In this report we aim to address the most harmful severe weather events that affected the United States' population health and property. Severe weather events were analyzed from 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 events in the database start in the year 1950 and end in November 2011. I analyzed the data to determine which severe weather events are most harmful to the US population along 1) fatalities and 2) injuries (giving priority to fatalities). Next, I provide the total amount of property and crop damage for the severe weather events, reported in US dollars.
temp <- tempfile()
url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(url, temp, method = "curl")
file <- bzfile(temp)
stormData <- read.csv(file)
unlink(temp)
head(stormData)
## 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 6
tail(stormData)
## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY
## 902292 47 11/28/2011 0:00:00 03:00:00 PM CST 21
## 902293 56 11/30/2011 0:00:00 10:30:00 PM MST 7
## 902294 30 11/10/2011 0:00:00 02:48:00 PM MST 9
## 902295 2 11/8/2011 0:00:00 02:58:00 PM AKS 213
## 902296 2 11/9/2011 0:00:00 10:21:00 AM AKS 202
## 902297 1 11/28/2011 0:00:00 08:00:00 PM CST 6
## COUNTYNAME STATE EVTYPE BGN_RANGE
## 902292 TNZ001>004 - 019>021 - 048>055 - 088 TN WINTER WEATHER 0
## 902293 WYZ007 - 017 WY HIGH WIND 0
## 902294 MTZ009 - 010 MT HIGH WIND 0
## 902295 AKZ213 AK HIGH WIND 0
## 902296 AKZ202 AK BLIZZARD 0
## 902297 ALZ006 AL HEAVY SNOW 0
## BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 902292 11/29/2011 0:00:00 12:00:00 PM 0
## 902293 11/30/2011 0:00:00 10:30:00 PM 0
## 902294 11/10/2011 0:00:00 02:48:00 PM 0
## 902295 11/9/2011 0:00:00 01:15:00 PM 0
## 902296 11/9/2011 0:00:00 05:00:00 PM 0
## 902297 11/29/2011 0:00:00 04:00:00 AM 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG
## 902292 NA 0 0 0 NA 0
## 902293 NA 0 0 0 NA 66
## 902294 NA 0 0 0 NA 52
## 902295 NA 0 0 0 NA 81
## 902296 NA 0 0 0 NA 0
## 902297 NA 0 0 0 NA 0
## FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO
## 902292 0 0 0 K 0 K MEG
## 902293 0 0 0 K 0 K RIW
## 902294 0 0 0 K 0 K TFX
## 902295 0 0 0 K 0 K AFG
## 902296 0 0 0 K 0 K AFG
## 902297 0 0 0 K 0 K HUN
## STATEOFFIC
## 902292 TENNESSEE, West
## 902293 WYOMING, Central and West
## 902294 MONTANA, Central
## 902295 ALASKA, Northern
## 902296 ALASKA, Northern
## 902297 ALABAMA, North
## ZONENAMES
## 902292 LAKE - LAKE - OBION - WEAKLEY - HENRY - DYER - GIBSON - CARROLL - LAUDERDALE - TIPTON - HAYWOOD - CROCKETT - MADISON - CHESTER - HENDERSON - DECATUR - SHELBY
## 902293 OWL CREEK & BRIDGER MOUNTAINS - OWL CREEK & BRIDGER MOUNTAINS - WIND RIVER BASIN
## 902294 NORTH ROCKY MOUNTAIN FRONT - NORTH ROCKY MOUNTAIN FRONT - EASTERN GLACIER
## 902295 ST LAWRENCE IS. BERING STRAIT - ST LAWRENCE IS. BERING STRAIT
## 902296 NORTHERN ARCTIC COAST - NORTHERN ARCTIC COAST
## 902297 MADISON - MADISON
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_
## 902292 0 0 0 0
## 902293 0 0 0 0
## 902294 0 0 0 0
## 902295 0 0 0 0
## 902296 0 0 0 0
## 902297 0 0 0 0
## REMARKS
## 902292 EPISODE NARRATIVE: A powerful upper level low pressure system brought snow to portions of Northeast Arkansas, the Missouri Bootheel, West Tennessee and extreme north Mississippi. Most areas picked up between 1 and 3 inches of with areas of Northeast Arkansas and the Missouri Bootheel receiving between 4 and 6 inches of snow.EVENT NARRATIVE: Around 1 inch of snow fell in Carroll County.
