An Analysis Report of Health and Economic Impact by Severe Weather Events
Pushkar Sinngh Bhauryal
December 14, 2015
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.
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 Processing
Loading Library
library(ggplot2)## Warning: package 'ggplot2' was built under R version 3.2.3library(gridExtra)## Warning: package 'gridExtra' was built under R version 3.2.3library(dplyr)## Warning: package 'dplyr' was built under R version 3.2.3##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionLoading Data
# if (!file.exists("c:/coursera/storm.csv.bz2")) {
# download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2",
# "c:/coursera/storm.csv.bz2")
# }
# # unzip file
# if (!file.exists("c:/coursera/storm.csv")) {
# library(R.utils)
# bunzip2("c:/coursera/storm.csv.bz2", "c:/coursera/storm.csv", remove = FALSE)
# }
# # load data into R
#storm_data <- read.csv("c:/coursera/storm.csv")
storm_data<- read.csv("repdata_data_StormData.csv")dim(storm_data)## [1] 902297 37str(storm_data)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : Factor w/ 16335 levels "1/1/1966 0:00:00",..: 6523 6523 4242 11116 2224 2224 2260 383 3980 3980 ...
## $ BGN_TIME : Factor w/ 3608 levels "00:00:00 AM",..: 272 287 2705 1683 2584 3186 242 1683 3186 3186 ...
## $ TIME_ZONE : Factor w/ 22 levels "ADT","AKS","AST",..: 7 7 7 7 7 7 7 7 7 7 ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: Factor w/ 29601 levels "","5NM E OF MACKINAC BRIDGE TO PRESQUE ISLE LT MI",..: 13513 1873 4598 10592 4372 10094 1973 23873 24418 4598 ...
## $ STATE : Factor w/ 72 levels "AK","AL","AM",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ EVTYPE : Factor w/ 985 levels " HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : Factor w/ 35 levels ""," N"," NW",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_LOCATI: Factor w/ 54429 levels "","- 1 N Albion",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_DATE : Factor w/ 6663 levels "","1/1/1993 0:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_TIME : Factor w/ 3647 levels ""," 0900CST",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ COUNTY_END: num 0 0 0 0 0 0 0 0 0 0 ...
## $ COUNTYENDN: logi NA NA NA NA NA NA ...
## $ END_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ END_AZI : Factor w/ 24 levels "","E","ENE","ESE",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_LOCATI: Factor w/ 34506 levels "","- .5 NNW",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ LENGTH : num 14 2 0.1 0 0 1.5 1.5 0 3.3 2.3 ...
## $ WIDTH : num 100 150 123 100 150 177 33 33 100 100 ...
## $ F : int 3 2 2 2 2 2 2 1 3 3 ...
## $ MAG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ FATALITIES: num 0 0 0 0 0 0 0 0 1 0 ...
## $ INJURIES : num 15 0 2 2 2 6 1 0 14 0 ...
## $ PROPDMG : num 25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...
## $ PROPDMGEXP: Factor w/ 19 levels "","-","?","+",..: 17 17 17 17 17 17 17 17 17 17 ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: Factor w/ 9 levels "","?","0","2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ WFO : Factor w/ 542 levels ""," CI","$AC",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ STATEOFFIC: Factor w/ 250 levels "","ALABAMA, Central",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ ZONENAMES : Factor w/ 25112 levels ""," "| __truncated__,..: 1 1 1 1 1 1 1 1 1 1 ...
## $ LATITUDE : num 3040 3042 3340 3458 3412 ...
## $ LONGITUDE : num 8812 8755 8742 8626 8642 ...
## $ LATITUDE_E: num 3051 0 0 0 0 ...
## $ LONGITUDE_: num 8806 0 0 0 0 ...
## $ REMARKS : Factor w/ 436774 levels "","-2 at Deer Park\n",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...There are 902297 rows and 37 columns in storm data.
Exacting the data contain weather event, health and economic impact data
head(storm_data,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 2The events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of reliable/complete records.
Adding a year valiable
storm_data$year <- as.numeric(format(as.Date(storm_data$BGN_DATE,format="%m/%d/%Y %H:%M:%S"), "%Y"))ploting the storm by year
ggplot(aes(x=year,y=),data=storm_data)+geom_histogram(color="red",fill="green")+
xlab("Year")## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.## Warning: position_stack requires constant width: output may be incorrect
Based on the above histogram we can see that the event track beging increasing on 1990. so we will subset the data from 1990 to 2011
storm_subset<- storm_data%>%filter(year>=1990)
dim(storm_subset)## [1] 751740 38Impact on Public Health
In this section we will checke the no fatalities and injurieshat are caused by the severe weather events. We would like to get the first 10 most severe types of weather events.
