Storm Data Analysis
Done by chuatt
3 Dec 2017
Rpub Link: Click Here
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.
Data Analysis shows that Tornado is the worst type of event that would cause the most harm with respect to population health. Tornado has also caused the worst economic consequences.
# Download file
setwd('C:/Users/TAT/Desktop/Coursera/Lectures/5) Reproducible Research/')
if(!dir.exists("Assignment 2")) {dir.create("Assignment 2")}
if(!file.exists("StormData.csv.bz2")){
fileURL <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(fileURL, destfile = "./Assignment 2/StormData.csv.bz2", method = "auto")
}
# Read file into R
storm <- read.csv("./Assignment 2/StormData.csv.bz2", header = T, sep = ",")Data Processing
Check summary of data and number of NA for each variable. Variables with >80% NA are removed from the dataset.
# Check summary
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 : 568 Mean :451149
## Trees were downed.\n : 446 3rd Qu.:676723
## Large trees and power lines were blown down.\n: 432 Max. :902297
## (Other) :588295# See number of NA for each variable
sapply(storm, function(x) sum(is.na(x)))## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME
## 0 0 0 0 0 0
## STATE EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE
## 0 0 0 0 0 0
## END_TIME COUNTY_END COUNTYENDN END_RANGE END_AZI END_LOCATI
## 0 0 902297 0 0 0
## LENGTH WIDTH F MAG FATALITIES INJURIES
## 0 0 843563 0 0 0
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC
## 0 0 0 0 0 0
## ZONENAMES LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS
## 0 47 0 40 0 0
## REFNUM
## 0Variables with >80% NA are removed from the dataset. And removed NA rows.
# Remove variables with >80% NA
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionstorm2 <- select(storm, -c(COUNTYENDN, F))
# Remove NA rows
storm2 <- na.omit(storm2)
# Variable class cleaning
library(lubridate)##
## Attaching package: 'lubridate'## The following object is masked from 'package:base':
##
## datestorm2$BGN_DATE <- mdy_hms(storm2$BGN_DATE)
storm2$END_DATE <- mdy_hms(storm2$END_DATE)
storm2$STATE__ <- as.factor(storm2$STATE__)
storm2$COUNTY <- as.factor(storm2$COUNTY)
storm2$REFNUM <- as.factor(storm2$REFNUM)
# Check summary
summary(storm2)## STATE__ BGN_DATE BGN_TIME
## 48 : 83728 Min. :1950-01-03 00:00:00 12:00:00 AM: 10162
## 20 : 53441 1st Qu.:1995-04-19 00:00:00 06:00:00 PM: 7350
## 40 : 46802 Median :2002-03-17 00:00:00 04:00:00 PM: 7261
## 29 : 35648 Mean :1998-12-27 18:58:13 05:00:00 PM: 6891
## 19 : 31069 3rd Qu.:2007-07-27 00:00:00 12:00:00 PM: 6701
## 31 : 30271 Max. :2011-11-30 00:00:00 03:00:00 PM: 6700
## (Other):621291 (Other) :857185
## TIME_ZONE COUNTY COUNTYNAME STATE
## CST :547493 1 : 17810 JEFFERSON : 7840 TX : 83728
## EST :245558 3 : 16218 WASHINGTON: 7603 KS : 53440
## MST : 68390 19 : 14141 JACKSON : 6660 OK : 46802
## PST : 28302 15 : 13858 FRANKLIN : 6256 MO : 35648
## AST : 6360 5 : 13847 LINCOLN : 5937 IA : 31069
## HST : 2563 17 : 13081 MADISON : 5632 NE : 30271
## (Other): 3584 (Other):813295 (Other) :862322 (Other):621292
## EVTYPE BGN_RANGE BGN_AZI
## HAIL :288661 Min. : 0.000 :547332
## TSTM WIND :219940 1st Qu.: 0.000 N : 86737
## THUNDERSTORM WIND: 82563 Median : 0.000 W : 38444
## TORNADO : 60652 Mean : 1.484 S : 37558
## FLASH FLOOD : 54262 3rd Qu.: 1.000 E : 33172
## FLOOD : 25326 Max. :3749.000 NW : 24038
## (Other) :170846 (Other):134969
## BGN_LOCATI END_DATE END_TIME
## :287743 Min. :1986-04-10 00:00:00 :238978
## COUNTYWIDE : 19680 1st Qu.:2000-09-01 00:00:00 06:00:00 PM: 9802
## Countywide : 993 Median :2005-04-30 00:00:00 05:00:00 PM: 8314
## SPRINGFIELD : 843 Mean :2004-09-26 01:24:09 04:00:00 PM: 8101
## SOUTH PORTION: 810 3rd Qu.:2008-08-10 00:00:00 12:00:00 PM: 7481
## NORTH PORTION: 784 Max. :2011-11-30 00:00:00 11:59:00 PM: 7184
## (Other) :591397 NA's :243411 (Other) :622390
## COUNTY_END END_RANGE END_AZI END_LOCATI
## Min. :0 Min. : 0.0000 :724830 :499218
## 1st Qu.:0 1st Qu.: 0.0000 N : 28069 COUNTYWIDE : 19731
## Median :0 Median : 0.0000 S : 22510 SOUTH PORTION : 833
## Mean :0 Mean : 0.9858 W : 20114 NORTH PORTION : 780
## 3rd Qu.:0 3rd Qu.: 0.0000 E : 20041 CENTRAL PORTION: 