Analysis of population health and economic damage due to Weather events in the US from 1951 to 2011
Avijit
12th August 2020
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. 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 report tries to answer the following two questions:
Q1. Across the United States, which types of events are most harmful with respect to population health?
Q2. Across the United States, which types of events have the greatest economic consequences?
Importing libraries
library(dplyr)
library(knitr)
library(data.table)
setwd("D:/R working directory")Processing Data
Downloading Data
From the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database we obtain Storm Data in the form of a comma-separated-value file compressed via the bzip2 algorithm.
if(!file.exists("./StormData.csv.bz2"))
{
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2",
destfile = "./StormData.csv.bz2")
}Reading Data
The events in the database start in the year 1950 and end in November 2011.
f <- fread(
"./StormData.csv.bz2",
sep = ",",
header = T
)
dim(f)## [1] 902297 37head(f)## 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
## 3: 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL
## 4: 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL
## 5: 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL
## 6: 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END COUNTYENDN
## 1: TORNADO 0 0 NA
## 2: TORNADO 0 0 NA
## 3: TORNADO 0 0 NA
## 4: TORNADO 0 0 NA
## 5: TORNADO 0 0 NA
## 6: TORNADO 0 0 NA
## END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES INJURIES PROPDMG
## 1: 0 14.0 100 3 0 0 15 25.0
## 2: 0 2.0 150 2 0 0 0 2.5
## 3: 0 0.1 123 2 0 0 2 25.0
## 4: 0 0.0 100 2 0 0 2 2.5
## 5: 0 0.0 150 2 0 0 2 2.5
## 6: 0 1.5 177 2 0 0 6 2.5
## PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES LATITUDE LONGITUDE
## 1: K 0 3040 8812
## 2: K 0 3042 8755
## 3: K 0 3340 8742
## 4: K 0 3458 8626
## 5: K 0 3412 8642
## 6: K 0 3450 8748
## LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1: 3051 8806 1
## 2: 0 0 2
## 3: 0 0 3
## 4: 0 0 4
## 5: 0 0 5
## 6: 0 0 6Extracting Required Data
The extracted dataset includes:
- Event Types (EVTYPE)
- Fatalities
- Injuries
- Property Damage (PROPDMG)
- Property Damage Expense by symbol (PROPDAMAGE)
- Crop Damage (CROPDMG)
- Crop Damage Expense by symbol (CROPDAMAGE)
f <- f[,c(8,23:28)]
head(f)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1: TORNADO 0 15 25.0 K 0
## 2: TORNADO 0 0 2.5 K 0
## 3: TORNADO 0 2 25.0 K 0
## 4: TORNADO 0 2 2.5 K 0
## 5: TORNADO 0 2 2.5 K 0
## 6: TORNADO 0 6 2.5 K 0PROPDMGEXP and CROPDMGEXP columns identify the magnitude that the damage should be multiplied against to accurately assess damage amount. For example, K indicates multiplying by 103, M by 106, B by 109 etc.
Finding Property Damage
table(f$PROPDMGEXP)##
## - ? + 0 1 2 3 4 5 6
## 465934 1 8 5 216 25 13 4 4 28 4
## 7 8 B h H K m M
## 5 1 40 1 6 424665 7 11330f <- f %>%
mutate(
PROPDMG = PROPDMG*case_when(
PROPDMGEXP == "B"|PROPDMGEXP == "b" ~ 10^9,
PROPDMGEXP == "M"|PROPDMGEXP == "m" ~ 10^6,
PROPDMGEXP == "K"|PROPDMGEXP == "k" ~ 10^3,
PROPDMGEXP == "H"|PROPDMGEXP == "h" ~ 10^2,
PROPDMGEXP == 1 ~ 1,
PROPDMGEXP == 2 ~ 2,
PROPDMGEXP == 3 ~ 3,
PROPDMGEXP == 4 ~ 4,
PROPDMGEXP == 5 ~ 5,
PROPDMGEXP == 6 ~ 6,
PROPDMGEXP == 7 ~ 7,
PROPDMGEXP == 8 ~ 8,
T ~ 0
),
PROPDMGEXP = NULL
)
head(f)## EVTYPE FATALITIES INJURIES PROPDMG CROPDMG CROPDMGEXP
## 1: TORNADO 0 15 25000 0
## 2: TORNADO 0 0 2500 0
## 3: TORNADO 0 2 25000 0
## 4: TORNADO 0 2 2500 0
## 5: TORNADO 0 2 2500 0
## 6: TORNADO 0 6 2500 0Finding Crop Damage
table(f$CROPDMGEXP)##
## ? 0 2 B k K m M
## 618413 7 19 1 9 21 281832 1 1994f <- f %>%
mutate(
CROPDMG = CROPDMG*case_when(
CROPDMGEXP == "B"|CROPDMGEXP == "b" ~ 10^9,
CROPDMGEXP == "M"|CROPDMGEXP == "m" ~ 10^6,
CROPDMGEXP == "K"|CROPDMGEXP == "k" ~ 10^3,
CROPDMGEXP == 2 ~ 2,
T ~ 0
),
CROPDMGEXP = NULL
)
head(f)## EVTYPE FATALITIES INJURIES PROPDMG CROPDMG
## 1: TORNADO 0 15 25000 0
## 2: TORNADO 0 0 2500 0
## 3: TORNADO 0 2 25000 0
## 4: TORNADO 0 2 2500 0
## 5: TORNADO 0 2 2500 0
## 6: TORNADO 0 6 2500 0Analysing Data
Q1. Across the United States, which types of events are most harmful with respect to population health?
