Storm Data Analysis
Mohamed Chenini
11/26/2017
Synopsis
The study and analysis of storm data is important to both public health and economic problems for communities and municipalities in order to tracks the characteristics of major storms and weather events in the United States. The analysis of storm data by tools such as the R ecosystem will allow to predict when and where these storms will occur, and will try to determine estimates of any fatalities, injuries, and property damage.
set the working directory
setwd("~/DS/datasciencecoursera/reproducible-research/reproducible-research-Week4-Project")Library Loading
library(“R.utils”) library(“ggplot2”) library(“gridExtra”)
Data Loading
get the link to the compressed file and download the “Storm Data” read the “Storm Data” file into a data frame
#url <- "https://d396qusza40orc.cloustormDataront.net/repdata%2Fdata%2FStormData.csv.bz2"
#download.file(url, "StormData.csv.bz2")
#
stormData <- read.csv("StormData.csv.bz2", header = TRUE)library("R.utils")## Loading required package: R.oo## Loading required package: R.methodsS3## R.methodsS3 v1.7.1 (2016-02-15) successfully loaded. See ?R.methodsS3 for help.## R.oo v1.21.0 (2016-10-30) successfully loaded. See ?R.oo for help.##
## Attaching package: 'R.oo'## The following objects are masked from 'package:methods':
##
## getClasses, getMethods## The following objects are masked from 'package:base':
##
## attach, detach, gc, load, save## R.utils v2.6.0 (2017-11-04) successfully loaded. See ?R.utils for help.##
## Attaching package: 'R.utils'## The following object is masked from 'package:utils':
##
## timestamp## The following objects are masked from 'package:base':
##
## cat, commandArgs, getOption, inherits, isOpen, parse, warnings# Extract file if needed and read the storm data
if (!exists("stormData")) {
if (file.exists("repdata_data_StormData.csv.bz2")) {
if (!file.exists("repdata_data_StormData.csv")) {
bunzip2("repdata_data_StormData.csv.bz2", overwrite = F)
}
stormData <- read.csv("repdata_data_StormData.csv", sep = ",")
}
}Data Processing and Analysis
Get to know the data to analyse
data(stormData)## Warning in data(stormData): data set 'stormData' not founddim(stormData)## [1] 902297 37str(stormData)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : Factor w/ 16335 levels "1/1/1966 0:00:00",..: 6523 6523 4242 11116 2224 2224 2260 383 3980 3980 ...
## $ 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 : num 97 3 57 89 43 77 9 123 125 57 ...
## $ 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 ""," Christiansburg",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_DATE : Factor w/ 6663 levels "","1/1/1993 0:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 ...
## $ COUNTYENDN: logi NA NA NA NA NA NA ...
## $ 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 ""," CANTON"," TULIA",..: 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 ...
