Analysis of the impact of weather events on economy and health in the United States
Massimo Zanetti
Thursday, February 12, 2015
Synopsis
This document contains an analysis of data from U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database and aims to study the impact of major storms on population’s heath and economy.
This is the second assignment of Reproducible Research Course by Prof. R.Peng on Coursera.
We perform an analysis of the consequences of severe weather events on population’s health end economy.
We analyze the impact of severe weather events considering both the total damage and the average magnitude of events of the same type.
We find different clusters of events distinguishing rare vs non rare events and weak vs powerful events.
Data Processing
Data for the analysis are provided in csv format by course website at the following link.
Data are collected from 1950 to November 2011.
Before starting the analysis we ensure to have the global options required by the assignment.
The following packages are required:
- knitr
- dplyr
- ggplot2
- bitops
- RCurl
- R.utils
- lubridate
- xtable
library ( bitops )
library ( RCurl )##
## Attaching package: 'RCurl'
##
## The following object is masked from 'package:R.utils':
##
## reset
##
## The following object is masked from 'package:R.oo':
##
## clone library ( knitr )
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, union library ( ggplot2 )
library ( R.utils )
library ( lubridate )
library ( xtable )
opts_chunk$set(echo = TRUE , fig.height = 4)
# set locale to get names in English
lct <- Sys.getlocale("LC_TIME")
Sys.setlocale("LC_TIME", "C")Data are contained in the file repdata-data-StormData.csv.bz2 in the working directory.
In order to read the data we must unzip the file and then read the resulting csv file.
Given the size of the file this operation can take a few minutes.
bunzip2("repdata-data-StormData.csv.bz2", destname=gsub("[.]bz2$", "", "repdata-data-StormData.csv.bz2"), overwrite=TRUE, remove=FALSE)
storm <- read.csv ( "repdata-data-StormData.csv" , header = TRUE )After loading the data we can examine the content of the dataset.
dim ( storm )## [1] 902297 37 str ( storm )## '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 "","- 1 N Albion",..: 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 "","- .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 ...
## $ 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","$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/ 436774 levels "","-2 at Deer Park\n",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ... head ( storm )## 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 6NAs are not a problem because relevant columns are complete.
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 JEFFERSON : 7840 TX : 83728
## EST :245558 1st Qu.: 31 WASHINGTON: 7603 KS : 53440
## MST : 68390 Median : 75 JACKSON : 6660 OK : 46802
## PST : 28302 Mean :101 FRANKLIN : 6256 MO : 35648
## AST : 6360 3rd Qu.:131 LINCOLN : 5937 IA : 31069
## HST : 2563 Max. :873 MADISON : 5632 NE : 30271
## (Other): 3631 (Other) :862369 (Other):621339
## EVTYPE BGN_RANGE BGN_AZI
## HAIL :288661 Min. : 0 :547332
## TSTM WIND :219940 1st Qu.: 0 N : 86752
## THUNDERSTORM WIND: 82563 Median : 0 W : 38446
## TORNADO : 60652 Mean : 1 S : 37558
## FLASH FLOOD : 54277 3rd Qu.: 1 E : 33178
## FLOOD : 25326 Max. :3749 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 :724837
## 1st Qu.:0 NA's:902297 1st Qu.: 0 N : 28082
## Median :0 Median : 0 S : 22510
## Mean :0 Mean : 1 W : 20119
## 3rd Qu.:0 3rd Qu.: 0 E : 20047
## Max. :0 Max. :925 NE : 14606
## (Other): 72096
## END_LOCATI LENGTH WIDTH F
## :499225 Min. : 0.0 Min. : 0 Min. :0
## COUNTYWIDE : 19731 1st Qu.: 0.0 1st Qu.: 0 1st Qu.:0
## SOUTH PORTION : 833 Median : 0.0 Median : 0 Median :1
## NORTH PORTION : 780 Mean : 0.2 Mean : 8 Mean :1
## CENTRAL PORTION: 617 3rd Qu.: 0.0 3rd Qu.: 0 3rd Qu.:1
## SPRINGFIELD : 575 Max. :2315.0 Max. :4400 Max. :5
## (Other) :380536 NA's :843563
## MAG FATALITIES INJURIES PROPDMG
## Min. : 0 Min. : 0 Min. : 0.0 Min. : 0
## 1st Qu.: 0 1st Qu.: 0 1st Qu.: 0.0 1st Qu.: 0
## Median : 50 Median : 0 Median : 0.0 Median : 0
## Mean : 47 Mean : 0 Mean : 0.2 Mean : 12
## 3rd Qu.: 75 3rd Qu.: 0 3rd Qu.: 0.0 3rd Qu.: 0
## Max. :22000 Max. :583 Max. :1700.0 Max. :5000
##
## PROPDMGEXP CROPDMG CROPDMGEXP WFO
## :465934 Min. : 0.0 :618413 :142069
## K :424665 1st Qu.: 0.0 K :281832 OUN : 17393
## M : 11330 Median : 0.0 M : 1994 JAN : 13889
## 0 : 216 Mean : 1.5 k : 21 LWX : 13174
## B : 40 3rd Qu.: 0.0 0 : 19 PHI : 12551
## 5 : 28 Max. :990.0 B : 9 TSA : 12483
## (Other): 84 (Other): 9 (Other):690738
## STATEOFFIC
## :248769
## TEXAS, North : 12193
## ARKANSAS, Central and North Central: 11738
## IOWA, Central : 11345
## KANSAS, Southwest : 11212
## GEORGIA, North and Central : 11120
## (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) :588294We format the dates so later we can have access to the dates functions.
