TITLE: Analysis of NOAA Storm Data: Impact on Public Health and Economic Consequences

Synopsis This analysis explores the U.S. National Oceanic and Atmospheric Administration (NOAA) storm database to determine which types of severe weather events are most harmful to public health and have the greatest economic consequences. The dataset was processed in R, focusing on fatalities, injuries, property damage, and crop damage. The results indicate that tornadoes are the most dangerous to public health, causing the highest number of fatalities and injuries. Meanwhile, floods and hurricanes/typhoons have the most significant economic impacts, leading to the highest property and crop damage. These findings can help government and municipal officials prioritize disaster preparedness and resource allocation.

Data Processing

#### 1. Downloading and Loading the Data The dataset was downloaded directly from the NOAA website as a compressed CSV file (repdata_data_StormData.csv.bz2). The following R code was used to load and process the data:

Load necessary libraries

library(dplyr)
library(ggplot2)
library(tidyr)
library(magrittr)

Download the file (if not already present)

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

Read the CSV file

storm_data <- read.csv("repdata_data_StormData.csv.bz2")

2. Data Preprocessing

We focus on the following variables:

EVTYPE (event type)

FATALITIES (number of deaths)

INJURIES (number of injuries)

PROPDMG (property damage)

PROPDMGEXP (property damage exponent, e.g., K, M, B)

CROPDMG (crop damage)

CROPDMGEXP (crop damage exponent)

First, we process the damage values by converting the exponent letters into numeric multipliers:

Function to convert damage exponents to numeric values

convert_exp <- function(exp) {
  exp <- toupper(exp)
  multiplier <- case_when(
    exp == "K" ~ 1e3,
    exp == "M" ~ 1e6,
    exp == "B" ~ 1e9,
    TRUE ~ 1  # Default (no multiplier)
  )
  return(multiplier)
}

Calculate total property and crop damage

storm_data <- storm_data %>%
  mutate(
    PROP_DAMAGE = PROPDMG * sapply(PROPDMGEXP, convert_exp),
    CROP_DAMAGE = CROPDMG * sapply(CROPDMGEXP, convert_exp),
    TOTAL_DAMAGE = PROP_DAMAGE + CROP_DAMAGE
  )

3. Aggregating Data for Analysis

We summarize the data to identify the most harmful events for public health and the most economically damaging events:

Health impact (fatalities + injuries)

health_impact <- storm_data %>%
  group_by(EVTYPE) %>%
  summarise(
    FATALITIES = sum(FATALITIES),
    INJURIES = sum(INJURIES),
    TOTAL_HEALTH = FATALITIES + INJURIES
  ) %>%
  arrange(desc(TOTAL_HEALTH))

Economic impact (property + crop damage)

economic_impact <- storm_data %>%
  group_by(EVTYPE) %>%
  summarise(
    PROP_DAMAGE = sum(PROP_DAMAGE),
    CROP_DAMAGE = sum(CROP_DAMAGE),
    TOTAL_ECONOMIC = sum(TOTAL_DAMAGE)
  ) %>%
  arrange(desc(TOTAL_ECONOMIC))

Results

  1. Most Harmful Events for Public Health The top 10 most harmful weather events in terms of fatalities and injuries are:
top_health <- head(health_impact, 10)
print(top_health)
## # A tibble: 10 × 4
##    EVTYPE            FATALITIES INJURIES TOTAL_HEALTH
##    <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 by Health Impact

ggplot(top_health, aes(x = reorder(EVTYPE, -TOTAL_HEALTH), y = TOTAL_HEALTH)) +
  geom_bar(stat = "identity", fill = "red") +
  labs(
    title = "Top 10 Most Harmful Weather Events (Fatalities + Injuries)",
    x = "Event Type",
    y = "Total Health Impact"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Key Finding:

Tornadoes are the deadliest and most injury-causing events, followed by excessive heat and flash floods.

  1. Events with the Greatest Economic Consequences The top 10 most economically damaging weather events are:
top_economic <- head(economic_impact, 10)
print(top_economic)
## # A tibble: 10 × 4
##    EVTYPE              PROP_DAMAGE CROP_DAMAGE TOTAL_ECONOMIC
##    <chr>                     <dbl>       <dbl>          <dbl>
##  1 FLOOD             144657709807   5661968450  150319678257 
##  2 HURRICANE/TYPHOON  69305840000   2607872800   71913712800 
##  3 TORNADO            56937160779.   414953270   57352114049.
##  4 STORM SURGE        43323536000         5000   43323541000 
##  5 HAIL               15732267048.  3025954473   18758221521.
##  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 Damage

ggplot(top_economic, aes(x = reorder(EVTYPE, -TOTAL_ECONOMIC), y = TOTAL_ECONOMIC / 1e9)) +
  geom_bar(stat = "identity", fill = "blue") +
  labs(
    title = "Top 10 Most Economically Damaging Weather Events",
    x = "Event Type",
    y = "Total Economic Damage (Billions USD)"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Key Finding:

Floods and hurricanes/typhoons cause the most economic damage, followed by tornadoes and storm surges.

Conclusion

This analysis highlights that:

Tornadoes are the most dangerous to public health.

Floods and hurricanes have the highest economic impact.

Government and municipal agencies should prioritize disaster preparedness and mitigation strategies for these high-impact weather events to minimize harm to people and infrastructure.