## $ F : int 3 2 2 2 2 2 2 1 3 3 ...
## $ 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","%SD",..: 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 "","\t","\t\t",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...names(stormData)## [1] "STATE__" "BGN_DATE" "BGN_TIME" "TIME_ZONE" "COUNTY"
## [6] "COUNTYNAME" "STATE" "EVTYPE" "BGN_RANGE" "BGN_AZI"
## [11] "BGN_LOCATI" "END_DATE" "END_TIME" "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE" "END_AZI" "END_LOCATI" "LENGTH" "WIDTH"
## [21] "F" "MAG" "FATALITIES" "INJURIES" "PROPDMG"
## [26] "PROPDMGEXP" "CROPDMG" "CROPDMGEXP" "WFO" "STATEOFFIC"
## [31] "ZONENAMES" "LATITUDE" "LONGITUDE" "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS" "REFNUM"Get a summary of the data
summary(stormData)## 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) :588295head(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 6Data Cleaning
Since we are concerned by the event types that have a bad impact on health and economy, we select the data pertaining to those ones only
stormSubset <- stormData[,c(8,23:28)]
str(stormSubset)## 'data.frame': 902297 obs. of 7 variables:
## $ EVTYPE : Factor w/ 985 levels " HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
## $ 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 ...fatalities <- aggregate(stormSubset$FATALITIES, by = list(stormSubset$EVTYPE), FUN = sum)
injuries <- aggregate(stormSubset$INJURIES, by = list(stormSubset$EVTYPE), FUN = sum)
dataSubset <- cbind(fatalities, injuries[, 2])
colnames(dataSubset) <- c("type", "fatalities", "injuries")show some of the event types and their numbers
head(unique(stormSubset$EVTYPE), 20)## [1] TORNADO TSTM WIND
## [3] HAIL FREEZING RAIN
## [5] SNOW ICE STORM/FLASH FLOOD
## [7] SNOW/ICE WINTER STORM
## [9] HURRICANE OPAL/HIGH WINDS THUNDERSTORM WINDS
## [11] RECORD COLD HURRICANE ERIN
## [13] HURRICANE OPAL HEAVY RAIN
## [15] LIGHTNING THUNDERSTORM WIND
## [17] DENSE FOG RIP CURRENT
## [19] THUNDERSTORM WINS FLASH FLOOD
## 985 Levels: HIGH SURF ADVISORY COASTAL FLOOD ... WNDlength(unique(stormSubset$EVTYPE))## [1] 985Events that are most harmful to population health
sortedData_fatalities <- dataSubset[order(-dataSubset$fatalities), ]
sortedData_injuries <- dataSubset[order(-dataSubset$injuries), ]A look to the sorted data by descending numbers in fatalities and injuries
head(sortedData_fatalities[, 1:2])## type fatalities
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816
## 856 TSTM WIND 504head(sortedData_injuries[,c(1, 3)])## type injuries
## 834 TORNADO 91346
## 856 TSTM WIND 6957
## 170 FLOOD 6789
## 130 EXCESSIVE HEAT 6525
## 464 LIGHTNING 5230
## 275 HEAT 2100Results
From the above numbers tornados are the most devastatrice to the population
Events that are most harmful to the population with respect to health
Plots
par(mar = c(5, 4, 4, 5) + 0.1)
plot(sortedData_fatalities[1:10, 2], type = "b", col = "green", ylab = "")
text(sortedData_fatalities[1:10, 2], row.names(sortedData_fatalities[1:10, ]),
cex = 0.8, pos = 3, col = "green")
mtext("fatalities", side = 2, line = 3)
par(new = TRUE)
plot(sortedData_injuries[1:10, 3], type = "b", col = "blue", xaxt = "n", yaxt = "n",
xlab = "", ylab = "")
text(sortedData_injuries[1:10, 3], row.names(sortedData_injuries[1:10, ]), cex = 0.8,
pos = 1, col = "blue")
axis(4)
mtext("injuries", side = 4, line = 3)
grid()
legend("topright", col = c("green", "blue"), lty = 1, legend = c("fatalites",
"injuries"))
Events that have the greatest economic consequences
From the “National Weather Service Storm Data Documentation” in page 12 the units corresponding to the letters are given:
“Alphabetical characters used to signify magnitude include “K” for thousands, “M” for millions, and “B” for billions.” The variables “PROPDMGEXP” and “CROPDMGEXP represent the property and crop damage and the letters mentioned above are used as multipliers for the magnitude of the damages.
