Impact of Weather Events on Public Health and Economics in US
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 dataset. The results show that tornado is the most harmful weather event to public health. As for the economic loss, flood causes the largest property loss and drought makes the largest crop damage.
Data Processing
The events in the dataset start in the year 1950 and end in November 2011. In the earlier years of the dataset there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete. The data used in this report are available here [47Mb].
After loading data from bzip2 file, one can find that the types of events are stored in EVTYPE variable,the fatalities are stored in FATALITIES, the injuries are stored in INJURIES, the property damage is stored in PROPDMG and PROPDMGEXP, and the crop damage is stored in CROPDMG and CROPDMGEXP.
Sys.setlocale("LC_ALL", "C")
data = read.csv(bzfile("repdata-data-StormData.csv.bz2"),stringsAsFactors=FALSE)Before analysis, we convert the items in EVTYPE to lowercase and replace all punctuation and characters with a space.
data2 = data
# before processing
length(unique(data2$EVTYPE)) ## [1] 985data2$EVTYPE = tolower(data2$EVTYPE)
data2$EVTYPE = gsub("[[:blank:][:punct:]+]", " ", data2$EVTYPE)
# after processing
length(unique(data2$EVTYPE)) ## [1] 874One can see that after the pre-processing, the number of unique event types reduce significantly(985 V.S. 874).
In addtion to pre-processing EVTYPE, we also need to deal with the exponents of PROPDMGEXP and CROPDMGEXP. The exponents of PROPDMGEXP and CROPDMGEXP are shown as follow.
# PROPDMGEXP
unique(data2$PROPDMGEXP)## [1] "K" "M" "" "B" "m" "+" "0" "5" "6" "?" "4" "2" "3" "h" "7" "H" "-"
## [18] "1" "8"# CROPDMGEXP
unique(data2$CROPDMGEXP)## [1] "" "M" "K" "m" "B" "?" "0" "k" "2"To deal with this exponent issue, we build a Converter function to transfer the exponent into a number.
Converter <- function(e) {
# h -> hundred, k -> thousand, m -> million, b -> billion
if (e %in% c('h', 'H'))
return(2)
else if (e %in% c('k', 'K'))
return(3)
else if (e %in% c('m', 'M'))
return(6)
else if (e %in% c('b', 'B'))
return(9)
else if (!is.na(as.numeric(e))) # if a digit
return(as.numeric(e))
else
return(0)
}Results
How Weather Events affect Public Health
Here is the top 20 events of the highest fatalities.
# weather events causes death
FATALITIES = head(sort(tapply(data2$FATALITIES,data2$EVTYPE, sum),decreasing=TRUE), n=20)
FATALITIES = data.frame("Event"=names(FATALITIES),"Fatalities"=unname(FATALITIES))
FATALITIES## Event 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 162
## 15 thunderstorm wind 133
## 16 heavy snow 127
## 17 extreme cold wind chill 125
## 18 high surf 104
## 19 strong wind 103
## 20 blizzard 101library(ggplot2)
library(scales)
ggplot(data=FATALITIES,
aes(x=reorder(Event,Fatalities), y=Fatalities, fill=Fatalities)) +
geom_bar(stat="identity") +
coord_flip() +
ylab("Total number of fatalities") +
xlab("Event type") +
theme(legend.position="none") +
ggtitle("Top deadly weather events in the US (1950-2011)") +
scale_y_log10(breaks =
trans_breaks("log10", function(x) 10^x), labels = trans_format("log10", math_format(10^.x)))
Here is the top 20 events of the highest injuries.
# weather events causes injuries
INJURIES = head(sort(tapply(data2$INJURIES,data2$EVTYPE, sum),decreasing = TRUE), n=20)
INJURIES = data.frame("Event"=names(INJURIES),"Injuries"=unname(INJURIES))
INJURIES## Event 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 440It is obvious that the tornado is the most harmful weather event to population health, which causes more than 5,000 death and more than 10,000 injuries in the last 60 years.