617
## Max. :0 Max. :925.0000 NE : 14599 SPRINGFIELD : 575
## (Other): 72087 (Other) :380496
## LENGTH WIDTH MAG
## Min. : 0.0000 Min. : 0.000 Min. : 0.0
## 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0
## Median : 0.0000 Median : 0.000 Median : 50.0
## Mean : 0.2301 Mean : 7.503 Mean : 46.9
## 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 75.0
## Max. :2315.0000 Max. :4400.000 Max. :22000.0
##
## FATALITIES INJURIES PROPDMG PROPDMGEXP
## Min. : 0.0000 Min. : 0.0000 Min. : 0.00 :465934
## 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.00 K :424618
## Median : 0.0000 Median : 0.0000 Median : 0.00 M : 11330
## Mean : 0.0168 Mean : 0.1558 Mean : 12.06 0 : 216
## 3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.: 0.50 B : 40
## Max. :583.0000 Max. :1700.0000 Max. :5000.00 5 : 28
## (Other): 84
## CROPDMG CROPDMGEXP WFO
## Min. : 0.000 :618413 :142069
## 1st Qu.: 0.000 K :281785 OUN : 17393
## Median : 0.000 M : 1994 JAN : 13889
## Mean : 1.527 k : 21 LWX : 13174
## 3rd Qu.: 0.000 0 : 19 PHI : 12551
## Max. :990.000 B : 9 TSA : 12483
## (Other): 9 (Other):690691
## STATEOFFIC
## :248769
## TEXAS, North : 12193
## ARKANSAS, Central and North Central: 11738
## IOWA, Central : 11345
## KANSAS, Southwest : 11212
## GEORGIA, North and Central : 11120
## (Other) :595873
## ZONENAMES
## :594029
## :205941
## 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 : 6939 Mean :1452 Mean : 3509
## 3rd Qu.:4019 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. :9706 Max. : 17124 Max. :9706 Max. :106220
##
## REMARKS REFNUM
## :287433 1 : 1
## : 24013 2 : 1
## Trees down.\n : 1110 3 : 1
## Several trees were blown down.\n : 568 4 : 1
## Trees were downed.\n : 446 5 : 1
## Large trees and power lines were blown down.\n: 432 6 : 1
## (Other) :588248 (Other):902244str(storm2)## 'data.frame': 902250 obs. of 35 variables:
## $ STATE__ : Factor w/ 70 levels "1","2","4","5",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : POSIXct, format: "1950-04-18" "1950-04-18" ...
## $ BGN_TIME : Factor w/ 3608 levels "00:00:00 AM",..: 272 287 2705 1683 2584 3186 242 1683 3186 3186 ...
## $ TIME_ZONE : Factor w/ 22 levels "ADT","AKS","AST",..: 7 7 7 7 7 7 7 7 7 7 ...
## $ COUNTY : Factor w/ 557 levels "0","1","2","3",..: 98 4 58 90 44 78 10 124 126 58 ...
## $ COUNTYNAME: Factor w/ 29601 levels "","5NM E OF MACKINAC BRIDGE TO PRESQUE ISLE LT MI",..: 13513 1873 4598 10592 4372 10094 1973 23873 24418 4598 ...
## $ STATE : Factor w/ 72 levels "AK","AL","AM",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ EVTYPE : Factor w/ 985 levels " HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : Factor w/ 35 levels ""," N"," NW",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_LOCATI: Factor w/ 54429 levels "","- 1 N Albion",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_DATE : POSIXct, format: NA NA ...
## $ END_TIME : Factor w/ 3647 levels ""," 0900CST",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ COUNTY_END: num 0 0 0 0 0 0 0 0 0 0 ...
## $ END_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ END_AZI : Factor w/ 24 levels "","E","ENE","ESE",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_LOCATI: Factor w/ 34506 levels "","- .5 NNW",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 ...
## $ 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: Factor w/ 19 levels "","-","?","+",..: 17 17 17 17 17 17 17 17 17 17 ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: Factor w/ 9 levels "","?","0","2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ WFO : Factor w/ 542 levels ""," CI","$AC",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ STATEOFFIC: Factor w/ 250 levels "","ALABAMA, Central",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ ZONENAMES : Factor w/ 25112 levels ""," "| __truncated__,..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 : Factor w/ 436781 levels "","-2 at Deer Park\n",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ REFNUM : Factor w/ 902250 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## - attr(*, "na.action")=Class 'omit' Named int [1:47] 647217 647219 647220 647221 690294 690295 690297 690298 690303 690304 ...