storm_helper <- function(type, top=10, inputdata=storm_subset){
col_id<-which(colnames(inputdata)==type)
fields<- aggregate(inputdata[,col_id],by=list(inputdata$EVTYPE),FUN=sum)
names(fields)<- c("EVTYPE",type)
fields<- arrange(fields,desc(fields$type))
fields <- head(fields, n = top)
fields <- within(fields, EVTYPE <- factor(x = EVTYPE, levels = fields$EVTYPE))
return(fields)
}
fatalities <- storm_helper("FATALITIES")
injuries <- storm_helper("INJURIES")Impact on Economy
We will convert the property damage and crop damage data into comparable numerical forms.Both PROPDMGEXP and CROPDMGEXP columns record a multiplier for each observation where we have Hundred (H), Thousand (K), Million (M) and Billion (B).
convertHelper <- function (fieldName,newFieldName, dataset=storm_subset){
totalLen <- dim(dataset)[2]
index <- which(colnames(dataset) == fieldName)
dataset[, index] <- as.character(dataset[, index])
dataset[toupper(dataset[, index]) == "B", index] <- "9"
dataset[toupper(dataset[, index]) == "M", index] <- "6"
dataset[toupper(dataset[, index]) == "K", index] <- "3"
dataset[toupper(dataset[, index]) == "H", index] <- "2"
dataset[toupper(dataset[, index]) == "", index] <- "0"
dataset[, index] <- as.numeric(dataset[, index])
dataset <- cbind(dataset, dataset[, index - 1] * 10^dataset[, index])
names(dataset)[totalLen + 1] <- newFieldName
return(dataset)
head(dataset,1)
}
storm_subset<- convertHelper("PROPDMGEXP","propertyDamage")## Warning in convertHelper("PROPDMGEXP", "propertyDamage"): NAs introduced by
## coercionstorm_subset<- convertHelper("CROPDMGEXP","cropDamage")## Warning in convertHelper("CROPDMGEXP", "cropDamage"): NAs introduced by
## coercionPropety<- storm_helper("propertyDamage")
crop <- storm_helper("cropDamage")Result
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 1752
## 3 FLASH FLOOD 978
## 4 HEAT 937
## 5 LIGHTNING 816
## 6 FLOOD 470
## 7 RIP CURRENT 368
## 8 TSTM WIND 327
## 9 HIGH WIND 248
## 10 AVALANCHE 224injuries ## EVTYPE INJURIES
## 1 TORNADO 26674
## 2 FLOOD 6789
## 3 EXCESSIVE HEAT 6525
## 4 LIGHTNING 5230
## 5 TSTM WIND 5022
## 6 HEAT 2100
## 7 ICE STORM 1975
## 8 FLASH FLOOD 1777
## 9 THUNDERSTORM WIND 1488
## 10 WINTER STORM 1321following is a pair of graphs of total fatalities and total injuries affected by these severe weather events.
fatalitiesplot <- ggplot(aes(x=EVTYPE,weight = FATALITIES,stat = "identity"),data=fatalities)+geom_bar()+
scale_y_continuous("Number of Fatalities") +
ggtitle("Total Fatalities by Severe Weather\n Events in the U.S.\n from 1990 - 2011")+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
injuriesplot<- ggplot(aes(x=EVTYPE,weight =INJURIES ),data = injuries)+geom_histogram()+
scale_y_continuous("Number of Injuries") +
ggtitle("Total Injuries by Severe Weather\n Events in the U.S.\n from 1990 - 2011")+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
grid.arrange(fatalitiesplot, injuriesplot, ncol = 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 1990 to 2011.
As for the impact on economy, we have got two sorted lists below by the amount of money cost by damages.
Propety## EVTYPE propertyDamage
## 1 FLOOD 144657709807
## 2 HURRICANE/TYPHOON 69305840000
## 3 STORM SURGE 43323536000
## 4 HURRICANE 11868319010
## 5 TROPICAL STORM 7703890550
## 6 WINTER STORM 6688497251
## 7 RIVER FLOOD 5118945500
## 8 WILDFIRE 4765114000
## 9 STORM SURGE/TIDE 4641188000
## 10 TSTM WIND 4484928495crop## EVTYPE cropDamage
## 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 1292973000
## 10 FROST/FREEZE 1094086000The following is are graphs of total property damage and total crop damage affected by these severe weather events.
propertyplot <- ggplot(aes(x=EVTYPE,weight = propertyDamage),data=Propety)+geom_bar()+
scale_y_continuous("Property Damage in US dollars") +
ggtitle("Total Property Damage by Severe Weather\n Events in the U.S.\n from 1990 - 2011")+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
xlab("Severe Weather Type")
cropplot<- ggplot(aes(x=EVTYPE,weight =cropDamage ),data = crop)+geom_histogram()+
scale_y_continuous("Crop Damage in US dollars") +
ggtitle("Total Crop Damage by Severe Weather\n Events in the U.S.\n from 1990 - 2011")+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
grid.arrange(propertyplot, cropplot, ncol = 2)
Based on the histograms above, we find that flood and hurricane/typhoon cause most property damage; drought and flood causes most crop damage in the United States from 1990 to 2011.
Conclusion
From these data, we found that excessive heat and tornado are most harmful with respect to population health, while flood, drought, and hurricane/typhoon have the greatest economic impact.