How Weather Events affect Economics
First, we combine the damage loss and the damage loss exponent together, i.e. \[PROPDMG^{PROPDMGEXP}\ \ and\ \ CROPDMG^{CROPDMGEXP},\] to get the total loss in dollars.
PROPDMGEXP <- sapply(data2$PROPDMGEXP, Converter)
data2$PROPDMG <- data2$PROPDMG*(10^unname(PROPDMGEXP))
CROPDMGEXP <- sapply(data2$CROPDMGEXP, Converter)
data2$CROPDMG <- data2$CROPDMG * (10^unname(CROPDMGEXP))Here is the top 20 events of the highest property damage.
# weather events causes death
PROPDMG = head(sort(tapply(data2$PROPDMG,data2$EVTYPE, sum),decreasing=TRUE), n=20)
PROPDMG = data.frame("Event"=names(PROPDMG),"PROPDMG"=unname(PROPDMG))
PROPDMG## Event PROPDMG
## 1 flood 144657709807
## 2 hurricane typhoon 69305840000
## 3 tornado 56947380677
## 4 storm surge 43323536000
## 5 flash flood 16822673979
## 6 hail 15735267513
## 7 hurricane 11868319010
## 8 tropical storm 7703890550
## 9 winter storm 6688497251
## 10 high wind 5270046295
## 11 river flood 5118945500
## 12 wildfire 4765114000
## 13 storm surge tide 4641188000
## 14 tstm wind 4484958495
## 15 ice storm 3944927860
## 16 thunderstorm wind 3483122472
## 17 hurricane opal 3172846000
## 18 wild forest fire 3001829500
## 19 heavy rain severe weather 2500000000
## 20 thunderstorm winds 1944590859ggplot(data=PROPDMG,
aes(x=reorder(Event,PROPDMG), y=PROPDMG, fill=PROPDMG)) +
geom_bar(stat="identity") +
coord_flip() +
ylab("Property damage in dollars") +
xlab("Event type") +
theme(legend.position="none") +
ggtitle("Weather costs to the US economy (1950-2011)") +
scale_y_log10(breaks =
trans_breaks("log10", function(x) 10^x), labels = trans_format("log10", math_format(10^.x)))
Here is the top 20 events of the highest crop damage.
# weather events causes injuries
CROPDMG = head(sort(tapply(data2$CROPDMG,data2$EVTYPE, sum),decreasing = TRUE), n=20)
CROPDMG = data.frame("Event"=names(CROPDMG),"CROPDMG"=unname(CROPDMG))
CROPDMG## Event CROPDMG
## 1 drought 13972566000
## 2 flood 5661968450
## 3 river flood 5029459000
## 4 ice storm 5022113500
## 5 hail 3025954473
## 6 hurricane 2741910000
## 7 hurricane typhoon 2607872800
## 8 flash flood 1421317100
## 9 extreme cold 1312973000
## 10 frost freeze 1094186000
## 11 heavy rain 733399800
## 12 tropical storm 678346000
## 13 high wind 638571300
## 14 tstm wind 554007350
## 15 excessive heat 492402000
## 16 freeze 456725000
## 17 tornado 414953270
## 18 thunderstorm wind 414843050
## 19 heat 401461500
## 20 damaging freeze 296230000ggplot(data=CROPDMG,
aes(x=reorder(Event,CROPDMG), y=CROPDMG, fill=CROPDMG)) +
geom_bar(stat="identity") +
coord_flip() +
ylab("Crop damage in dollars") +
xlab("Event type") +
theme(legend.position="none") +
ggtitle("Weather costs to the US economy (1950-2011)") +
scale_y_log10(breaks =
trans_breaks("log10", function(x) 10^x), labels = trans_format("log10", math_format(10^.x)))
The figures show that the flood causes the largest property loss while the drought makes the largest crop damage.