The list of the multipliers is shown below
unique(stormSubset$PROPDMGEXP)## [1] K M B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels: - ? + 0 1 2 3 4 5 6 7 8 B h H K m Munique(stormSubset$CROPDMGEXP)## [1] M K m B ? 0 k 2
## Levels: ? 0 2 B k K m Mtable(stormSubset$PROPDMGEXP)##
## - ? + 0 1 2 3 4 5
## 465934 1 8 5 216 25 13 4 4 28
## 6 7 8 B h H K m M
## 4 5 1 40 1 6 424665 7 11330table(stormSubset$CROPDMGEXP)##
## ? 0 2 B k K m M
## 618413 7 19 1 9 21 281832 1 1994We compute the damages values using the multipliers shown above
stormSubset$PROPDMG[stormSubset$PROPDMGEXP == ""] <- stormSubset$PROPDMG[stormSubset$PROPDMGEXP == ""] *
1
stormSubset$PROPDMG[stormSubset$PROPDMGEXP == "h"] <- stormSubset$PROPDMG[stormSubset$PROPDMGEXP ==
"h"] * 100
stormSubset$PROPDMG[stormSubset$PROPDMGEXP == "k"] <- stormSubset$PROPDMG[stormSubset$PROPDMGEXP ==
"k"] * 1000
stormSubset$PROPDMG[stormSubset$PROPDMGEXP == "m"] <- stormSubset$PROPDMG[stormSubset$PROPDMGEXP ==
"m"] * 1e+06
stormSubset$PROPDMG[stormSubset$PROPDMGEXP == "B"] <- stormSubset$PROPDMG[stormSubset$PROPDMGEXP ==
"B"] * 1e+09
stormSubset$CROPDMG[stormSubset$CROPDMGEXP == ""] <- stormSubset$CROPDMG[stormSubset$CROPDMGEXP == ""] *
1
stormSubset$CROPDMG[stormSubset$CROPDMGEXP == "h"] <- stormSubset$CROPDMG[stormSubset$CROPDMGEXP ==
"h"] * 100
stormSubset$CROPDMG[stormSubset$CROPDMGEXP == "k"] <- stormSubset$CROPDMG[stormSubset$CROPDMGEXP ==
"k"] * 1000
stormSubset$CROPDMG[stormSubset$CROPDMGEXP == "m"] <- stormSubset$CROPDMG[stormSubset$CROPDMGEXP ==
"m"] * 1e+06
stormSubset$CROPDMG[stormSubset$CROPDMGEXP == "B"] <- stormSubset$CROPDMG[stormSubset$CROPDMGEXP ==
"B"] * 1e+09Produce the damaged property and crop data
propdmg <- aggregate(stormSubset$PROPDMG, by = list(stormSubset$EVTYPE), FUN = sum)
cropdmg <- aggregate(stormSubset$CROPDMG, by = list(stormSubset$EVTYPE), FUN = sum)
stormSubset3 <- cbind(propdmg, cropdmg[, 2])
colnames(stormSubset3) <- c("type", "propdmg", "cropdmg")
### Sort the resulting data in descending order
stormSubset3_propdmg <- stormSubset3[order(-stormSubset3$propdmg), ]
stormSubset3_cropdmg <- stormSubset3[order(-stormSubset3$cropdmg), ]Plot the resulting data
par(mar = c(5, 4, 4, 5) + 0.1)
plot(stormSubset3_propdmg[1:10, 2], type = "b", col = "red", ylab = "")
mtext("propdmg in USD", side = 2, line = 3)
text(stormSubset3_propdmg[1:10, 2], row.names(stormSubset3_propdmg[1:10, ]), cex = 0.8,
pos = 1, col = "red")
par(new = TRUE)
plot(stormSubset3_cropdmg[1:10, 3], type = "b", col = "green", xaxt = "n", yaxt = "n",
xlab = "", ylab = "")
text(stormSubset3_cropdmg[1:10, 3], row.names(stormSubset3_cropdmg[1:10, ]), cex = 0.8,
pos = 3, col = "green")
axis(4)
mtext("cropdmg in USD", side = 4, line = 3)
grid()
legend("top", col = c("red", "green"), lty = 1, legend = c("propdmg", "cropdmg"))
Conclusion
head(sortedData_fatalities[, 1:2])## type fatalities
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816
## 856 TSTM WIND 504head(sortedData_injuries[,c(1, 3)])## type injuries
## 834 TORNADO 91346
## 856 TSTM WIND 6957
## 170 FLOOD 6789
## 130 EXCESSIVE HEAT 6525
## 464 LIGHTNING 5230
## 275 HEAT 2100The above data and the graphs show that:
- Tornado and Heat are the most harmful events on fatalities and injuries.
- Flood and Hurricane are the most harmful events on property damage and crop damage.