begin_date <- as.POSIXlt ( storm$BGN_DATE , format="%m/%d/%Y" )
end_date <- as.POSIXlt ( storm$END_DATE , format="%m/%d/%Y" )
# create year and decade variable
begin_year <- year ( begin_date )
decade <- ((begin_year %% 100) %/% 10) * 10
decade[decade < 50] <- decade[decade < 50] + 100
# add new variables
storm <- cbind ( storm , begin_date , end_date , begin_year , decade )
# keep workspace clean
rm ( begin_date , end_date , begin_year , decade )Having observed an unequal distribution of events in the first decades we decide to focus on observations starting from 1994.
storm <-storm[ storm$begin_year>=1994 ,]The columns reporting damage amount (PROPDMG, PROPDMGEXP, CROPDMG ,CROPDMGEXP) should be read together. First coumns contains the damage amount rounded to 3 significant digits. The EXP column contain the metric. The codebook reports the valid values for EXP are H K M B. Since there are only few valid values we remove them. We also create a binary variable indicating whether there have been an economic damage.
# new property damage exp column
newPropExp <- rep ( 0 , length= nrow(storm))
newPropExp[storm$PROPDMGEXP %in% c("H","h")] <- 1e2
newPropExp[storm$PROPDMGEXP %in% c("K","k")] <- 1e3
newPropExp[storm$PROPDMGEXP %in% c("M","m")] <- 1e6
newPropExp[storm$PROPDMGEXP %in% c("B","b")] <- 1e9
# new crop damage exp column
newCropExp <- rep ( 0 , length= nrow(storm))
newCropExp[storm$CROPDMGEXP %in% c("H","h")] <- 1e2
newCropExp[storm$CROPDMGEXP %in% c("K","k")] <- 1e3
newCropExp[storm$CROPDMGEXP %in% c("M","m")] <- 1e6
newCropExp[storm$CROPDMGEXP %in% c("B","b")] <- 1e9
# flag for economic damage presence
prop_damage <- storm$PROPDMG * newPropExp
crop_damage <- storm$CROPDMG * newCropExp
economic_damage <- rep ( 0 , length= nrow(storm))
economic_damage [ newPropExp>0 | newCropExp >0 ] <- 1
storm <- cbind ( storm , prop_damage , crop_damage , economic_damage )
# keep workspace clean
rm ( newPropExp , newCropExp , prop_damage , crop_damage , economic_damage )The columns reporting population damage are Fatalities and Injuries.
We create a binary variable indicating whether there have been health damages.
# flag for population damage presence
health_damage <- rep ( 0 , length= nrow(storm))
health_damage[ storm$FATALITIES>0 | storm$INJURIES >0 ] <- 1
storm <- cbind ( storm , health_damage )
rm ( health_damage )Now we proceed to format the event variable.
There are over 985 different entries for the event type.