## 902293 EPISODE NARRATIVE: A strong cold front moved south through north central Wyoming bringing high wind to the Meeteetse area and along the south slopes of the western Owl Creek Range. Wind gusts to 76 mph were recorded at Madden Reservoir.EVENT NARRATIVE:
## 902294 EPISODE NARRATIVE: A strong westerly flow aloft produced gusty winds at the surface along the Rocky Mountain front and over the plains of Central Montana. Wind gusts in excess of 60 mph were reported.EVENT NARRATIVE: A wind gust to 60 mph was reported at East Glacier Park 1ENE (the Two Medicine DOT site).
## 902295 EPISODE NARRATIVE: A 960 mb low over the southern Aleutians at 0300AKST on the 8th intensified to 945 mb near the Gulf of Anadyr by 2100AKST on the 8th. The low crossed the Chukotsk Peninsula as a 956 mb low at 0900AKST on the 9th, and moved into the southern Chukchi Sea as a 958 mb low by 2100AKST on the 9th. The low then tracked to the northwest and weakened to 975 mb about 150 miles north of Wrangel Island by 1500AKST on the 10th. The storm was one of the strongest storms to impact the west coast of Alaska since November 1974. \n\nZone 201: Blizzard conditions were observed at Wainwright from approximately 1153AKST through 1611AKST on the 9th. The visibility was frequently reduced to one quarter mile in snow and blowing snow. There was a peak wind gust to 43kt (50 mph) at the Wainwright ASOS. During this event, there was also a peak wind gust to \n68 kt (78 mph) at the Cape Lisburne AWOS. \n\nZone 202: Blizzard conditions were observed at Barrow from approximately 1021AKST through 1700AKST on the 9th. The visibility was frequently reduced to one quarter mile or less in blowing snow. There was a peak wind gust to 46 kt (53 mph) at the Barrow ASOS. \n\nZone 207: Blizzard conditions were observed at Kivalina from approximately 0400AKST through 1230AKST on the 9th. The visibility was frequently reduced to one quarter of a mile in snow and blowing snow. There was a peak wind gust to 61 kt (70 mph) at the Kivalina ASOS. The doors to the village transportation shed were blown out to sea. Many homes lost portions of their tin roofing, and satellite dishes were ripped off of roofs. One home had its door blown off. At Point Hope, severe blizzard conditions were observed. There was a peak wind gust of 68 kt (78 mph) at the Point Hope AWOS before power was lost to the AWOS. It was estimated that the wind gusted as high as 85 mph in the village during the height of the storm during the morning and early afternoon hours on the 9th. Five power poles were knocked down in the storm EVENT NARRATIVE:
## 902296 EPISODE NARRATIVE: A 960 mb low over the southern Aleutians at 0300AKST on the 8th intensified to 945 mb near the Gulf of Anadyr by 2100AKST on the 8th. The low crossed the Chukotsk Peninsula as a 956 mb low at 0900AKST on the 9th, and moved into the southern Chukchi Sea as a 958 mb low by 2100AKST on the 9th. The low then tracked to the northwest and weakened to 975 mb about 150 miles north of Wrangel Island by 1500AKST on the 10th. The storm was one of the strongest storms to impact the west coast of Alaska since November 1974. \n\nZone 201: Blizzard conditions were observed at Wainwright from approximately 1153AKST through 1611AKST on the 9th. The visibility was frequently reduced to one quarter mile in snow and blowing snow. There was a peak wind gust to 43kt (50 mph) at the Wainwright ASOS. During this event, there was also a peak wind gust to \n68 kt (78 mph) at the Cape Lisburne AWOS. \n\nZone 202: Blizzard conditions were observed at Barrow from approximately 1021AKST through 1700AKST on the 9th. The visibility was frequently reduced to one quarter mile or less in blowing snow. There was a peak wind