Impact of Weather events on U.S. National Health and Economy
forhad
12/24/2019
Introduction
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 of this project.
Synopsis
This project involves exploring 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.
The analysis on the storm event database revealed that tornadoes are the most dangerous weather event to the populations health. The second most dangerous event type is excessive heat. The economic impact of weather events was also analyzed. Flash floods and thunderstorm winds caused billions of dollars in property damages between 1950 and 2011. The largest damage to crops were caused by droughts, followed by floods and hailing.
Data Preprocessing
- Loading required package
suppressMessages(library(R.utils))
suppressMessages(library(dplyr))
suppressMessages(library(ggplot2))- Getting and Loading the Data
if(!file.exists('storm_data.csv.bz2')){ ## if the source file not already downloaded
download.file('https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2', 'storm_data.csv.bz2')
## if the zip file not extracted yet
if(!file.exists('storm.csv')){
bunzip2('storm_data.csv.bz2', 'storm.csv')
}
}
storm <- read.csv('storm.csv', header = TRUE)- Taking a quick look into the data set
summary(storm)## STATE__ BGN_DATE BGN_TIME
## Min. : 1.0 5/25/2011 0:00:00: 1202 12:00:00 AM: 10163
## 1st Qu.:19.0 4/27/2011 0:00:00: 1193 06:00:00 PM: 7350
## Median :30.0 6/9/2011 0:00:00 : 1030 04:00:00 PM: 7261
## Mean :31.2 5/30/2004 0:00:00: 1016 05:00:00 PM: 6891
## 3rd Qu.:45.0 4/4/2011 0:00:00 : 1009 12:00:00 PM: 6703
## Max. :95.0 4/2/2006 0:00:00 : 981 03:00:00 PM: 6700
## (Other) :895866 (Other) :857229
## TIME_ZONE COUNTY COUNTYNAME STATE
## CST :547493 Min. : 0.0 JEFFERSON : 7840 TX : 83728
## EST :245558 1st Qu.: 31.0 WASHINGTON: 7603 KS : 53440
## MST : 68390 Median : 75.0 JACKSON : 6660 OK : 46802
## PST : 28302 Mean :100.6 FRANKLIN : 6256 MO : 35648
## AST : 6360 3rd Qu.:131.0 LINCOLN : 5937 IA : 31069
## HST : 2563 Max. :873.0 MADISON : 5632 NE : 30271
## (Other): 3631 (Other) :862369 (Other):621339
## EVTYPE BGN_RANGE BGN_AZI
## HAIL :288661 Min. : 0.000 :547332
## TSTM WIND :219940 1st Qu.: 0.000 N : 86752
## THUNDERSTORM WIND: 82563 Median : 0.000 W : 38446
## TORNADO : 60652 Mean : 1.484 S : 37558
## FLASH FLOOD : 54277 3rd Qu.: 1.000 E : 33178
## FLOOD : 25326 Max. :3749.000 NW : 24041
## (Other) :170878 (Other):134990
## BGN_LOCATI END_DATE END_TIME
## :287743 :243411 :238978
## COUNTYWIDE : 19680 4/27/2011 0:00:00: 1214 06:00:00 PM: 9802
## Countywide : 993 5/25/2011 0:00:00: 1196 05:00:00 PM: 8314