There are a lot of mispelled labels. We process them in order to have a clean list of events.
storm$event <- factor( tolower( storm$EVTYPE )) # uniformly lower all the letters
storm <- storm[ -grep( "^summary(.*| *.*)" , x= storm$event) , ] # remove summary events
storm$event <- factor( storm$event ) # remove unused levels
# format levels
storm$event <- gsub( pattern= "tstm(.*| *.*)" , replacement= "thunderstorm", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "thunderstorm(.*| *.*)" , replacement= "thunderstorm", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "thu(.*) wind(.*)" , replacement= "thunderstorm", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "(.*)storm wind(.*)" , replacement= "thunderstorm", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "tornado(.*| *.*)" , replacement= "tornado", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "tropical(.*| *.*)" , replacement= "tropical storm", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "aval(.*)" , replacement= "avalanche", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "blizzard(.*| *.*)" , replacement= "blizzard", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "(.*| *.*)blizzard(.*| *.*)" , replacement= "blizzard", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "winter storm(.*)" , replacement= "winter storm", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "winter we(.*)" , replacement= "winter weather", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "wild(.*| .*)" , replacement= "wildfire", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "(.*| .*)waterspout(.*| .*)" , replacement= "waterspout", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "lig(.*)ing(.*| *.*)" , replacement= "lightning", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "lake(.*| *.*)effect(.*| *.*)" , replacement= "lake-effect snow", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "hurricane(.*| .*)" , replacement= "hurricane", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "high wind(.*| .*)" , replacement= "high wind", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "heavy snow(.*| .*)" , replacement= "heavy snow", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "heavy rain(.*| .*)" , replacement= "heavy rain", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "cold(.*| .*)" , replacement= "cold/wind chill", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "drought(.*| .*)" , replacement= "drought", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "coast(.*| .*)flood(.*)" , replacement= "coastal flood", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "(.*| .*)surf(.*| .*)" , replacement= "high surf", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "hail(.*| .*)" , replacement= "hail", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "heat(.*| .*)" , replacement= "heat", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "dust devil(.*| .*)" , replacement= "dust devil", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "dust storm(.*| .*)" , replacement= "dust storm", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "volcanic(.*| .*)" , replacement= "volcanic ash", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "urban(.*| .*)flood(.*| .*)" , replacement= "flood", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "river(.*| .*)flood(.*| .*)" , replacement= "flood", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "urban(.*| .*)small(.*| .*)" , replacement= "flood", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "rip(.*| .*)curr(.*| .*)" , replacement= "rip current", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "ext(.*| .*)cold(.*| .*)" , replacement= "extreme cold/wind chill", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "ext(.*| .*)wind(.*| .*)" , replacement= "extreme cold/wind chill", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "storm(.*| .*)surge(.*| .*)" , replacement= "storm surge/tide", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "storm(.*| .*)tide(.*| .*)" , replacement= "storm surge/tide", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "(.*| .*)high(.*| .*)tide(.*| .*)" , replacement= "storm surge/tide", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "flash(.*| .*)flood(.*| .*)" , replacement= "flash flood", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "flood(.*| .*)flash(.*| .*)" , replacement= "flash flood", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "frost( *)" , replacement= "frost/freeze", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "freeze( *)" , replacement= "frost/freeze", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "frost(.*| .*)" , replacement= "frost/freeze", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "(.*| .*)frost(.*| .*)" , replacement= "frost/freeze", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "lake(.*| .*)flood(.*| .*)" , replacement= "lakeshore flood", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "(.*| .*)ice(.*| .*)storm(.*| .*)" , replacement= "ice storm", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "(.*| .*)storm(.*| .*)ice(.*| .*)" , replacement= "ice storm", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "mud(.*| .*)slide(.*| .*)" , replacement= "mud slide", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "(.*| .*)sleet(.*| .*)" , replacement= "sleet", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "hypothermia(.*| .*)" , replacement= "cold/wind chill", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "(.*| .*)surge(.*| .*)" , replacement= "storm surge/tide", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "strong wind(.*| .*)" , replacement= "strong wind", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "flood(.