Analysis of severe weather events on the US population and economy
Anjaney
7/19/2018
Synopsis
Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severe events can result in fatalities, injuries, and property damage, and preventing such outcomes to the extent possible is a key concern.
This project involves exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. This database tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage.
Data Loading
We load the data from the csv file and trim it down to only the columns that we need for the analysis.
StormDataFile <- read.csv("StormData.csv")
head(StormDataFile)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 0
## 2 TORNADO 0 0
## 3 TORNADO 0 0
## 4 TORNADO 0 0
## 5 TORNADO 0 0
## 6 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14.0 100 3 0 0
## 2 NA 0 2.0 150 2 0 0
## 3 NA 0 0.1 123 2 0 0
## 4 NA 0 0.0 100 2 0 0
## 5 NA 0 0.0 150 2 0 0
## 6 NA 0 1.5 177 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## 3 2 25.0 K 0
## 4 2 2.5 K 0
## 5 2 2.5 K 0
## 6 6 2.5 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2
## 3 3340 8742 0 0 3
## 4 3458 8626 0 0 4
## 5 3412 8642 0 0 5
## 6 3450 8748 0 0 6dim(StormDataFile)## [1] 902297 37StormData <- StormDataFile[ , c(8, 23:28)]
rm(StormDataFile)
head(StormData)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 0 15 25.0 K 0
## 2 TORNADO 0 0 2.5 K 0
## 3 TORNADO 0 2 25.0 K 0
## 4 TORNADO 0 2 2.5 K 0
## 5 TORNADO 0 2 2.5 K 0
## 6 TORNADO 0 6 2.5 K 01. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Data Processing
We calculate the aggregate of fatalities and injuries caused by the different event types
Injuries <- aggregate(INJURIES~EVTYPE, StormData, sum)
Injuries <- arrange(Injuries, desc(INJURIES))
Injuries <- Injuries[1:20, ]
Injuries## EVTYPE INJURIES
## 1 TORNADO 91346
## 2 TSTM WIND 6957
## 3 FLOOD 6789
## 4 EXCESSIVE HEAT 6525
## 5 LIGHTNING 5230
## 6 HEAT 2100
## 7 ICE STORM 1975
## 8 FLASH FLOOD 1777
## 9 THUNDERSTORM WIND 1488
## 10 HAIL 1361
## 11 WINTER STORM 1321
## 12 HURRICANE/TYPHOON 1275
## 13 HIGH WIND 1137
## 14 HEAVY SNOW 1021
## 15 WILDFIRE 911
## 16 THUNDERSTORM WINDS 908
## 17 BLIZZARD 805
## 18 FOG 734
## 19 WILD/FOREST FIRE 545
## 20 DUST STORM 440Fatalities <- aggregate(FATALITIES~EVTYPE, StormData, sum)
Fatalities <- arrange(Fatalities, desc(FATALITIES))
Fatalities <- Fatalities[1:20, ]
Fatalities## EVTYPE FATALITIES
## 1 TORNADO 5633
## 2 EXCESSIVE HEAT 1903
## 3 FLASH FLOOD 978
## 4 HEAT 937
## 5 LIGHTNING 816
## 6 TSTM WIND 504
## 7 FLOOD 470
## 8 RIP CURRENT 368
## 9 HIGH WIND 248
## 10 AVALANCHE 224
## 11 WINTER STORM 206
## 12 RIP CURRENTS 204
## 13 HEAT WAVE 172
## 14 EXTREME COLD 160
## 15 THUNDERSTORM WIND 133
## 16 HEAVY SNOW 127
## 17 EXTREME COLD/WIND CHILL 125
## 18 STRONG WIND 103
## 19 BLIZZARD 101
## 20 HIGH SURF 101Results
We plot the results by merging the fatality and injury datasets
HealthHarm <- merge(Fatalities, Injuries, by.x = "EVTYPE", by.y = "EVTYPE")
HealthHarm <-arrange (HealthHarm,desc(FATALITIES+INJURIES))
Events <- HealthHarm$EVTYPE
barplot(t(HealthHarm[,-1]), names.arg = Events, ylim = c(0,95000), beside = T, cex.names = 0.8, las=2, col = c("light blue", "dark blue"), main="Events Harmful for Population Health")
legend("topright",c("Fatalities","Injuries"),fill=c("light blue","dark blue"),bty = "n")
From the plot above, we see that Tornados are most harmful for population health.
2. Across the United States, which types of events have the greatest economic consequences?
Data Processing
We convert the propdmg and cropdmg data from factors H,K, etc to numbers
StormData$PROPDAMAGE = 0
StormData[StormData$PROPDMGEXP == "H", ]$PROPDAMAGE = StormData[StormData$PROPDMGEXP == "H", ]$PROPDMG * 10^2
StormData[StormData$PROPDMGEXP == "K", ]$PROPDAMAGE = StormData[StormData$PROPDMGEXP == "K", ]$PROPDMG * 10^3
StormData[StormData$PROPDMGEXP == "M", ]$PROPDAMAGE = StormData[StormData$PROPDMGEXP == "M", ]$PROPDMG * 10^6
StormData[StormData$PROPDMGEXP == "B", ]$PROPDAMAGE = StormData[StormData$PROPDMGEXP == "B", ]$PROPDMG * 10^9
StormData$CROPDAMAGE = 0
StormData[StormData$CROPDMGEXP == "H", ]$CROPDAMAGE = StormData[StormData$CROPDMGEXP == "H", ]$CROPDMG * 10^2
StormData[StormData$CROPDMGEXP == "K", ]$CROPDAMAGE = StormData[StormData$CROPDMGEXP == "K", ]$CROPDMG * 10^3
StormData[StormData$CROPDMGEXP == "M", ]$CROPDAMAGE = StormData[StormData$CROPDMGEXP == "M", ]$CROPDMG * 10^6
StormData[StormData$CROPDMGEXP == "B", ]$CROPDAMAGE = StormData[StormData$CROPDMGEXP == "B", ]$CROPDMG * 10^9We calculate the aggregate of economic damage by merging propdmg and cropdmg
EconomicHarm <- aggregate(PROPDAMAGE + CROPDAMAGE ~ EVTYPE, StormData, sum)
names(EconomicHarm) = c("EVENT_TYPE", "TOTAL_DAMAGE")
EconomicHarm <- arrange(EconomicHarm, desc(TOTAL_DAMAGE))
EconomicHarm <- EconomicHarm[1:20, ]
EconomicHarm$TOTAL_DAMAGE <- EconomicHarm$TOTAL_DAMAGE/10^9
EconomicHarm$EVENT_TYPE <- factor(EconomicHarm$EVENT_TYPE, levels = EconomicHarm$EVENT_TYPE)
head(EconomicHarm)## EVENT_TYPE TOTAL_DAMAGE
## 1 FLOOD 150.31968
## 2 HURRICANE/TYPHOON 71.91371
## 3 TORNADO 57.34061
## 4 STORM SURGE 43.32354
## 5 HAIL 18.75290
## 6 FLASH FLOOD 17.56213Results
We plot the results to understand the economic damage
with(EconomicHarm, barplot(TOTAL_DAMAGE, names.arg = EVENT_TYPE, beside = T, cex.names = 0.8, las=2, col = "purple", main = "Total Property and Crop Damage by Top 20 Event Types", ylab = "Total Damage in USD (10^9)"))
From the plot above, we see that Floods are most harmful for economic damage.