Population Health and Economic Impact of Severe Weather Events in U.S.
ZHONGHENG CHEN
2017-06-04
Synopsis
The basic goal of this report is to explore the NOAA Storm Database and answer some basic questions about severe weather events. In this report we aim to find out which types of events are most harmful with respect to population health and which types of events have the greatest economic consequences across United States.
Data Processing
1. Data Source
- The data for this study come from the U.S. National Oceanic and Atmospheric Administration’s (NOAA) database.The data can be obtained from the course web site: Strom Data.
- The 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 good records. More recent years should be considered more complete.
- The definition & construction of variables are available from National Weather Service Storm Data Documentation.
- National Climatic Data Center Storm Events FAQ
2. Loading Data
setwd("/Users/benchan/Google Drive/R/Reproducible Research/")
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", destfile = "StormData.csv.bz2")
storm_data <- read.csv("StormData.csv.bz2", stringsAsFactors = FALSE)3. Subseting Data
- Check out the structure and summary of the data set.
str(storm_data)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : chr "4/18/1950 0:00:00" "4/18/1950 0:00:00" "2/20/1951 0:00:00" "6/8/1951 0:00:00" ...
## $ BGN_TIME : chr "0130" "0145" "1600" "0900" ...
## $ TIME_ZONE : chr "CST" "CST" "CST" "CST" ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: chr "MOBILE" "BALDWIN" "FAYETTE" "MADISON" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : chr "" "" "" "" ...
## $ BGN_LOCATI: chr "" "" "" "" ...
## $ END_DATE : chr "" "" "" "" ...
## $ END_TIME : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ END_LOCATI: chr "" "" "" "" ...
## $ 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: chr "K" "K" "K" "K" ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr "" "" "" "" ...
## $ WFO : chr "" "" "" "" ...
## $ STATEOFFIC: chr "" "" "" "" ...
## $ ZONENAMES : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...summary(storm_data)## STATE__ BGN_DATE BGN_TIME TIME_ZONE
## Min. : 1.0 Length:902297 Length:902297 Length:902297
## 1st Qu.:19.0 Class :character Class :character Class :character
## Median :30.0 Mode :character Mode :character Mode :character
## Mean :31.2
## 3rd Qu.:45.0
## Max. :95.0
##
## COUNTY COUNTYNAME STATE EVTYPE
## Min. : 0.0 Length:902297 Length:902297 Length:902297
## 1st Qu.: 31.0 Class :character Class :character Class :character
## Median : 75.0 Mode :character Mode :character Mode :character
## Mean :100.6
## 3rd Qu.:131.0
## Max. :873.0
##
## BGN_RANGE BGN_AZI BGN_LOCATI
## Min. : 0.000 Length:902297 Length:902297
## 1st Qu.: 0.000 Class :character Class :character
## Median : 0.000 Mode :character Mode :character
## Mean : 1.484
## 3rd Qu.: 1.000
## Max. :3749.000
##
## END_DATE END_TIME COUNTY_END COUNTYENDN
## Length:902297 Length:902297 Min. :0 Mode:logical
## Class :character Class :character 1st Qu.:0 NA's:902297
## Mode :character Mode :character Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
##
## END_RANGE END_AZI END_LOCATI
## Min. : 0.0000 Length:902297 Length:902297
## 1st Qu.: 0.0000 Class :character Class :character
## Median : 0.0000 Mode :character Mode :character
## Mean : 0.9862
## 3rd Qu.: 0.0000
## Max. :925.0000
##
## LENGTH WIDTH F MAG
## Min. : 0.0000 Min. : 0.000 Min. :0.0 Min. : 0.0
## 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.:0.0 1st Qu.: 0.0
## Median : 0.0000 Median : 0.000 Median :1.0 Median : 50.0
## Mean : 0.2301 Mean : 7.503 Mean :0.9 Mean : 46.9
## 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.:1.0 3rd Qu.: 75.0
## Max. :2315.0000 Max. :4400.000 Max. :5.0 Max. :22000.0
## NA's :843563
## FATALITIES INJURIES PROPDMG
## Min. : 0.0000 Min. : 0.0000 Min. : 0.00
## 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.00
## Median : 0.0000 Median : 0.0000 Median : 0.00
## Mean : 0.0168 Mean : 0.1557 Mean : 12.06
## 3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.: 0.50
## Max. :583.0000 Max. :1700.0000 Max. :5000.00
##
## PROPDMGEXP CROPDMG CROPDMGEXP
## Length:902297 Min. : 0.000 Length:902297
## Class :character 1st Qu.: 0.000 Class :character
## Mode :character Median : 0.000 Mode :character
## Mean : 1.527
## 3rd Qu.: 0.000
## Max. :990.000
##
## WFO STATEOFFIC ZONENAMES LATITUDE
## Length:902297 Length:902297 Length:902297 Min. : 0
## Class :character Class :character Class :character 1st Qu.:2802
## Mode :character Mode :character Mode :character Median :3540
## Mean :2875
## 3rd Qu.:4019
## Max. :9706
## NA's :47
## LONGITUDE LATITUDE_E LONGITUDE_ REMARKS
## Min. :-14451 Min. : 0 Min. :-14455 Length:902297
## 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0 Class :character
## Median : 8707 Median : 0 Median : 0 Mode :character
## Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. : 17124 Max. :9706 Max. :106220
## NA's :40
## REFNUM
## Min. : 1
## 1st Qu.:225575
## Median :451149
## Mean :451149
## 3rd Qu.:676723
## Max. :902297
## - To reduce the computing time of processing this huge dataset, I subset only the relevant variables for the following study, which are the event types (variable “EVTYPE”) and the figures related to population health impacts (variable “Fatalities” & “Injures”“) and economic consequences (variable”PropDMG“,”PROPDMGEXP“,”CROPDMG" & “CROPDMGEXP”).