## SPRINGFIELD : 843 6/9/2011 0:00:00 : 1021 04:00:00 PM: 8104
## SOUTH PORTION: 810 4/4/2011 0:00:00 : 1007 12:00:00 PM: 7483
## NORTH PORTION: 784 5/30/2004 0:00:00: 998 11:59:00 PM: 7184
## (Other) :591444 (Other) :653450 (Other) :622432
## COUNTY_END COUNTYENDN END_RANGE END_AZI
## Min. :0 Mode:logical Min. : 0.0000 :724837
## 1st Qu.:0 NA's:902297 1st Qu.: 0.0000 N : 28082
## Median :0 Median : 0.0000 S : 22510
## Mean :0 Mean : 0.9862 W : 20119
## 3rd Qu.:0 3rd Qu.: 0.0000 E : 20047
## Max. :0 Max. :925.0000 NE : 14606
## (Other): 72096
## END_LOCATI LENGTH WIDTH
## :499225 Min. : 0.0000 Min. : 0.000
## COUNTYWIDE : 19731 1st Qu.: 0.0000 1st Qu.: 0.000
## SOUTH PORTION : 833 Median : 0.0000 Median : 0.000
## NORTH PORTION : 780 Mean : 0.2301 Mean : 7.503
## CENTRAL PORTION: 617 3rd Qu.: 0.0000 3rd Qu.: 0.000
## SPRINGFIELD : 575 Max. :2315.0000 Max. :4400.000
## (Other) :380536
## F MAG FATALITIES INJURIES
## Min. :0.0 Min. : 0.0 Min. : 0.0000 Min. : 0.0000
## 1st Qu.:0.0 1st Qu.: 0.0 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median :1.0 Median : 50.0 Median : 0.0000 Median : 0.0000
## Mean :0.9 Mean : 46.9 Mean : 0.0168 Mean : 0.1557
## 3rd Qu.:1.0 3rd Qu.: 75.0 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :5.0 Max. :22000.0 Max. :583.0000 Max. :1700.0000
## NA's :843563
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Min. : 0.00 :465934 Min. : 0.000 :618413
## 1st Qu.: 0.00 K :424665 1st Qu.: 0.000 K :281832
## Median : 0.00 M : 11330 Median : 0.000 M : 1994
## Mean : 12.06 0 : 216 Mean : 1.527 k : 21
## 3rd Qu.: 0.50 B : 40 3rd Qu.: 0.000 0 : 19
## Max. :5000.00 5 : 28 Max. :990.000 B : 9
## (Other): 84 (Other): 9
## WFO STATEOFFIC
## :142069 :248769
## OUN : 17393 TEXAS, North : 12193
## JAN : 13889 ARKANSAS, Central and North Central: 11738
## LWX : 13174 IOWA, Central : 11345
## PHI : 12551 KANSAS, Southwest : 11212
## TSA : 12483 GEORGIA, North and Central : 11120
## (Other):690738 (Other) :595920
## ZONENAMES
## :594029
## :205988
## GREATER RENO / CARSON CITY / M - GREATER RENO / CARSON CITY / M : 639
## GREATER LAKE TAHOE AREA - GREATER LAKE TAHOE AREA : 592
## JEFFERSON - JEFFERSON : 303
## MADISON - MADISON : 302
## (Other) :100444
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_
## Min. : 0 Min. :-14451 Min. : 0 Min. :-14455
## 1st Qu.:2802 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0
## Median :3540 Median : 8707 Median : 0 Median : 0
## Mean :2875 Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.:4019 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. :9706 Max. : 17124 Max. :9706 Max. :106220
