Effects of Different Weather Events on Economy and Health in the U.S.
Synopsis
An analysis of the effects of different weather events on how they cause injuries and fatalities as well as their effects on the economy in the U.S. This study uses the Injury, fatality, and crop and property damage report from the NOAA 1950-2011.
Basic settings
echo = TRUE # Always make code visible
options(scipen = 1) # Turn off scientific notations for numbers
library(R.utils)
library(ggplot2)## Warning: package 'ggplot2' was built under R version 3.1.1library(plyr)
require(gridExtra)## Loading required package: gridExtra## Warning: package 'gridExtra' was built under R version 3.1.1## Loading required package: gridData Processing
Read the generated csv file.
stormData <- read.csv("StormData.csv.bz2", sep = ",")
dim(stormData)## [1] 902297 37head(stormData, n = 2)## 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
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 0
## 2 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14 100 3 0 0
## 2 NA 0 2 150 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2Due to the lack of events recorded in earlier years, more consideration will be given to more recent years, as we are sure that records during these years are more complete.
if (dim(stormData)[2] == 37) {
stormData$year <- as.numeric(format(as.Date(stormData$BGN_DATE, format = "%m/%d/%Y %H:%M:%S"), "%Y"))
}
hist(stormData$year, breaks = 30,col="darkblue")
From the date above, we can see that the number of even starts to increase from 1995. We will then use data from a subset from 1990 to 2011 to utilize years with good records.
storm <- stormData[stormData$year >= 1995, ]
dim(storm)## [1] 681500 38Impact on Public Health
In this section, we check the number of fatalities and injuries that are caused by the weather events. We would like to get the first 15 most severe types of weather events.
sortHelper <- function(fieldName, top = 15, dataset = stormData) {
index <- which(colnames(dataset) == fieldName)
field <- aggregate(dataset[, index], by = list(dataset$EVTYPE), FUN = "sum")
names(field) <- c("EVTYPE", fieldName)
field <- arrange(field, field[, 2], decreasing = T)
field <- head(field, n = top)
field <- within(field, EVTYPE <- factor(x = EVTYPE, levels = field$EVTYPE))
return(field)
}
fatalities <- sortHelper("FATALITIES", dataset = storm)
injuries <- sortHelper("INJURIES", dataset = storm)Impact on Economy
In this section we convert property damage and crop damage data into comparable numerical values, and compare which weather event causes the most dame for each category.
convertHelper <- function(dataset = storm, fieldName, newFieldName) {
totalLen <- dim(dataset)[2]
index <- which(colnames(dataset) == fieldName)
dataset[, index] <- as.character(dataset[, index])
logic <- !is.na(toupper(dataset[, index]))
dataset[logic & toupper(dataset[, index]) == "B", index] <- "9"
dataset[logic & toupper(dataset[, index]) == "M", index] <- "6"
dataset[logic & toupper(dataset[, index]) == "K", index] <- "3"
dataset[logic & toupper(dataset[, index]) == "H", index] <- "2"
dataset[logic & toupper(dataset[, index]) == "", index] <- "0"
dataset[, index] <- as.numeric(dataset[, index])
dataset[is.na(dataset[, index]), index] <- 0
dataset <- cbind(dataset, dataset[, index - 1] * 10^dataset[, index])
names(dataset)[totalLen + 1] <- newFieldName
return(dataset)
}
storm <- convertHelper(storm, "PROPDMGEXP", "propertyDamage")## Warning: NAs introduced by coercionstorm <- convertHelper(storm, "CROPDMGEXP", "cropDamage")## Warning: NAs introduced by coercionnames(storm)## [1] "STATE__" "BGN_DATE" "BGN_TIME" "TIME_ZONE"
## [5] "COUNTY" "COUNTYNAME" "STATE" "EVTYPE"
## [9] "BGN_RANGE" "BGN_AZI" "BGN_LOCATI" "END_DATE"
## [13] "END_TIME" "COUNTY_END" "COUNTYENDN" "END_RANGE"
## [17] "END_AZI" "END_LOCATI" "LENGTH" "WIDTH"
## [21] "F" "MAG" "FATALITIES" "INJURIES"
## [25] "PROPDMG" "PROPDMGEXP" "CROPDMG" "CROPDMGEXP"
## [29] "WFO" "STATEOFFIC" "ZONENAMES" "LATITUDE"
## [33] "LONGITUDE" "LATITUDE_E" "LONGITUDE_" "REMARKS"
## [37] "REFNUM" "year" "propertyDamage" "cropDamage"options(scipen=999)
property <- sortHelper("propertyDamage", dataset = storm)
crop <- sortHelper("cropDamage", dataset = storm)Results
As for the impact on public health, we have got two sorted lists of severe weather events below by the number of people badly affected.