gust to 46 kt (53 mph) at the Barrow ASOS. \n\nZone 207: Blizzard conditions were observed at Kivalina from approximately 0400AKST through 1230AKST on the 9th. The visibility was frequently reduced to one quarter of a mile in snow and blowing snow. There was a peak wind gust to 61 kt (70 mph) at the Kivalina ASOS. The doors to the village transportation shed were blown out to sea. Many homes lost portions of their tin roofing, and satellite dishes were ripped off of roofs. One home had its door blown off. At Point Hope, severe blizzard conditions were observed. There was a peak wind gust of 68 kt (78 mph) at the Point Hope AWOS before power was lost to the AWOS. It was estimated that the wind gusted as high as 85 mph in the village during the height of the storm during the morning and early afternoon hours on the 9th. Five power poles were knocked down in the storm EVENT NARRATIVE:
## 902297 EPISODE NARRATIVE: An intense upper level low developed on the 28th at the base of a highly amplified upper trough across the Great Lakes and Mississippi Valley. The upper low closed off over the mid South and tracked northeast across the Tennessee Valley during the morning of the 29th. A warm conveyor belt of heavy rainfall developed in advance of the low which dumped from around 2 to over 5 inches of rain across the eastern two thirds of north Alabama and middle Tennessee. The highest rain amounts were recorded in Jackson and DeKalb Counties with 3 to 5 inches. The rain fell over 24 to 36 hour period, with rainfall remaining light to moderate during most its duration. The rainfall resulted in minor river flooding along the Little River, Big Wills Creek and Paint Rock. A landslide occurred on Highway 35 just north of Section in Jackson County. A driver was trapped in his vehicle, but was rescued unharmed. Trees, boulders and debris blocked 100 to 250 yards of Highway 35.\n\nThe rain mixed with and changed to snow across north Alabama during the afternoon and evening hours of the 28th, and lasted into the 29th. The heaviest bursts of snow occurred in northwest Alabama during the afternoon and evening hours, and in north central and northeast Alabama during the overnight and morning hours. Since ground temperatures were in the 50s, and air temperatures in valley areas only dropped into the mid 30s, most of the snowfall melted on impact with mostly trace amounts reported in valley locations. However, above 1500 foot elevation, snow accumulations of 1 to 2 inches were reported. The heaviest amount was 2.3 inches on Monte Sano Mountain, about 5 miles northeast of Huntsville.EVENT NARRATIVE: Snowfall accumulations of up to 2.3 inches were reported on the higher elevations of eastern Madison County. A snow accumulation of 1.5 inches was reported 2.7 miles south of Gurley, while 2.3 inches was reported 3 miles east of Huntsville atop Monte Sano Mountain.
## REFNUM
## 902292 902292
## 902293 902293
## 902294 902294
## 902295 902295
## 902296 902296
## 902297 902297
dateDownloaded <- date()
The data set was downloaded from rurl.
The data set was downloaded on rdataDownloaded.
library(ggplot2) # for plotting
library(dplyr) # for data frame manipulation
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
theme_set(theme_minimal(16)) # set styling for plots
obs <- dim(stormData)[1]
uniqEvents <- unique(stormData$EVTYPE)
numEvents <- length(uniqEvents)
There are rnumEvents unique severe weather event types in the data. (according to string labels, this number is actually less)
There are robs severe weather reported events.
# Determine which event types have the most fatalities and injuries.
popHealth <- stormData %.% group_by(EVTYPE) %.% summarize(fatalities = sum(FATALITIES),
injuries = sum(INJURIES))