## NA's :47 NA's :40
## REMARKS REFNUM
## :287433 Min. : 1
## : 24013 1st Qu.:225575
## Trees down.\n : 1110 Median :451149
## Several trees were blown down.\n : 569 Mean :451149
## Trees were downed.\n : 446 3rd Qu.:676723
## Large trees and power lines were blown down.\n: 432 Max. :902297
## (Other) :588294Impact on 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.
Health_FATALITIES <- storm %>%
select(EVTYPE, FATALITIES) %>%
group_by(EVTYPE) %>%
summarise(Total_Health_FATALITIS = sum(FATALITIES)) %>%
arrange(- Total_Health_FATALITIS)
head(Health_FATALITIES, 10)## # A tibble: 10 x 2
## EVTYPE Total_Health_FATALITIS
## <fct> <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 224Health_INJURIES <- storm %>%
select(EVTYPE, INJURIES) %>%
group_by(EVTYPE) %>%
summarise(Total_Health_INJURIES = sum(INJURIES)) %>%
arrange(- Total_Health_INJURIES)
head(Health_INJURIES, 10)## # A tibble: 10 x 2
## EVTYPE Total_Health_INJURIES
## <fct> <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 $USD is indicated by PROPDMGEXP and CROPDMGEXP parameters. According to this link, the index in the PROPDMGEXP and CROPDMGEXP can be interpreted as the following:-
- H, h -> hundreds = x100
- K, K -> kilos = x1,000
- M, m -> millions = x1,000,000
- B,b -> billions = x1,000,000,000
- (+) -> x1
- (-) -> x0
- (?) -> x0
- blank -> x0
The total damage caused by each event type is calculated with the following code.
damage <- storm %>%
select(EVTYPE, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP)
icon <- sort(unique(as.character(damage$PROPDMGEXP)))
icon_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)
multiplier <- data.frame(icon, icon_multiplier)
damage$Prop.Multiplier <- multiplier$icon_multiplier[match(damage$PROPDMGEXP, multiplier$icon)]
damage$Crop.Multiplier <- multiplier$icon_multiplier[match(damage$CROPDMGEXP, multiplier$icon)]
damage <- damage %>%
mutate(Prop_DAMAGE = PROPDMG*Prop.Multiplier) %>%
mutate(Crop_DAMAGE = CROPDMG*Crop.Multiplier) %>%
mutate(TOTAL_DAMAGE = Prop_DAMAGE + Crop_DAMAGE)
damage_total <- damage %>%
group_by(EVTYPE) %>%
summarize(TOTAL_DAMAGE_EVTYPE = sum(TOTAL_DAMAGE)) %>%
arrange(-TOTAL_DAMAGE_EVTYPE)
head(damage_total,10)## # A tibble: 10 x 2
## EVTYPE TOTAL_DAMAGE_EVTYPE
## <fct> <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
Impact on Health
The top 10 events with the highest total fatalities and injuries are shown graphically.
ggplot(Health_FATALITIES[1:10,],
aes(x = reorder(EVTYPE, -Total_Health_FATALITIS), y = Total_Health_FATALITIS)) +
geom_bar(stat = "identity", aes(fill = interaction(Total_Health_FATALITIS, EVTYPE))) +
theme(axis.text.x = element_text(angle=90, vjust=0.5, hjust=1), legend.position = 'none') +
ggtitle("Top 10 Events with Highest Total Fatalities") +
labs(x="EVENT TYPE", y="Total Fatalities")
ggplot(Health_INJURIES[1:10,],
aes(x = reorder(EVTYPE, -Total_Health_INJURIES), y = Total_Health_INJURIES)) +
geom_bar(stat = "identity", aes(fill = interaction(Total_Health_INJURIES, EVTYPE))) +
theme(axis.text.x = element_text(angle=90, vjust=0.5, hjust=1), legend.position = 'none') +
ggtitle("Top 10 Events with Highest Total Injuries") +
labs(x="EVENT TYPE", y="Total Injuries")
Both figures shows that tornado causes the hightest damage on health. ### Impact on Economy
The top 10 events with the highest total economic damages (property and crop combined) are shown graphically.
ggplot(damage_total[1:10,],
aes(x = reorder(EVTYPE, -TOTAL_DAMAGE_EVTYPE), y = TOTAL_DAMAGE_EVTYPE)) +
geom_bar(stat="identity", aes(fill = interaction(TOTAL_DAMAGE_EVTYPE, EVTYPE))) +
theme(axis.text.x = element_text(angle=90, vjust=0.5, hjust=1), legend.position = 'none') +
ggtitle("Top 10 Events with Highest Economic Impact") +
labs(x="EVENT TYPE", y="Total Economic Impact ($USD)")
Above figure shows that flood has the highest economic impact.
Summary
From these data and graphs, we found that Tornado are most harmful with respect to population health, while Flood have the greatest economic consequences.