Finding Aggregate Fatalities per event type
total_fatalities <- aggregate(FATALITIES~EVTYPE,f, sum) %>%
arrange(desc(FATALITIES))
total_fatalities <- total_fatalities[1:10,]
total_fatalities## EVTYPE FATALITIES
## 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 224Finding Aggregate Injuries per event type
total_injuries <- aggregate(INJURIES~EVTYPE,f, sum) %>%
arrange(desc(INJURIES))
total_injuries <- total_injuries[1:10,]
total_injuries## EVTYPE INJURIES
## 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 1361Plotting events with highest fatalities and injuries
par(
mfrow = c(1, 2),
mar = c(12, 4, 3, 2),
cex = 0.8
)
barplot(
total_fatalities$FATALITIES,
names.arg = total_fatalities$EVTYPE,
las = 3,
main = "Events with Highest Fatalities",
ylab = "Number of fatalities",
col = "red"
)
barplot(
total_injuries$INJURIES,
names.arg = total_injuries$EVTYPE,
las = 3,
main = "Events with Highest Injuries",
ylab = "Number of injuries",
col = "red"
)
Q2. Across the United States, which types of events have the greatest economic consequences?
Finding Aggregate Property Damage per event type
total_propdmg <- aggregate(PROPDMG~EVTYPE,f, sum) %>%
arrange(desc(PROPDMG))
total_propdmg <- total_propdmg[1:10,]
total_propdmg## EVTYPE PROPDMG
## 1 FLOOD 144657709800
## 2 HURRICANE/TYPHOON 69305840000
## 3 TORNADO 56937160991
## 4 STORM SURGE 43323536000
## 5 FLASH FLOOD 16140812087
## 6 HAIL 15732267370
## 7 HURRICANE 11868319010
## 8 TROPICAL STORM 7703890550
## 9 WINTER STORM 6688497250
## 10 HIGH WIND 5270046260Finding Aggregate Crop Damage per event type
total_cropdmg <- aggregate(CROPDMG~EVTYPE,f, sum) %>%
arrange(desc(CROPDMG))
total_cropdmg <- total_cropdmg[1:10,]
total_cropdmg## EVTYPE CROPDMG
## 1 DROUGHT 13972566000
## 2 FLOOD 5661968450
## 3 RIVER FLOOD 5029459000
## 4 ICE STORM 5022113500
## 5 HAIL 3025954450
## 6 HURRICANE 2741910000
## 7 HURRICANE/TYPHOON 2607872800
## 8 FLASH FLOOD 1421317100
## 9 EXTREME COLD 1292973000
## 10 FROST/FREEZE 1094086000Plotting events with highest property and crop damage
par(
mfrow = c(1, 2),
mar = c(12, 4, 3, 2),
cex = 0.8
)
barplot(
total_propdmg$PROPDMG/10^9,
names.arg = total_propdmg$EVTYPE,
las = 3,
main = "Events with Highest Property Damage",
ylab = "Damage Cost(in billion $)",
col = "green"
)
barplot(
total_cropdmg$CROPDMG/10^9,
names.arg = total_cropdmg$EVTYPE,
las = 3,
main = "Events with Highest Crop Damage",
ylab = "Damage Cost(in billion $)",
col = "green"
)
Finding total economic damage per event type
economic_damage <- aggregate(PROPDMG+CROPDMG~EVTYPE, f, sum)
names(economic_damage) <- c("EVTYPE","Total_Damage")
economic_damage <- arrange(economic_damage,desc(Total_Damage))
economic_damage <- economic_damage[1:15,]
economic_damage## EVTYPE Total_Damage
## 1 FLOOD 150319678250
## 2 HURRICANE/TYPHOON 71913712800
## 3 TORNADO 57352114101
## 4 STORM SURGE 43323541000
## 5 HAIL 18758221820
## 6 FLASH FLOOD 17562129187
## 7 DROUGHT 15018672000
## 8 HURRICANE 14610229010
## 9 RIVER FLOOD 10148404500
## 10 ICE STORM 8967041310
## 11 TROPICAL STORM 8382236550
## 12 WINTER STORM 6715441250
## 13 HIGH WIND 5908617560
## 14 WILDFIRE 5060586800
## 15 TSTM WIND 5038935790Plotting events with highest total economic damage
par(
mfrow = c(1, 1),
mar = c(12, 4, 3, 2),
cex = 0.8
)
barplot(
economic_damage$Total_Damage/10^9,
names.arg = economic_damage$EVTYPE,
las = 3,
main = "Events with Highest Crop Damage",
ylab = "Damage Cost(in billion $)",
col = "yellow"
)
Results
1. Across the United States, which types of events are most harmful with respect to population health?
Tornados caused the maximum number of fatalities and injuries. It was followed by Excessive Heat for fatalities and Thunderstorm wind for injuries.
2. Across the United States, which types of events have the greatest economic consequences?
Floods caused the maximum property damage where as Drought caused the maximum crop damage. Second major events that caused the maximum damage was Hurricanes/Typhoon for property damage and Floods for crop damage.
Ovreall maximum total economic damage is caused by Floods, followed by Hurricanes/Typhoon.