*| .*)" , replacement= "flood", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "falling snow(.*| .*)" , replacement= "avalanche", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "low temp(.*| .*)" , replacement= "cold/wind chill", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "coastal(.*| .*)" , replacement= "coastal flood", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "unseason(.*| .*)cold(.*| .*)" , replacement= "cold/wind chill", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "unseason(.*| .*)warm(.*| .*)" , replacement= "heat", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "record(.*| .*)cold(.*| .*)" , replacement= "extreme cold/wind chill", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "record(.*| .*)heat(.*| .*)" , replacement= "excessive heat", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "thundersnow" , replacement= "ice storm", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "small(.*| .*)flood(.*| .*)" , replacement= "flood", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "hyperthermia(.*| .*)" , replacement= "heat", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "warm(.*| .*)" , replacement= "heat", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "small hail(.*| .*)" , replacement= "hail", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "major(.*| .*)flood(.*| .*)" , replacement= "flood", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "minor(.*| .*)flood(.*| .*)" , replacement= "flood", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "severe(.*| .*)thunder(.*| .*)" , replacement= "thunderstorm", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "cstl flood(.*| .*)" , replacement= "coastal flood", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "rural flood(.*| .*)" , replacement= "flood", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "forest fire(.*| .*)" , replacement= "wildfire", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "tidal(.*| .*)" , replacement= "storm surge/tide", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "wind storm(.*| .*)" , replacement= "thunderstorm", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "torn(.*| .*)" , replacement= "tornado", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "(.*| .*)coastal flood(.*| .*)" , replacement= "coastal flood", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "snowmelt flood(.*| .*)" , replacement= "flood", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "^( *)thunderstorm" , replacement= "thunderstorm", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "(.*| .*)heavy snow(.*| .*)" , replacement= "heavy snow", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "record snow(.*| .*)" , replacement= "heavy snow", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "^snow$" , replacement= "heavy snow", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "(.*| .*)blowing snow(.*| .*)" , replacement= "heavy snow", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "(.*| .*)hvy rain" , replacement= "heavy rain", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "^fog$" , replacement= "dense fog", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "(.*| .*)typhoon(.*| .*)" , replacement= "hurricane", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "(.*| .*)flash(.*| .*)" , replacement= "flash flood", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "( *)high(.*| .*)win(.*| .*)" , replacement= "high wind", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "(.*| .*)marine(.*| .*)high(.*| .*)win(.*| .*)" , replacement= "marine high wind", x= storm$event , ignore.case= TRUE)
storm$event <- gsub( pattern= "^wind chill(.*| .*)" , replacement= "cold/wind chill", x= storm$event , ignore.case= TRUE)
storm$event <- factor ( storm$event ) # remove unused levelsNow the different entries for the event type are 308.
In order to reduce noise we decide to eliminate the rows with events not contained
in Storm Data Event Table on page 6 of NATIONAL WEATHER SERVICE INSTRUCTION.
In addition we add a short name for the event, in order to use it for graphical purposes.
valid_events <- c("astronomical low tide" , "avalanche" , "blizzard", "coastal flood",
"cold/wind chill", "dense fog", "dense smoke","drought",
"dust devil", "dust storm", "excessive heat","extreme cold/wind chill",
"flash flood", "flood", "frost/freeze", "funnel cloud",
"freezing fog", "hail", "heat", "heavy rain",
"heavy snow", "high surf", "high wind", "hurricane",
"ice storm", "lake-effect snow", "lakeshore flood", "lightning",
"marine hail", "marine high wind", "marine strong wind", "marine thunderstorm",
"rip current", "seiche", "sleet", "storm surge/tide",
"strong wind", "thunderstorm", "tornado", "tropical storm",
"tsunami", "volcanic ash", "waterspout", "wildfire",
"winter storm", "winter weather" )
valid_events <- data.frame ( valid_events )
valid_events$id <- 1:nrow( valid_events )
colnames ( valid_events )[1] <- "event"
storm$event_code <- as.numeric ( storm$event ) # id of event
storm <- storm[ storm$event %in% valid_events$event, ]
storm$event <- factor ( storm$event ) # clean levels
storm$short_name <- lapply ( strsplit(as.vector(storm$event) , split=" ") , substr , start=1, stop=4 )
storm$short_name <- unlist(lapply ( storm$short_name , paste , sep="", collapse=".") )The preprocessing step is complete. We can now proceed to analyze the clean dataset.
Results
Analysis of weather events impact on population health?
We initialize the dataset to be processed with dplyr functions
storm <- tbl_df ( storm )In order to assess the impact of the extreme events on population health we compute the number of
injuries and fatalities caused by each event type in the period since 1994.