Health Impact
library(dplyr)
storm_fatalities <- storm_data %>% select(EVTYPE, FATALITIES) %>% group_by(EVTYPE) %>% summarise(total.fatalities = sum(FATALITIES)) %>% arrange(-total.fatalities)
head(storm_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 224storm_injuries <- storm_data %>% select(EVTYPE, INJURIES) %>% group_by(EVTYPE) %>% summarise(total.injuries = sum(INJURIES)) %>% arrange(-total.injuries)
head(storm_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 1361Economic Impact
- The data provides two types of economic impact, namely property damage (PROPDMG) and crop damage (CROPDMG). The actual damage in $USD is indicated by PROPDMGEXP and CROPDMGEXP parameters. the index in the PROPDMGEXP and CROPDMGEXP can be interpreted as the following:
- H, h -> hundreds = x100
- K, K -> kilos = x1,000
- M, m -> millions = x1,000,000
- B,b -> billions = x1,000,000,000
- (+) -> x1
- (-) -> x0
- (?) -> x0
- blank -> x0
storm_damage <- storm_data %>% select(EVTYPE, PROPDMG,PROPDMGEXP,CROPDMG,CROPDMGEXP)
Symbol <- sort(unique(as.character(storm_damage$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)
storm_damage$Prop.Multiplier <- convert.Multiplier$Multiplier[match(storm_damage$PROPDMGEXP, convert.Multiplier$Symbol)]
storm_damage$Crop.Multiplier <- convert.Multiplier$Multiplier[match(storm_damage$CROPDMGEXP, convert.Multiplier$Symbol)]
storm_damage <- storm_damage %>% mutate(PROPDMG = PROPDMG*Prop.Multiplier) %>% mutate(CROPDMG = CROPDMG*Crop.Multiplier) %>% mutate(TOTAL.DMG = PROPDMG+CROPDMG)
storm_damage_total <- storm_damage %>% group_by(EVTYPE) %>% summarize(TOTAL.DMG.EVTYPE = sum(TOTAL.DMG))%>% arrange(-TOTAL.DMG.EVTYPE)
head(storm_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. As shown in the figures, tornado is the most harmful event with respect to population health.
library(ggplot2)
plot_fatalities <- ggplot(storm_fatalities[1:10,],
aes(x=reorder(EVTYPE, -total.fatalities),
y=total.fatalities, fill = EVTYPE)) +
geom_bar(stat="identity") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
plot.title = element_text(hjust = 0.5)) +
ggtitle("Top 10 Events with Highest Total Fatalities") +
labs(x = "EVENT TYPE", y = "Total Fatalities")
plot_fatalities
plot_injuries <- ggplot(storm_injuries[1:10,],
aes(x=reorder(EVTYPE, -total.injuries),
y=total.injuries, fill = EVTYPE)) +
geom_bar(stat="identity") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
plot.title = element_text(hjust = 0.5)) +
ggtitle("Top 10 Events with Highest Total Injuries") +
labs(x = "EVENT TYPE", y = "Total Injuries")
plot_injuries
Economic Impact
The top 10 events with the highest total economic damages (property and crop combined) are shown graphically. As shown in the figure, flood has greatest economic consequences.
plot_damage <- ggplot(storm_damage_total[1:10,],
aes(x=reorder(EVTYPE, -TOTAL.DMG.EVTYPE),
y=TOTAL.DMG.EVTYPE, fill = EVTYPE)) +
geom_bar(stat="identity") +
theme(axis.text.x = element_text(angle=90, vjust=0.5, hjust=1),
plot.title = element_text(hjust = 0.5)) +
ggtitle("Top 10 Events with Highest Economic Impact") +
labs(x="EVENT TYPE", y="Total Economic Impact ($USD)")
plot_damage