Economic and Health Effects of Severe Weather Events in the U.S.
garrym3k
October 25, 2015
Synopsis
The data is from the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. We will analyse the data to assess the impact of severe weather events in the US from 1995 to 2011. It was found that, of all severe weather events, excessive heat and tornadoes have the most impact on public health while floods and drought are responsible for the most property damage.
Data Processing
The following libraries will be needed:
library(ggplot2)## Warning: package 'ggplot2' was built under R version 3.2.2library(plyr)## Warning: package 'plyr' was built under R version 3.2.2Load the data into R and briefly examine it using the head, dim, and summary functions.
storm_data <- read.csv("repdata-data-StormData.csv")
head(storm_data)## 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
## 1 TORNADO 0 0
## 2 TORNADO 0 0
## 3 TORNADO 0 0
## 4 TORNADO 0 0
## 5 TORNADO 0 0
## 6 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14.0 100 3 0 0
## 2 NA 0 2.0 150 2 0 0
## 3 NA 0 0.1 123 2 0 0
## 4 NA 0 0.0 100 2 0 0
## 5 NA 0 0.0 150 2 0 0
## 6 NA 0 1.5 177 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## 3 2 25.0 K 0
## 4 2 2.5 K 0
## 5 2 2.5 K 0
## 6 6 2.5 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2
## 3 3340 8742 0 0 3
## 4 3458 8626 0 0 4
## 5 3412 8642 0 0 5
## 6 3450 8748 0 0 6dim(storm_data)## [1] 902297 37summary(storm_data)## 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) :588294The histogram below shows that data prior to 1995 is relatively incomplete. Thus, we will use onlly the data from 1995 onwards in for the duration of our analysis.
if (dim(storm_data)[2] == 37) {
storm_data$year <- as.numeric(format(as.Date(storm_data$BGN_DATE, format = "%m/%d/%Y %H:%M:%S"), "%Y"))
}
hist(storm_data$year, breaks = 30, main = "Storm Data From 1950 - 2011", xlab = "Year", col = "lightblue")
storm <- storm_data[storm_data$year >= 1995, ]To analyse the impact of severe weather events on public health, we will brake the data into categories for fatalities and injuries.
sortHelper <- function(fieldName, top = 15, dataset = storm_data) {
index <- which(colnames(dataset) == fieldName)
field <- aggregate(dataset[, index], by = list(dataset$EVTYPE), FUN = "sum")
names(field) <- c("EVTYPE", fieldName)
field <- arrange(field, field[, 2], decreasing = T)
field <- head(field, n = top)
field <- within(field, EVTYPE <- factor(x = EVTYPE, levels = field$EVTYPE))
return(field)
}
fatalities <- sortHelper("FATALITIES", dataset = storm)
injuries <- sortHelper("INJURIES", dataset = storm)A multiplier needs to be applied to the data so that the numerical data can be compared to assess economic impacts of severe weather.
convertHelper <- function(dataset = storm, fieldName, newFieldName) {
totalLen <- dim(dataset)[2]
index <- which(colnames(dataset) == fieldName)
dataset[, index] <- as.character(dataset[, index])
logic <- !is.na(toupper(dataset[, index]))
dataset[logic & toupper(dataset[, index]) == "B", index] <- "9"
dataset[logic & toupper(dataset[, index]) == "M", index] <- "6"
dataset[logic & toupper(dataset[, index]) == "K", index] <- "3"
dataset[logic & toupper(dataset[, index]) == "H", index] <- "2"
dataset[logic & toupper(dataset[, index]) == "", index] <- "0"
dataset[, index] <- as.numeric(dataset[, index])
dataset[is.na(dataset[, index]), index] <- 0
dataset <- cbind(dataset, dataset[, index - 1] * 10^dataset[, index])
names(dataset)[totalLen + 1] <- newFieldName
return(dataset)
}
storm <- convertHelper(storm, "PROPDMGEXP", "propertyDamage")## Warning in convertHelper(storm, "PROPDMGEXP", "propertyDamage"): NAs
## introduced by coercionstorm <- convertHelper(storm, "CROPDMGEXP", "cropDamage")## Warning in convertHelper(storm, "CROPDMGEXP", "cropDamage"): NAs introduced
## by coercionoptions(scipen=999)
property <- sortHelper("propertyDamage", dataset = storm)
crop <- sortHelper("cropDamage", dataset = storm)Results
fatalities## EVTYPE FATALITIES
## 1 EXCESSIVE HEAT 1903
## 2 TORNADO 1545
## 3 FLASH FLOOD 934
## 4 HEAT 924
## 5 LIGHTNING 729
## 6 FLOOD 423
## 7 RIP CURRENT 360
## 8 HIGH WIND 241
## 9 TSTM WIND 241
## 10 AVALANCHE 223
## 11 RIP CURRENTS 204
## 12 WINTER STORM 195
## 13 HEAT WAVE 161
## 14 THUNDERSTORM WIND 131
## 15 EXTREME COLD 126injuries## EVTYPE INJURIES
## 1 TORNADO 21765
## 2 FLOOD 6769
## 3 EXCESSIVE HEAT 6525
## 4 LIGHTNING 4631
## 5 TSTM WIND 3630
## 6 HEAT 2030
## 7 FLASH FLOOD 1734
## 8 THUNDERSTORM WIND 1426
## 9 WINTER STORM 1298
## 10 HURRICANE/TYPHOON 1275
## 11 HIGH WIND 1093
## 12 HAIL 916
## 13 WILDFIRE 911
## 14 HEAVY SNOW 751
## 15 FOG 718property## EVTYPE propertyDamage
## 1 FLOOD 144022037057
## 2 HURRICANE/TYPHOON 69305840000
## 3 STORM SURGE 43193536000
## 4 TORNADO 24935939545
## 5 FLASH FLOOD 16047794571
## 6 HAIL 15048722103
## 7 HURRICANE 11812819010
## 8 TROPICAL STORM 7653335550
## 9 HIGH WIND 5259785375
## 10 WILDFIRE 4759064000
## 11 STORM SURGE/TIDE 4641188000
## 12 TSTM WIND 4482361440
## 13 ICE STORM 3643555810
## 14 THUNDERSTORM WIND 3399282992
## 15 HURRICANE OPAL 3172846000crop## EVTYPE cropDamage
## 1 DROUGHT 13922066000
## 2 FLOOD 5422810400
## 3 HURRICANE 2741410000
## 4 HAIL 2614127070
## 5 HURRICANE/TYPHOON 2607872800
## 6 FLASH FLOOD 1343915000
## 7 EXTREME COLD 1292473000
## 8 FROST/FREEZE 1094086000
## 9 HEAVY RAIN 728399800
## 10 TROPICAL STORM 677836000
## 11 HIGH WIND 633561300
## 12 TSTM WIND 553947350
## 13 EXCESSIVE HEAT 492402000
## 14 THUNDERSTORM WIND 414354000
## 15 HEAT 401411500Excessive heat was the number 1 cause of fatalities due to severe weather events, followed by tornadoes.
Tornadoes accounted for the most injuries by a wide margin.
Floods were responsible for more than twice as much property damage than hurricane/typhoons.
Droughts were 2.5 times more damaging to crops in the US than floods .