pop_health_events <-
storm %>%
group_by (event , short_name ) %>%
summarise(injuries= sum(INJURIES), fatalities= sum (FATALITIES),
events= n(), damage_event = sum(health_damage) ) %>%
mutate ( tot_damage = injuries+fatalities ,
perc_damage = damage_event/events ) %>%
mutate ( damage_per_event = tot_damage / events ) %>%
arrange ( -tot_damage )For each type of event we compute:
* injuries
* fatalities
* events: number of registered events
* damage_event: number of events that caused damages
* tot_damage: fatalities + injuries
* perc_damage: percentage of damaging events
* damage_per_event: average damage per event
library ( xtable )
xt <- xtable( pop_health_events )
print ( xt , type= "html")| event | short_name | injuries | fatalities | events | damage_event | tot_damage | perc_damage | damage_per_event | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | tornado | torn | 22589.00 | 1593.00 | 25308 | 2185.00 | 24182.00 | 0.09 | 0.96 |
| 2 | excessive heat | exce.heat | 6575.00 | 1922.00 | 1759 | 681.00 | 8497.00 | 0.39 | 4.83 |
| 3 | flood | floo | 6782.00 | 459.00 | 25586 | 403.00 | 7241.00 | 0.02 | 0.28 |
| 4 | thunderstorm | thun | 5988.00 | 436.00 | 230026 | 2650.00 | 6424.00 | 0.01 | 0.03 |
| 5 | lightning | ligh | 5118.00 | 795.00 | 15326 | 3210.00 | 5913.00 | 0.21 | 0.39 |
| 6 | heat | heat | 2508.00 | 1152.00 | 987 | 243.00 | 3660.00 | 0.25 | 3.71 |
| 7 | flash flood | flas.floo | 1770.00 | 1007.00 | 54640 | 945.00 | 2777.00 | 0.02 | 0.05 |
| 8 | ice storm | ice.stor | 1989.00 | 87.00 | 1991 | 96.00 | 2076.00 | 0.05 | 1.04 |
| 9 | wildfire | wild | 1456.00 | 87.00 | 4231 | 331.00 | 1543.00 | 0.08 | 0.36 |
| 10 | high wind | high.wind | 1259.00 | 256.00 | 21307 | 589.00 | 1515.00 | 0.03 | 0.07 |
| 11 | winter storm | wint.stor | 1313.00 | 196.00 | 11406 | 222.00 | 1509.00 | 0.02 | 0.13 |
| 12 | hurricane | hurr | 1332.00 | 135.00 | 295 | 70.00 | 1467.00 | 0.24 | 4.97 |
| 13 | heavy snow | heav.snow | 1024.00 | 132.00 | 15801 | 195.00 | 1156.00 | 0.01 | 0.07 |
| 14 | dense fog | dens.fog | 1063.00 | 75.00 | 1825 | 121.00 | 1138.00 | 0.07 | 0.62 |
| 15 | rip current | rip.curr | 529.00 | 572.00 | 774 | 637.00 | 1101.00 | 0.82 | 1.42 |
| 16 | hail | hail | 953.00 | 10.00 | 222782 | 211.00 | 963.00 | 0.00 | 0.00 |
| 17 | winter weather | wint.weat | 538.00 | 61.00 | 8102 | 76.00 | 599.00 | 0.01 | 0.07 |
| 18 | extreme cold/wind chill | extr.cold.chil | 259.00 | 295.00 | 1939 | 220.00 | 554.00 | 0.11 | 0.29 |
| 19 | blizzard | bliz | 390.00 | 71.00 | 2692 | 72.00 | 461.00 | 0.03 | 0.17 |
| 20 | dust storm | dust.stor | 439.00 | 22.00 | 426 | 44.00 | 461.00 | 0.10 | 1.08 |
| 21 | tropical storm | trop.stor | 383.00 | 66.00 | 753 | 43.00 | 449.00 | 0.06 | 0.60 |
| 22 | high surf | high.surf | 246.00 | 165.00 | 1063 | 159.00 | 411.00 | 0.15 | 0.39 |
| 23 | strong wind | stro.wind | 301.00 | 110.00 | 3772 | 234.00 | 411.00 | 0.06 | 0.11 |
| 24 | avalanche | aval | 171.00 | 225.00 | 383 | 241.00 | 396.00 | 0.63 | 1.03 |
| 25 | heavy rain | heav.rain | 241.00 | 98.00 | 11778 | 120.00 | 339.00 | 0.01 | 0.03 |
| 26 | cold/wind chill | cold.chil | 60.00 | 168.00 | 701 | 125.00 | 228.00 | 0.18 | 0.33 |
| 27 | tsunami | tsun | 129.00 | 33.00 | 20 | 2.00 | 162.00 | 0.10 | 8.10 |
| 28 | storm surge/tide | stor.surg | 43.00 | 13.00 | 532 | 11.00 | 56.00 | 0.02 | 0.11 |
| 29 | marine thunderstorm | mari.thun | 34.00 | 19.00 | 11987 | 17.00 | 53.00 | 0.00 | 0.00 |
| 30 | dust devil | dust.devi | 43.00 | 2.00 | 145 | 15.00 | 45.00 | 0.10 | 0.31 |