fatalities## EVTYPE FATALITIES
## 1 EXCESSIVE HEAT 1903
## 2 TORNADO 1545
## 3 FLASH FLOOD 934
## 4 HEAT 924
## 5 LIGHTNING 729
## 6 FLOOD 423
## 7 RIP CURRENT 360
## 8 HIGH WIND 241
## 9 TSTM WIND 241
## 10 AVALANCHE 223
## 11 RIP CURRENTS 204
## 12 WINTER STORM 195
## 13 HEAT WAVE 161
## 14 THUNDERSTORM WIND 131
## 15 EXTREME COLD 126injuries## EVTYPE INJURIES
## 1 TORNADO 21765
## 2 FLOOD 6769
## 3 EXCESSIVE HEAT 6525
## 4 LIGHTNING 4631
## 5 TSTM WIND 3630
## 6 HEAT 2030
## 7 FLASH FLOOD 1734
## 8 THUNDERSTORM WIND 1426
## 9 WINTER STORM 1298
## 10 HURRICANE/TYPHOON 1275
## 11 HIGH WIND 1093
## 12 HAIL 916
## 13 WILDFIRE 911
## 14 HEAVY SNOW 751
## 15 FOG 718And the following is a pair of graphs of total fatalities and total injuries affected by these severe weather events.
fatalitiesPlot <- qplot(EVTYPE, data = fatalities, weight = FATALITIES, geom = "bar", binwidth = 1) +
scale_y_continuous("Number of Fatalities") +
theme(axis.text.x = element_text(angle = 20,
hjust = 1)) + xlab("Severe Weather Type") +
ggtitle("Total Fatalities by Severe Weather\n Events in the U.S.\n from 1995 - 2011")
injuriesPlot <- qplot(EVTYPE, data = injuries, weight = INJURIES, geom = "bar", binwidth = 1) +
scale_y_continuous("Number of Injuries") +
theme(axis.text.x = element_text(angle = 20,
hjust = 1)) + xlab("Severe Weather Type") +
ggtitle("Total Injuries by Severe Weather\n Events in the U.S.\n from 1995 - 2011")
grid.arrange(fatalitiesPlot, injuriesPlot, nrow = 2)
Based on the above histograms, we find that excessive heat and tornado cause most fatalities; tornato causes most injuries in the United States from 1995 to 2011.
As for the impact on economy, we have got two sorted lists below by the amount of money cost by damages.
property## EVTYPE propertyDamage
## 1 FLOOD 144022037057
## 2 HURRICANE/TYPHOON 69305840000
## 3 STORM SURGE 43193536000
## 4 TORNADO 24935939545
## 5 FLASH FLOOD 16047794571
## 6 HAIL 15048722103
## 7 HURRICANE 11812819010
## 8 TROPICAL STORM 7653335550
## 9 HIGH WIND 5259785375
## 10 WILDFIRE 4759064000
## 11 STORM SURGE/TIDE 4641188000
## 12 TSTM WIND 4482361440
## 13 ICE STORM 3643555810
## 14 THUNDERSTORM WIND 3399282992
## 15 HURRICANE OPAL 3172846000crop## EVTYPE cropDamage
## 1 DROUGHT 13922066000
## 2 FLOOD 5422810400
## 3 HURRICANE 2741410000
## 4 HAIL 2614127070
## 5 HURRICANE/TYPHOON 2607872800
## 6 FLASH FLOOD 1343915000
## 7 EXTREME COLD 1292473000
## 8 FROST/FREEZE 1094086000
## 9 HEAVY RAIN 728399800
## 10 TROPICAL STORM 677836000
## 11 HIGH WIND 633561300
## 12 TSTM WIND 553947350
## 13 EXCESSIVE HEAT 492402000
## 14 THUNDERSTORM WIND 414354000
## 15 HEAT 401411500And the following is a pair of graphs of total property damage and total crop damage affected by these severe weather events.
propertyPlot <- qplot(EVTYPE, data = property, weight = propertyDamage, geom = "bar", binwidth = 1) +
theme(axis.text.x = element_text(angle = 20, hjust = 1)) + scale_y_continuous("Property Damage in US dollars")+
xlab("Severe Weather Type") + ggtitle("Total Property Damage by\n Severe Weather Events in\n the U.S. from 1995 - 2011")
cropPlot<- qplot(EVTYPE, data = crop, weight = cropDamage, geom = "bar", binwidth = 1) +
theme(axis.text.x = element_text(angle = 20, hjust = 1)) + scale_y_continuous("Crop Damage in US dollars") +
xlab("Severe Weather Type") + ggtitle("Total Crop Damage by \nSevere Weather Events in\n the U.S. from 1995 - 2011")
grid.arrange(propertyPlot, cropPlot, nrow = 2)
Conclusion
High injuries and fatalities are reported during excessive heat and tornado events. While typhonns, droughts, and floods cause the greatest economic damage.