PA2
twan
6/14/2020
Impact of Severe Weather Events on Public Health and Economy in the United States
Synonpsis
In this report, we aim to analyze the impact of different weather events on public health and economy based on the storm database collected from the U.S. National Oceanic and Atmospheric Administration’s (NOAA) from 1950 - 2011. We will use the estimates of fatalities, injuries, property and crop damage to decide which types of event are most harmful to the population health and economy. 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 consequences.
Basic settings
echo = TRUE # Always make code visible
options(scipen = 1) # Turn off scientific notations for numbers
library(utils)
library(ggplot2)
library(plyr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionData Processing
We read the csv file. If the data already exists in the working environment, we do not need to load it again. Otherwise, we read the csv file.
if (!"repdata_data_StormData.csv.bz2" %in% dir("..")) {
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", destfile = "repdata_data_StormData.csv.bz2")
}
if (!"df" %in% ls()) {
df <- read.table("repdata_data_StormData.csv.bz2", sep=",", header=TRUE)
}
dim(df)## [1] 902297 37head(df, n = 2)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE EVTYPE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL TORNADO
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL TORNADO
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END COUNTYENDN
## 1 0 0 NA
## 2 0 0 NA
## END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES INJURIES PROPDMG
## 1 0 14 100 3 0 0 15 25.0
## 2 0 2 150 2 0 0 0 2.5
## PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES LATITUDE LONGITUDE
## 1 K 0 3040 8812
## 2 K 0 3042 8755
## LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3051 8806 1
## 2 0 0 2There are 902297 rows and 37 columns in total.
Impact on Public Health
To evaluate the health impact, the total fatalities and the total injuries for each event type (EVTYPE) are calculated. The codes for this calculation are shown as follows.
df.fatalities <- df %>% select(EVTYPE, FATALITIES) %>% group_by(EVTYPE) %>% summarise(total.fatalities = sum(FATALITIES)) %>% arrange(-total.fatalities)## `summarise()` ungrouping output (override with `.groups` argument)head(df.fatalities, 10)## # A tibble: 10 x 2
## EVTYPE total.fatalities
## <chr> <dbl>
## 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 224df.injuries <- df %>% select(EVTYPE, INJURIES) %>% group_by(EVTYPE) %>% summarise(total.injuries = sum(INJURIES)) %>% arrange(-total.injuries)## `summarise()` ungrouping output (override with `.groups` argument)head(df.injuries, 10)## # A tibble: 10 x 2
## EVTYPE total.injuries
## <chr> <dbl>
## 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 1361Impact on Economy
The data provides two types of economic impact, namely property damage (PROPDMG) and crop damage (CROPDMG). The actual damage in $USD is indicated by PROPDMGEXP and CROPDMGEXP parameters. According to this link, the index in the PROPDMGEXP and CROPDMGEXP can be interpreted as the following:
H, h -> hundreds = x100 K, K -> kilos = x1,000 M, m -> millions = x1,000,000 B,b -> billions = x1,000,000,000 (+) -> x1 (-) -> x0 (?) -> x0 blank -> x0
The total damage caused by each event type is calculated with the following code.
df.damage <- df %>% select(EVTYPE, PROPDMG,PROPDMGEXP,CROPDMG,CROPDMGEXP)
Symbol <- sort(unique(as.character(df.damage$PROPDMGEXP)))
Multiplier <- c(0,0,0,1,10,10,10,10,10,10,10,10,10,10^9,10^2,10^2,10^3,10^6,10^6)
convert.Multiplier <- data.frame(Symbol, Multiplier)
df.damage$Prop.Multiplier <- convert.Multiplier$Multiplier[match(df.damage$PROPDMGEXP, convert.Multiplier$Symbol)]
df.damage$Crop.Multiplier <- convert.Multiplier$Multiplier[match(df.damage$CROPDMGEXP, convert.Multiplier$Symbol)]
df.damage <- df.damage %>% mutate(PROPDMG = PROPDMG*Prop.Multiplier) %>% mutate(CROPDMG = CROPDMG*Crop.Multiplier) %>% mutate(TOTAL.DMG = PROPDMG+CROPDMG)
df.damage.total <- df.damage %>% group_by(EVTYPE) %>% summarize(TOTAL.DMG.EVTYPE = sum(TOTAL.DMG))%>% arrange(-TOTAL.DMG.EVTYPE) ## `summarise()` ungrouping output (override with `.groups` argument)head(df.damage.total,10)## # A tibble: 10 x 2
## EVTYPE TOTAL.DMG.EVTYPE
## <chr> <dbl>
## 1 FLOOD 150319678250
## 2 HURRICANE/TYPHOON 71913712800
## 3 TORNADO 57352117607
## 4 STORM SURGE 43323541000
## 5 FLASH FLOOD 17562132111
## 6 DROUGHT 15018672000
## 7 HURRICANE 14610229010
## 8 RIVER FLOOD 10148404500
## 9 ICE STORM 8967041810
## 10 TROPICAL STORM 8382236550Results
Health Impact
The top 10 events with the highest total fatalities and injuries are shown graphically.
g <- ggplot(df.fatalities[1:10,], aes(x=reorder(EVTYPE, -total.fatalities), y=total.fatalities))+geom_bar(stat="identity") + theme(axis.text.x = element_text(angle=90, vjust=0.5, hjust=1))+ggtitle("Top 10 Events with Highest Total Fatalities") +labs(x="EVENT TYPE", y="Total Fatalities")
g
g <- ggplot(df.injuries[1:10,], aes(x=reorder(EVTYPE, -total.injuries), y=total.injuries))+geom_bar(stat="identity") + theme(axis.text.x = element_text(angle=90, vjust=0.5, hjust=1))+ggtitle("Top 10 Events with Highest Total Injuries") +labs(x="EVENT TYPE", y="Total Injuries")
g
Based on the above histograms, we find that tornado causes the highest in both the total fatality and injury count.
Economic Impact
The top 10 events with the highest total economic damages (property and crop combined) are shown graphically.
g <- ggplot(df.damage.total[1:10,], aes(x=reorder(EVTYPE, -TOTAL.DMG.EVTYPE), y=TOTAL.DMG.EVTYPE))+geom_bar(stat="identity") + theme(axis.text.x = element_text(angle=90, vjust=0.5, hjust=1))+ggtitle("Top 10 Events with Highest Economic Impact") +labs(x="EVENT TYPE", y="Total Economic Impact ($USD)")
g
As shown in the figure, flood has the highest economic impact.
Conclusion
From these data, we found that excessive heat and tornado are most harmful with respect to population health, while flood and hurricane/typhoon have the greatest economic consequences.