| 31 | marine strong wind | mari.stro.wind | 22.00 | 14.00 | 48 | 16.00 | 36.00 | 0.33 | 0.75 |
| 32 | waterspout | wate | 33.00 | 2.00 | 3764 | 6.00 | 35.00 | 0.00 | 0.01 |
| 33 | coastal flood | coas.floo | 9.00 | 10.00 | 830 | 12.00 | 19.00 | 0.01 | 0.02 |
| 34 | drought | drou | 4.00 | 2.00 | 2491 | 2.00 | 6.00 | 0.00 | 0.00 |
| 35 | frost/freeze | fros | 3.00 | 1.00 | 1494 | 1.00 | 4.00 | 0.00 | 0.00 |
| 36 | funnel cloud | funn.clou | 3.00 | 0.00 | 6676 | 2.00 | 3.00 | 0.00 | 0.00 |
| 37 | marine high wind | mari.high.wind | 1.00 | 1.00 | 135 | 1.00 | 2.00 | 0.01 | 0.01 |
| 38 | sleet | slee | 0.00 | 2.00 | 114 | 1.00 | 2.00 | 0.01 | 0.02 |
| 39 | astronomical low tide | astr.low.tide | 0.00 | 0.00 | 174 | 0.00 | 0.00 | 0.00 | 0.00 |
| 40 | dense smoke | dens.smok | 0.00 | 0.00 | 10 | 0.00 | 0.00 | 0.00 | 0.00 |
| 41 | freezing fog | free.fog | 0.00 | 0.00 | 46 | 0.00 | 0.00 | 0.00 | 0.00 |
| 42 | lake-effect snow | lake.snow | 0.00 | 0.00 | 657 | 0.00 | 0.00 | 0.00 | 0.00 |
| 43 | lakeshore flood | lake.floo | 0.00 | 0.00 | 24 | 0.00 | 0.00 | 0.00 | 0.00 |
| 44 | marine hail | mari.hail | 0.00 | 0.00 | 442 | 0.00 | 0.00 | 0.00 | 0.00 |
| 45 | seiche | seic | 0.00 | 0.00 | 21 | 0.00 | 0.00 | 0.00 | 0.00 |
| 46 | volcanic ash | volc.ash | 0.00 | 0.00 | 29 | 0.00 | 0.00 | 0.00 | 0.00 |
We can easily verify that the event type with the greatest impact on the population health
is tornado with nearly 24.182 thousand occurrences.
A different view of the impact compare damage and frequency of events.
In the following chart we can see that events cluster in different ways.
There are weak events that caused a lot of damage because of their frequency (i.e. thunderstorms),
and there are rare but very harmful events (such as tsunamis).
ggplot( subset( pop_health_events , damage_event>=1 ) ,
aes( x= log10(events) , y= log10(tot_damage) , size= damage_per_event ) ) +
geom_point(shape=21, colour="black", fill="cornsilk") +
geom_text(aes( x= log10(events) , y= log10(tot_damage) , label=short_name), size=4 ) +
labs( title="Chart 1\nImpact on population health\nper event (1994-2011)" ,
x="Event Frequency" , y="Total Damage" )+
scale_size_area(max_size=20) +
scale_y_continuous(breaks=0:4, labels=10^c(0:4))+
scale_x_continuous(breaks=2:4, labels=10^c(2:4))
Chart 1: Each event is represented with respect to the number of observations in the period
and the total damages. The size of the circle represents the average magnitude per event (fatalities+injures).
Analysis of economic consequences of weather events
In order to compute the impact of the extreme events on economy we compute the number of
injuries and fatalities caused by each event type in the period since 1994.
econ_events <-
storm %>%
mutate ( tot_damage= prop_damage + crop_damage )%>%
group_by ( event , short_name ) %>%
summarise ( events= n() ,
damage_events= sum(economic_damage) ,
tot_damage=sum( tot_damage ) ,
prop_damage= sum( prop_damage) ,
crop_damage=sum( crop_damage ) ) %>%
mutate ( perc_damage = damage_events/events , damage_per_event = tot_damage/ events )%>%
arrange ( -tot_damage )For each type of event we compute: * events: number of registered events
* damage_events: number of events that caused damages
* tot_damage: total damages in USD
* prop_damage: damages to properties
* crop_damage: damages to crops
* perc_damage: percentage of damaging events
* damage_per_event: average damage per event
Data are sorted by total damages.
