Analysis of NOAA Storm Database

Synopsis

This report analyzes the U.S. National Oceanic and Atmospheric Administration (NOAA) Storm Database. The objective is to identify which weather events are most harmful to population health and which have the greatest economic consequences. Population health is measured using the total number of fatalities and injuries. Economic consequences are measured using property and crop damage estimates. The data were loaded directly from the original NOAA Storm Database and processed using R. The results are summarized using tables and figures.

Data Processing

The data were downloaded directly from the NOAA Storm Database and loaded into R. Only the variables required for this analysis were selected. Property and crop damage estimates were converted into numeric values using the exponent variables provided in the dataset. Population health impact was calculated as the total number of fatalities and injuries, while economic impact was calculated as the sum of property and crop damage.

library(dplyr)
library(ggplot2)
library(knitr)

url <- "https://d396qusza40orc.cloudfront.net/repdata/data/StormData.csv.bz2"


if(!file.exists("StormData.csv.bz2")){
    download.file(url, "StormData.csv.bz2")
}

storm <- read.csv("StormData.csv.bz2")

storm <- storm %>%
    select(EVTYPE,
           FATALITIES,
           INJURIES,
           PROPDMG,
           PROPDMGEXP,
           CROPDMG,
           CROPDMGEXP)

storm$PROPDMGEXP <- toupper(storm$PROPDMGEXP)
storm$CROPDMGEXP <- toupper(storm$CROPDMGEXP)

mult <- function(x){

    x <- toupper(x)

    if(x=="H") return(1e2)
    if(x=="K") return(1e3)
    if(x=="M") return(1e6)
    if(x=="B") return(1e9)

    if(grepl("^[0-8]$",x))
        return(10^as.numeric(x))

    return(1)
}

storm$PropertyDamage <-
storm$PROPDMG*sapply(storm$PROPDMGEXP,mult)

storm$CropDamage <-
storm$CROPDMG*sapply(storm$CROPDMGEXP,mult)

Population health

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

Economic consequences

economic <- storm %>%
    group_by(EVTYPE) %>%
    summarise(
        Damage = sum(PropertyDamage + CropDamage, na.rm = TRUE)
    ) %>%
    arrange(desc(Damage))

Results

Question 1

Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?

kable(head(health,10))

EVTYPE	Fatalities	Injuries	Total
TORNADO	5633	91346	96979
EXCESSIVE HEAT	1903	6525	8428
TSTM WIND	504	6957	7461
FLOOD	470	6789	7259
LIGHTNING	816	5230	6046
HEAT	937	2100	3037
FLASH FLOOD	978	1777	2755
ICE STORM	89	1975	2064
THUNDERSTORM WIND	133	1488	1621
WINTER STORM	206	1321	1527

Figure 1

ggplot(head(health,10),
       aes(reorder(EVTYPE,Total),Total))+
geom_col(fill="steelblue")+
coord_flip()+
labs(
title="Top 10 Weather Events Harmful to Population Health",
x="Event Type",
y="Fatalities + Injuries"
)

The results indicate that tornadoes have the greatest impact on population health. They account for the highest combined number of fatalities and injuries, followed by excessive heat and floods.

Question 2

Across the United States, which types of events have the greatest economic consequences?

 kable(head(economic,10))

EVTYPE	Damage
FLOOD	150319678257
HURRICANE/TYPHOON	71913712800
TORNADO	57362333947
STORM SURGE	43323541000
HAIL	18761221986
FLASH FLOOD	18243991079
DROUGHT	15018672000
HURRICANE	14610229010
RIVER FLOOD	10148404500
ICE STORM	8967041360

Figure 2

 ggplot(head(economic,10),
       aes(reorder(EVTYPE,Damage),Damage/1e9))+
geom_col(fill="tomato")+
coord_flip()+
labs(
title="Top 10 Weather Events by Economic Damage",
x="Event Type",
y="Damage (Billion USD)"
)

Floods produced the largest economic losses, followed by hurricanes, storm surge events and tornadoes. Most of the financial losses were associated with property damage.

Conclusion

The analysis shows that tornadoes are responsible for the greatest impact on population health, producing the largest combined number of fatalities and injuries.

Floods, hurricanes and storm surge events account for the highest economic losses due to property and crop damage.

These findings may help emergency managers prioritize preparedness activities for weather events that historically produce the greatest human and economic impacts.