NOAA Storm Data Analysis: Impacts on Health and Economy

Synopsis

This report analyzes the U.S. National Oceanic and Atmospheric Administrations (NOAA) storm database to answer two key questions:

Which types of severe weather events are most harmful to population health?

Which types of events have the greatest economic consequences?

The analysis covers weather events from 1950 through 2011. After processing and cleaning the data, we find that tornadoes are the most dangerous events for human health, causing the most fatalities and injuries. For economic impacts, floods result in the greatest damage to property and crops. These findings can help government officials prioritize resources for severe weather preparedness.

Data Processing

Loading the Data We begin with the raw data file without any preprocessing:

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

Cleaning Event Types

The event type (EVTYPE) variable required standardization:

  storm_data <- storm_data %>%
  mutate(
    EVTYPE = tolower(EVTYPE),
    EVTYPE = trimws(EVTYPE),
    EVTYPE = case_when(
      grepl("tornado|funnel", EVTYPE) ~ "tornado",
      grepl("heat|hot", EVTYPE) ~ "excessive heat",
      grepl("flood", EVTYPE) ~ "flood",
      grepl("hurricane|typhoon", EVTYPE) ~ "hurricane",
      grepl("thunderstorm|tstm", EVTYPE) ~ "thunderstorm",
      grepl("lightning", EVTYPE) ~ "lightning",
      grepl("blizzard", EVTYPE) ~ "winter storm",
      TRUE ~ EVTYPE
    )
  )

Processing Damage Values

Property and crop damage values needed conversion:

  storm_data <- storm_data %>%
  mutate(
    PROPDMG_adj = PROPDMG * case_when(
      PROPDMGEXP == "K" ~ 1000,
      PROPDMGEXP == "M" ~ 1e6,
      PROPDMGEXP == "B" ~ 1e9,
      TRUE ~ 1
    ),
    CROPDMG_adj = CROPDMG * case_when(
      CROPDMGEXP == "K" ~ 1000,
      CROPDMGEXP == "M" ~ 1e6,
      CROPDMGEXP == "B" ~ 1e9,
      TRUE ~ 1
    ),
    TOTAL_DAMAGE = PROPDMG_adj + CROPDMG_adj
  )

Preparing Analysis Data:

# For health impacts

health_impact <- storm_data %>%
  group_by(EVTYPE) %>%
  summarise(
    Fatalities = sum(FATALITIES),
    Injuries = sum(INJURIES),
    Total_Health = sum(FATALITIES + INJURIES)
  ) %>%
  arrange(desc(Total_Health)) %>%
  filter(Total_Health > 0)

For economic impacts

econ_impact <- storm_data %>%
  group_by(EVTYPE) %>%
  summarise(
    Property_Damage = sum(PROPDMG_adj),
    Crop_Damage = sum(CROPDMG_adj),
    Total_Damage = sum(TOTAL_DAMAGE)
  ) %>%
  arrange(desc(Total_Damage)) %>%
  filter(Total_Damage > 0)

Results Most Harmful Events to Population Health:

  health_top10 <- head(health_impact, 10)

ggplot(health_top10, aes(x = reorder(EVTYPE, Total_Health), y = Total_Health)) +
  geom_col(fill = "steelblue") +
  coord_flip() +
  labs(
    title = "Top 10 Most Harmful Weather Events to Population Health",
    x = "",
    y = "Total Fatalities + Injuries"
  ) +
  theme_minimal()

Key Findings:

Tornadoes cause the most harm (r format(health_top10$Total_Health[1], big.mark=“,”) total health impacts)

Excessive heat is second most dangerous

Floods and thunderstorms also significant

Events with Greatest Economic Consequences:

  econ_top10 <- head(econ_impact, 10)

ggplot(econ_top10, aes(x = reorder(EVTYPE, Total_Damage), y = Total_Damage/1e9)) +
  geom_col(fill = "darkorange") +
  coord_flip() +
  labs(
    title = "Top 10 Most Costly Weather Events",
    x = "",
    y = "Total Damage (Billions USD)"
  ) +
  theme_minimal()

Key Findings:

Floods cause most damage (\(r format(econ_top10\)Total_Damage[1]/1e9, digits=2) billion)

Hurricanes second (\(r format(econ_top10\)Total_Damage[2]/1e9, digits=2) billion)

Tornadoes third (\(r format(econ_top10\)Total_Damage[3]/1e9, digits=2) billion)

Conclusion Based on the analysis of NOAA storm data from 1950-2011:

For Public Health Protection, priority should be given to:

Tornado warning systems and shelters

Heat wave response plans

Flood safety measures

For Economic Protection, focus should be on:

Flood prevention infrastructure

Hurricane-resistant building codes

Agricultural protection systems

These findings provide evidence-based guidance for severe weather preparedness planning. ```