xt <- xtable( econ_events )
print ( xt , type= "html")| event | short_name | events | damage_events | tot_damage | prop_damage | crop_damage | perc_damage | damage_per_event | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | flood | floo | 25586 | 17539.00 | 150172206650.00 | 144490820700.00 | 5681385950.00 | 0.69 | 5869311.60 |
| 2 | hurricane | hurr | 295 | 224.00 | 90816527810.00 | 85300910010.00 | 5515617800.00 | 0.76 | 307852636.64 |
| 3 | storm surge/tide | stor.surg | 532 | 323.00 | 47845017000.00 | 47844162000.00 | 855000.00 | 0.61 | 89934242.48 |
| 4 | tornado | torn | 25308 | 16900.00 | 25986972420.00 | 25625139910.00 | 361832510.00 | 0.67 | 1026828.37 |
| 5 | hail | hail | 222782 | 93514.00 | 18580948920.00 | 15577316670.00 | 3003632250.00 | 0.42 | 83404.18 |
| 6 | flash flood | flas.floo | 54640 | 32943.00 | 17990711610.00 | 16477220060.00 | 1513491550.00 | 0.60 | 329259.00 |
| 7 | drought | drou | 2491 | 1533.00 | 14968177780.00 | 1046106000.00 | 13922071780.00 | 0.62 | 6008903.16 |
| 8 | thunderstorm | thun | 230026 | 155792.00 | 11794391430.00 | 10531054330.00 | 1263337100.00 | 0.68 | 51274.17 |
| 9 | ice storm | ice.stor | 1991 | 1303.00 | 8855041310.00 | 3832927810.00 | 5022113500.00 | 0.65 | 4447534.56 |
| 10 | tropical storm | trop.stor | 753 | 631.00 | 8410013550.00 | 7715622550.00 | 694391000.00 | 0.84 | 11168676.69 |
| 11 | wildfire | wild | 4231 | 2324.00 | 8280845130.00 | 7877563500.00 | 403281630.00 | 0.55 | 1957183.91 |
| 12 | high wind | high.wind | 21307 | 14484.00 | 6084335610.00 | 5416038810.00 | 668296800.00 | 0.68 | 285555.71 |
| 13 | heavy rain | heav.rain | 11778 | 5848.00 | 4014950940.00 | 3224195140.00 | 790755800.00 | 0.50 | 340885.63 |
| 14 | frost/freeze | fros | 1494 | 1065.00 | 1904761000.00 | 18700000.00 | 1886061000.00 | 0.71 | 1274940.43 |
| 15 | winter storm | wint.stor | 11406 | 7495.00 | 1637041250.00 | 1620097250.00 | 16944000.00 | 0.66 | 143524.57 |
| 16 | extreme cold/wind chill | extr.cold.chil | 1939 | 931.00 | 1457263400.00 | 127240400.00 | 1330023000.00 | 0.48 | 751554.10 |
| 17 | lightning | ligh | 15326 | 10712.00 | 889238720.00 | 877251630.00 | 11987090.00 | 0.70 | 58021.58 |
| 18 | heavy snow | heav.snow | 15801 | 6979.00 | 886744740.00 | 757561640.00 | 129183100.00 | 0.44 | 56119.53 |
| 19 | blizzard | bliz | 2692 | 1839.00 | 539218950.00 | 532158950.00 | 7060000.00 | 0.68 | 200304.22 |
| 20 | excessive heat | exce.heat | 1759 | 727.00 | 500155700.00 | 7753700.00 | 492402000.00 | 0.41 | 284340.93 |
| 21 | coastal flood | coas.floo | 830 | 614.00 | 438876560.00 | 438820560.00 | 56000.00 | 0.74 | 528766.94 |
| 22 | heat | heat | 987 | 647.00 | 419528550.00 | 12457050.00 | 407071500.00 | 0.66 | 425054.26 |
| 23 | strong wind | stro.wind | 3772 | 3390.00 | 247572740.00 | 177619240.00 | 69953500.00 | 0.90 | 65634.34 |
| 24 | tsunami | tsun | 20 | 19.00 | 144082000.00 | 144062000.00 | 20000.00 | 0.95 | 7204100.00 |
| 25 | high surf | high.surf | 1063 | 463.00 | 101025000.00 | 101025000.00 | 0.00 | 0.44 | 95037.63 |
| 26 | cold/wind chill | cold.chil | 701 | 533.00 | 99286600.00 | 2544050.00 | 96742550.00 | 0.76 | 141635.66 |
| 27 | winter weather | wint.weat | 8102 | 6808.00 | 42298000.00 | 27298000.00 | 15000000.00 | 0.84 | 5220.69 |
| 28 | lake-effect snow | lake.snow | 657 | 618.00 | 40182000.00 | 40182000.00 | 0.00 | 0.94 | 61159.82 |
| 29 | dense fog | dens.fog | 1825 | 1012.00 | 22224500.00 | 22224500.00 | 0.00 | 0.55 | 12177.81 |
