Synopsis

The U.S. National Oceanic and Atmospheric Administration (NOAA) Storm Database contains information on major storms and weather events in the United States. This report analyzes the database to answer two questions:

  1. Which event types are most harmful to population health?
  2. Which event types have the greatest economic consequences?

Population health is measured using fatalities and injuries, while economic impact is measured using total property and crop damage.

Data Processing

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

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

storm$PROP_MULT <- ifelse(storm$PROPDMGEXP=="K",1000,
                    ifelse(storm$PROPDMGEXP=="M",1000000,
                    ifelse(storm$PROPDMGEXP=="B",1000000000,1)))

storm$CROP_MULT <- ifelse(storm$CROPDMGEXP=="K",1000,
                    ifelse(storm$CROPDMGEXP=="M",1000000,
                    ifelse(storm$CROPDMGEXP=="B",1000000000,1)))

storm$TOTALDMG <- storm$PROPDMG*storm$PROP_MULT +
                  storm$CROPDMG*storm$CROP_MULT

Results

Question 1: Most Harmful Events to Population Health

health <- aggregate(cbind(FATALITIES, INJURIES) ~ EVTYPE,
                    data = storm,
                    sum)

health$TOTAL <- health$FATALITIES + health$INJURIES

health <- health[order(-health$TOTAL),]

head(health,10)
##                EVTYPE FATALITIES INJURIES TOTAL
## 834           TORNADO       5633    91346 96979
## 130    EXCESSIVE HEAT       1903     6525  8428
## 856         TSTM WIND        504     6957  7461
## 170             FLOOD        470     6789  7259
## 464         LIGHTNING        816     5230  6046
## 275              HEAT        937     2100  3037
## 153       FLASH FLOOD        978     1777  2755
## 427         ICE STORM         89     1975  2064
## 760 THUNDERSTORM WIND        133     1488  1621
## 972      WINTER STORM        206     1321  1527

Figure 1

barplot(health$TOTAL[1:10],
        names.arg=health$EVTYPE[1:10],
        las=2,
        col="steelblue",
        main="Top 10 Weather Events Harmful to Population Health",
        ylab="Fatalities + Injuries")

Interpretation

The analysis shows that Tornadoes are the most harmful weather events to population health. Tornadoes caused the highest combined number of fatalities and injuries, followed by Excessive Heat, TSTM Wind, Flood, and Lightning.

Question 2: Greatest Economic Consequences

damage <- aggregate(TOTALDMG ~ EVTYPE,
                    data = storm,
                    sum)

damage <- damage[order(-damage$TOTALDMG),]

head(damage,10)
##                EVTYPE     TOTALDMG
## 170             FLOOD 150319678257
## 411 HURRICANE/TYPHOON  71913712800
## 834           TORNADO  57352114049
## 670       STORM SURGE  43323541000
## 244              HAIL  18758221521
## 153       FLASH FLOOD  17562129167
## 95            DROUGHT  15018672000
## 402         HURRICANE  14610229010
## 590       RIVER FLOOD  10148404500
## 427         ICE STORM   8967041360

Figure 2

barplot(damage$TOTALDMG[1:10]/1e9,
        names.arg=damage$EVTYPE[1:10],
        las=2,
        col="darkgreen",
        main="Top 10 Weather Events by Economic Damage",
        ylab="Damage (Billion USD)")

Interpretation

Floods caused the greatest economic losses in the United States, with total damages exceeding 150 billion USD. Hurricane/Typhoon, Tornado, Storm Surge, and Hail were also responsible for substantial economic damage.

Conclusion

This analysis demonstrates that:

These assignment can be helpfull for government agencies and other agencies to prioritize disaster preparedness and resource allocation.