# On inspection of the data, there are many subcategories with low counts
# like 0 or less than 50.
LIMIT <- 100
dim(popHealth[popHealth$fatalities <= LIMIT, ])[1]
## [1] 965
dim(popHealth[popHealth$injuries <= LIMIT, ])[1]
## [1] 948
# Set a minimum of 100 deaths and 100 injuries for events that are most
# harmful to population health.
subPopHealth <- subset(popHealth, fatalities > 100 & injuries > 100)
subPopHealth <- subPopHealth[order(subPopHealth$fatalities, decreasing = T),
]
subPopHealth
## Source: local data frame [19 x 3]
##
## EVTYPE fatalities injuries
## 834 TORNADO 5633 91346
## 130 EXCESSIVE HEAT 1903 6525
## 153 FLASH FLOOD 978 1777
## 275 HEAT 937 2100
## 464 LIGHTNING 816 5230
## 856 TSTM WIND 504 6957
## 170 FLOOD 470 6789
## 585 RIP CURRENT 368 232
## 359 HIGH WIND 248 1137
## 19 AVALANCHE 224 170
## 972 WINTER STORM 206 1321
## 586 RIP CURRENTS 204 297
## 278 HEAT WAVE 172 309
## 140 EXTREME COLD 160 231
## 760 THUNDERSTORM WIND 133 1488
## 310 HEAVY SNOW 127 1021
## 676 STRONG WIND 103 280
## 30 BLIZZARD 101 805
## 350 HIGH SURF 101 152
The top 2 harmful events with respect to population health are tornados and excessive heat. Note that some categories are repeated such as TSTM WIND and THUNDERSTORM WIND. This analysis does not combine such similarly labeled events since their totals would not come close to the death tolls and injuries of tornados and excessive heat.
These findings suggest that resources should be dedicated to early detection systems of tornados, tornado alerts and preparedness, and heat advisories.
Here are the top 10 states with the most number of reported event types for “TORNADO” and “EXCESSIVE HEAT”.
tornadoStates <- sort(table(stormData[stormData$EVTYPE == "TORNADO", ]$STATE),
decreasing = T)
head(tornadoStates, 10)
##
## TX KS OK FL NE IA IL MO MS AL
## 8288 3972 3709 3089 2693 2464 2351 2281 2166 2101
heatStates <- sort(table(stormData[stormData$EVTYPE == "EXCESSIVE HEAT", ]$STATE),
decreasing = T)
head(heatStates, 10)
##
## AL MO TX IL CA PA OK AR MD NJ
## 207 136 121 116 88 76 75 72 65 60
Inspect the levels of the multipliers for property damage and crop damage.
levels(stormData$PROPDMGEXP)
## [1] "" "-" "?" "+" "0" "1" "2" "3" "4" "5" "6" "7" "8" "B" "h" "H" "K"
## [18] "m" "M"
levels(stormData$CROPDMGEXP)
## [1] "" "?" "0" "2" "B" "k" "K" "m" "M"
The storm data documentation identifies H, K, M, and B for damage units. Some data entries appear incorrect such as “?”, and “-” among other numerical entries. This analysis will only include reported observations with anticipated identifiers either upper or lower case to analyze property/crop damage.
stormData$propDmgX <- NA
stormData$cropDmgX <- NA
# Calculate property damage in dollars
stormData[stormData$PROPDMGEXP == "h" | stormData$PROPDMGEXP == "H", ]$propDmgX <- 100
stormData[stormData$PROPDMGEXP == "k" | stormData$PROPDMGEXP == "K", ]$propDmgX <- 1000
stormData[stormData$PROPDMGEXP == "m" | stormData$PROPDMGEXP == "M", ]$propDmgX <- 1e+06
stormData[stormData$PROPDMGEXP == "b" | stormData$PROPDMGEXP == "B", ]$propDmgX <- 1e+09
# Calculate crop damage in dollars
stormData[stormData$CROPDMGEXP == "k" | stormData$CROPDMGEXP == "K", ]$cropDmgX <- 1000
stormData[stormData$CROPDMGEXP == "m" | stormData$CROPDMGEXP == "M", ]$cropDmgX <- 1e+06
stormData[stormData$CROPDMGEXP == "b" | stormData$CROPDMGEXP == "B", ]$cropDmgX <- 1e+09
stormData$adjPropDmg <- stormData$PROPDMG * stormData$propDmgX
stormData$adjCropDmg <- stormData$CROPDMG * stormData$cropDmgX
Subset the stormData for observations with the correct identifiers for PROPDMGEXP and CROPDMGEXP.