## .. ..- attr(*, "names")= chr [1:47] "647217" "647219" "647220" "647221" ...Results
Question 1: Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
library(ggplot2)
# Plot Number of Fatalities by EVTYPE
table1 <- summarise(group_by(storm2,EVTYPE),Total_death=sum(FATALITIES))
table1 <- arrange(table1,desc(Total_death))[1:10,]
plot1 <- ggplot(table1, aes(x=reorder(EVTYPE,Total_death), y=Total_death, fill=Total_death)) +
geom_bar(stat="identity") +
scale_fill_gradient2(low='red', mid='snow3', high='red', space='Lab') +
labs(title="Top 10 Event by Number of Fatalities", y="Number of Fatalities", x="Event") +
coord_flip()
plot1
# Plot Number of Injuries by EVTYPE
table2 <- summarise(group_by(storm2,EVTYPE),Total_injuries=sum(INJURIES))
table2 <- arrange(table2,desc(Total_injuries))[1:10,]
plot2 <- ggplot(table2,aes(x=reorder(EVTYPE,Total_injuries), y=Total_injuries, fill=Total_injuries)) +
geom_bar(stat="identity") +
scale_fill_gradient2(low='red', mid='snow3', high='purple', space='Lab') +
labs(title="Top 10 Event by Number of Injuries", y="Number of Injuries", x="Event") +
coord_flip()
plot2
# Show both death & injuries tables
library(formattable)## Warning: package 'formattable' was built under R version 3.4.3table1 <- mutate(table1, Percentage=percent(Total_death/sum(Total_death)))
table1## # A tibble: 10 x 3
## EVTYPE Total_death Percentage
## <fctr> <dbl> <S3: formattable>
## 1 TORNADO 5633 46.63%
## 2 EXCESSIVE HEAT 1903 15.75%
## 3 FLASH FLOOD 978 8.10%
## 4 HEAT 937 7.76%
## 5 LIGHTNING 816 6.75%
## 6 TSTM WIND 504 4.17%
## 7 FLOOD 470 3.89%
## 8 RIP CURRENT 368 3.05%
## 9 HIGH WIND 248 2.05%
## 10 AVALANCHE 224 1.85%table2 <- mutate(table2, Percentage=percent(Total_injuries/sum(Total_injuries)))
table2## # A tibble: 10 x 3
## EVTYPE Total_injuries Percentage
## <fctr> <dbl> <S3: formattable>
## 1 TORNADO 91346 72.76%
## 2 TSTM WIND 6957 5.54%
## 3 FLOOD 6789 5.41%
## 4 EXCESSIVE HEAT 6525 5.20%
## 5 LIGHTNING 5230 4.17%
## 6 HEAT 2100 1.67%
## 7 ICE STORM 1975 1.57%
## 8 FLASH FLOOD 1777 1.42%
## 9 THUNDERSTORM WIND 1488 1.19%
## 10 HAIL 1361 1.08%Comment: Based on the data above, we can see Tornado is most harmful with respect to population health. Tornado caused the highest total death (46.63%) and highest injuries (72.76%).
Question 2: Across the United States, which types of events have the greatest economic consequences?
# Plot Number of Property Damage by EVTYPE
table3 <- summarise(group_by(storm2, EVTYPE), Total_PropDmg=sum(PROPDMG))
table3 <- arrange(table3,desc(Total_PropDmg))[1:10,]
plot3 <- ggplot(table3, aes(x=reorder(EVTYPE,Total_PropDmg), y=Total_PropDmg, fill=Total_PropDmg)) +
geom_bar(stat="identity") +
scale_fill_gradient2(low='red', mid='snow3', high='dark green', space='Lab') +
labs(title="Top 10 Event by Number of Property Damage", y="Number of Property Damage", x="Event") +
coord_flip()
plot3
# Show property damage table
table3 <- mutate(table3, Percentage=percent(Total_PropDmg/sum(Total_PropDmg)))
table3## # A tibble: 10 x 3
## EVTYPE Total_PropDmg Percentage
## <fctr> <dbl> <S3: formattable>
## 1 TORNADO 3212258.2 32.31%
## 2 FLASH FLOOD 1419874.6 14.28%
## 3 TSTM WIND 1335965.6 13.44%
## 4 FLOOD 899938.5 9.05%
## 5 THUNDERSTORM WIND 876844.2 8.82%
## 6 HAIL 688693.4 6.93%
## 7 LIGHTNING 603231.8 6.07%
## 8 THUNDERSTORM WINDS 446293.2 4.49%
## 9 HIGH WIND 324731.6 3.27%
## 10 WINTER STORM 132720.6 1.34%Comment: The Top 10 EVTYPE by Number of Property Damage shows that Tornado is the worst type of event which accounted for 32.31% of total Property Damaged.