NOAA Storm Database Analysis

knitr::opts_chunk$set(echo = TRUE, cache = TRUE)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)
library(tidyr)

Synopsis

This report analyzes the NOAA Storm Database to determine which severe weather events cause the most severe consequences regarding population health and economic damage across the United States. By processing storm records from 1950 to 2011, we aggregate total fatalities, injuries, and property/crop damage by event type (EVTYPE). The findings indicate that specific weather phenomena disproportionately impact public safety and financial infrastructure. Government and municipal managers can utilize these insights to prioritize emergency preparedness resources and disaster mitigation strategies effectively.

Data Processing

In this section, we load the raw storm data directly from the source archive, clean the event types, and compute the total health and economic impact metrics.

1. Loading the Data

csv_file <- "StormData.csv"

storm_data <- read.csv(csv_file)

2. Processing Population Health Data

We aggregate fatalities and injuries by event type to evaluate population health impact.

health_data <- storm_data %>%
  group_by(EVTYPE) %>%
  summarise(
    Fatalities = sum(FATALITIES, na.rm = TRUE),
    Injuries = sum(INJURIES, na.rm = TRUE),
    Total_Health_Impact = Fatalities + Injuries
  ) %>%
  arrange(desc(Total_Health_Impact))

# Top 10 harmful weather events for health
top_health <- head(health_data, 10)
print(top_health)

## # A tibble: 10 × 4
##    EVTYPE            Fatalities Injuries Total_Health_Impact
##    <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

3. Processing Economic Consequence Data

We convert property and crop damage exponent characters (PROPDMGEXP, CROPDMGEXP) into numerical multipliers to calculate total economic loss.

# Function to convert exponent letters to multipliers
get_multiplier <- function(exp) {
  exp <- toupper(as.character(exp))
  case_when(
    exp == "H" ~ 100,
    exp == "K" ~ 1000,
    exp == "M" ~ 1e6,
    exp == "B" ~ 1e9,
    exp %in% c("1", "2", "3", "4", "5", "6", "7", "8") ~ 10^as.numeric(exp),
    TRUE ~ 1
  )
}

economic_data <- storm_data %>%
  mutate(
    Prop_Multiplier = get_multiplier(PROPDMGEXP),
    Crop_Multiplier = get_multiplier(CROPDMGEXP),
    Total_Prop_Damage = PROPDMG * Prop_Multiplier,
    Total_Crop_Damage = CROPDMG * Crop_Multiplier,
    Total_Economic_Damage = Total_Prop_Damage + Total_Crop_Damage
  ) %>%
  group_by(EVTYPE) %>%
  summarise(Economic_Damage = sum(Total_Economic_Damage, na.rm = TRUE)) %>%
  arrange(desc(Economic_Damage))

## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `Prop_Multiplier = get_multiplier(PROPDMGEXP)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.

# Top 10 weather events with greatest economic consequences
top_economic <- head(economic_data, 10)
print(top_economic)

## # A tibble: 10 × 2
##    EVTYPE            Economic_Damage
##    <chr>                       <dbl>
##  1 FLOOD               150319678257 
##  2 HURRICANE/TYPHOON    71913712800 
##  3 TORNADO              57362333946.
##  4 STORM SURGE          43323541000 
##  5 HAIL                 18761221986.
##  6 FLASH FLOOD          18243991078.
##  7 DROUGHT              15018672000 
##  8 HURRICANE            14610229010 
##  9 RIVER FLOOD          10148404500 
## 10 ICE STORM             8967041360

Results

In this section, we present our findings using figures that visualize the events most harmful to health and the economy.

Figure 1: Top Weather Events Harmful to Population Health

top_health_long <- top_health %>%
  select(EVTYPE, Fatalities, Injuries) %>%
  pivot_longer(cols = c(Fatalities, Injuries), names_to = "Damage_Type", values_to = "Count")

ggplot(top_health_long, aes(x = reorder(EVTYPE, Count), y = Count, fill = Damage_Type)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(
    title = "Top 10 Weather Events Most Harmful to US Population Health",
    x = "Event Type",
    y = "Number of Casualties",
    fill = "Casualty Type"
  ) +
  theme_minimal()

### Figure 2: Top Weather Events with Greatest Economic Consequences

ggplot(top_economic, aes(x = reorder(EVTYPE, Economic_Damage / 1e9), y = Economic_Damage / 1e9)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  coord_flip() +
  labs(
    title = "Top 10 Weather Events with Greatest Economic Consequences",
    x = "Event Type",
    y = "Total Damage (in Billions USD)"
  ) +
  theme_minimal()