table(stormData$propDmgX, useNA = "ifany")
##
## 100 1000 1e+06 1e+09 <NA>
## 7 424665 11337 40 466248
table(stormData$cropDmgX, useNA = "ifany")
##
## 1000 1e+06 1e+09 <NA>
## 281853 1995 9 618440
stormDamage <- subset(stormData, !is.na(propDmgX) | !is.na(cropDmgX))
Create aggregated data for property and crop damage by event type.
econDamage <- stormDamage %.% group_by(EVTYPE) %.% summarize(totalPropDmg = sum(adjPropDmg),
totalCropDmg = sum(adjCropDmg))
head(econDamage)
## Source: local data frame [6 x 3]
##
## EVTYPE totalPropDmg totalCropDmg
## 1 HIGH SURF ADVISORY 200000 NA
## 2 FLASH FLOOD 50000 NA
## 3 TSTM WIND 8100000 NA
## 4 TSTM WIND (G45) 8000 NA
## 5 ? 5000 NA
## 6 AGRICULTURAL FREEZE NA 28820000
Order the data frame by property damage and crop damage
propDamage <- econDamage[order(econDamage$totalPropDmg, decreasing = T), ]
head(propDamage)
## Source: local data frame [6 x 3]
##
## EVTYPE totalPropDmg totalCropDmg
## 193 HURRICANE/TYPHOON 6.931e+10 NA
## 297 STORM SURGE 4.332e+10 NA
## 298 STORM SURGE/TIDE 4.641e+09 NA
## 190 HURRICANE OPAL 3.173e+09 NA
## 139 HEAVY RAIN/SEVERE WEATHER 2.500e+09 NA
## 358 TORNADOES, TSTM WIND, HAIL 1.600e+09 2500000
cropDamage <- econDamage[order(econDamage$totalCropDmg, decreasing = T), ]
head(cropDamage)
## Source: local data frame [6 x 3]
##
## EVTYPE totalPropDmg totalCropDmg
## 95 FROST/FREEZE NA 1.094e+09
## 50 EXCESSIVE WETNESS NA 1.420e+08
## 77 FLOOD/RAIN/WINDS NA 1.128e+08
## 29 COLD AND WET CONDITIONS NA 6.600e+07
## 46 Early Frost NA 4.200e+07
## 33 Damaging Freeze NA 3.413e+07
# Create Dot Plots for 19 most harmful events in terms of fatalities
ggplot(data = subPopHealth, aes(x = reorder(EVTYPE, fatalities), y = fatalities)) +
geom_point() + coord_flip() + labs(title = "Fatalities by Event", x = "",
y = "Fatalities")
propDamage <- propDamage[1:5, ]
ggplot(aes(x = reorder(EVTYPE, totalPropDmg), y = totalPropDmg), data = propDamage) +
geom_bar(fill = "#9ecae1") + coord_flip() + labs(y = "Total Property Damage ($)",
x = "Event", title = "Top 5 Events for Property Damage")
## Mapping a variable to y and also using stat="bin".
## With stat="bin", it will attempt to set the y value to the count of cases in each group.
## This can result in unexpected behavior and will not be allowed in a future version of ggplot2.
## If you want y to represent counts of cases, use stat="bin" and don't map a variable to y.
## If you want y to represent values in the data, use stat="identity".
## See ?geom_bar for examples. (Deprecated; last used in version 0.9.2)
cropDamage <- cropDamage[1:5, ]
ggplot(aes(x = reorder(EVTYPE, totalCropDmg), y = totalCropDmg), data = cropDamage) +
geom_bar(fill = "#a1d99b") + coord_flip() + labs(y = "Total Crop Damage ($)",
x = "Event", title = "Top 5 Events for Crop Damage")
## Mapping a variable to y and also using stat="bin".
## With stat="bin", it will attempt to set the y value to the count of cases in each group.
## This can result in unexpected behavior and will not be allowed in a future version of ggplot2.
## If you want y to represent counts of cases, use stat="bin" and don't map a variable to y.
## If you want y to represent values in the data, use stat="identity".
## See ?geom_bar for examples. (Deprecated; last used in version 0.9.2)
