names_to = "Type", values_to = "Count") %>%
  mutate(EVTYPE = reorder(EVTYPE, -Count))

ggplot(health_long, aes(x = reorder(EVTYPE, Count), y = Count, fill = Type)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  scale_fill_manual(values = c("Fatalities" = "#c0392b", "Injuries" = "#e67e22")) +
  labs(
    title    = "Top 10 Weather Events by Health Impact (1950–2011)",
    subtitle = "Source: NOAA Storm Database",
    x        = "Event Type",
    y        = "Total Cases",
    fill     = ""
  ) +
  theme_minimal(base_size = 13) +
  theme(legend.position = "bottom")

Figure 1. Top 10 weather event types by total health impact (fatalities + injuries) in the U.S., 1950–2011. Tornadoes dominate both categories by a wide margin.

Tornadoes are by far the most harmful event type, accounting for the majority of both fatalities and injuries. Excessive heat and floods follow distantly in second and third place.

Question 2: Which events have the greatest economic consequences?

economic_long <- economic %>%
  select(EVTYPE, PropertyDamage, CropDamage) %>%
  pivot_longer(cols = c(PropertyDamage, CropDamage), names_to = "Type", values_to = "Damage") %>%
  mutate(
    EVTYPE = reorder(EVTYPE, Damage),
    Damage = Damage / 1e9  # convert to billions
  )

ggplot(economic_long, aes(x = reorder(EVTYPE, Damage), y = Damage, fill = Type)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  scale_fill_manual(values = c("PropertyDamage" = "#2980b9", "CropDamage" = "#27ae60")) +
  labs(
    title    = "Top 10 Weather Events by Economic Damage (1950–2011)",
    subtitle = "Source: NOAA Storm Database",
    x        = "Event Type",
    y        = "Total Damage (Billion USD)",
    fill     = ""
  ) +
  theme_minimal(base_size = 13) +
  theme(legend.position = "bottom")

Figure 2. Top 10 weather event types by total economic damage (property + crop damage) in the U.S., 1950–2011. Floods produce the highest combined economic losses.

Floods cause the greatest total economic damage, driven primarily by property losses. Hurricanes/typhoons and tornadoes also rank among the top economic threats, while droughts stand out for their disproportionate impact on crop damage.

Summary Table

knitr::kable(
  health[, c("EVTYPE", "Fatalities", "Injuries", "Total")],
  caption = "Table 1. Top 10 events by health impact",
  col.names = c("Event Type", "Fatalities", "Injuries", "Total"),
  format.args = list(big.mark = ",")
)

Table 1. Top 10 events by health impact
Event Type	Fatalities	Injuries	Total
TORNADO	5,633	91,346	96,979
EXCESSIVE HEAT	1,903	6,525	8,428
TSTM WIND	504	6,957	7,461
FLOOD	470	6,789	7,259
LIGHTNING	816	5,230	6,046
HEAT	937	2,100	3,037
FLASH FLOOD	978	1,777	2,755
ICE STORM	89	1,975	2,064
THUNDERSTORM WIND	133	1,488	1,621
WINTER STORM	206	1,321	1,527

knitr::kable(
  economic[, c("EVTYPE", "PropertyDamage", "CropDamage", "TotalDamage")],
  caption = "Table 2. Top 10 events by economic damage (USD)",
  col.names = c("Event Type", "Property Damage", "Crop Damage", "Total Damage"),
  format.args = list(big.mark = ",")
)

Table 2. Top 10 events by economic damage (USD)
Event Type	Property Damage	Crop Damage	Total Damage
FLOOD	144,657,709,807	5,661,968,450	150,319,678,257
HURRICANE/TYPHOON	69,305,840,000	2,607,872,800	71,913,712,800
TORNADO	56,947,380,677	414,953,270	57,362,333,947
STORM SURGE	43,323,536,000	5,000	43,323,541,000
HAIL	15,735,267,513	3,025,954,473	18,761,221,986
FLASH FLOOD	16,822,673,979	1,421,317,100	18,243,991,079
DROUGHT	1,046,106,000	13,972,566,000	15,018,672,000
HURRICANE	11,868,319,010	2,741,910,000	14,610,229,010
RIVER FLOOD	5,118,945,500	5,029,459,000	10,148,404,500
ICE STORM	3,944,927,860	5,022,113,500	8,967,041,360