| 30 | dust storm | dust.stor | 426 | 238.00 | 9194000.00 | 5594000.00 | 3600000.00 | 0.56 | 21582.16 |
| 31 | waterspout | wate | 3764 | 1106.00 | 8414700.00 | 8414700.00 | 0.00 | 0.29 | 2235.57 |
| 32 | lakeshore flood | lake.floo | 24 | 24.00 | 7570000.00 | 7570000.00 | 0.00 | 1.00 | 315416.67 |
| 33 | marine thunderstorm | mari.thun | 11987 | 5918.00 | 5907400.00 | 5857400.00 | 50000.00 | 0.49 | 492.82 |
| 34 | avalanche | aval | 383 | 192.00 | 3716800.00 | 3716800.00 | 0.00 | 0.50 | 9704.44 |
| 35 | freezing fog | free.fog | 46 | 39.00 | 2182000.00 | 2182000.00 | 0.00 | 0.85 | 47434.78 |
| 36 | sleet | slee | 114 | 62.00 | 1500000.00 | 1500000.00 | 0.00 | 0.54 | 13157.89 |
| 37 | marine high wind | mari.high.wind | 135 | 135.00 | 1297010.00 | 1297010.00 | 0.00 | 1.00 | 9607.48 |
| 38 | seiche | seic | 21 | 12.00 | 980000.00 | 980000.00 | 0.00 | 0.57 | 46666.67 |
| 39 | dust devil | dust.devi | 145 | 104.00 | 712630.00 | 712630.00 | 0.00 | 0.72 | 4914.69 |
| 40 | volcanic ash | volc.ash | 29 | 5.00 | 500000.00 | 500000.00 | 0.00 | 0.17 | 17241.38 |
| 41 | marine strong wind | mari.stro.wind | 48 | 48.00 | 418330.00 | 418330.00 | 0.00 | 1.00 | 8715.21 |
| 42 | astronomical low tide | astr.low.tide | 174 | 174.00 | 320000.00 | 320000.00 | 0.00 | 1.00 | 1839.08 |
| 43 | rip current | rip.curr | 774 | 285.00 | 163000.00 | 163000.00 | 0.00 | 0.37 | 210.59 |
| 44 | funnel cloud | funn.clou | 6676 | 2424.00 | 144600.00 | 144600.00 | 0.00 | 0.36 | 21.66 |
| 45 | dense smoke | dens.smok | 10 | 9.00 | 100000.00 | 100000.00 | 0.00 | 0.90 | 10000.00 |
| 46 | marine hail | mari.hail | 442 | 209.00 | 4000.00 | 4000.00 | 0.00 | 0.47 | 9.05 |
Floods and hurricanes caused the greatest economic damage.
Even with respect to economic damage we can observe the clustering between the
destruction power and frequency of events. We focus on events with total damage greater than 1 million dollars.
ggplot( subset(econ_events , tot_damage>=1e6) ,
aes( x= log10(events) , y= log10(tot_damage/1e6) , size= damage_per_event ) ) +
geom_point(shape=21, colour="black", fill="cornsilk") +
geom_text(aes( x= log10(events) , y= log10(tot_damage/1e6) , label= short_name ), size=4 ) +
labs( title="Chart 2\nEconomic consequences\nper event (1994-2011)" , x="Event Frequency" ,y="Total Damage (mln $)" )+
scale_size_area(max_size=30) +
scale_y_continuous(breaks=0:5, labels=10^c(0:5))+
scale_x_continuous(breaks=1:5, labels=10^c(1:5))
Chart 2: Each event is represented with respect to the number of observations in the period
and the total damages. The size of the circle represents the average magnitude per event (USD). We can see there are events with high frequency but low impact per single event
(such as floods and hails) that have high impact on the economy. We can also see that events like hurricanes or storm surge/tide have high impact
due to their power.
Conclusions
The analysis of the impact of severe weather events on population’s health and economy
shows different patterns in damaging events. Events with greatest impact on population’s health can be divided into high frequency/low magnitude
(such as tornadoes, thunderstorms and floods) and medium frequency/high magnitude
(hurricanes, heat and ice storm).
Events with greatest impact on population’s economy con be divided into high frequency/low magnitude
(such as tornadoes and floods) and low frequency/high magnitude
(hurricanes, storm surge / tide).
Hurricanes, floods and tornadoes are the most harmful events observed in the period 1994-2011.