Econmic and Health Consequences of US Storms since 1950
Synopsis
The purpose of this analysis is to assess the economic and health consequences that the storms leave in its trail.
The NOAA Storm Database has documented all the major weather events since 1950. After each weather event the National Weather Service with the help of its regional offices collects details about the storm, the exact location where it started and ended, damage to property & crops, fatalities, injuries and other details about the storm. Data from all the 50 states are maintained by the National Climatic Data Center.
In this analysis, we will leverage this database. For every major event we will use the associated event type like Winter Storm, Hurricane, Snow, etc.to group and analyze consequences.
Data Processing
In order to perform our analysis, we need to first understand the data provided and also transform that into a format that will help us perform the analysis better.
To begin with the data is available as a ‘.csv’ file compressed under the ‘.bz2’ format. While we can unzip teh file and perform a read.csv operation on the ‘.csv’, we can do the same using ‘.bz2’ file also. Loading the data will take a few seconds more when ‘.bz2’ file is used, because the data has to be decompressed and then read.
Once we have the data, we can use the head, summary and str commands to explore the data at a high level
## Load Data
StormData <- read.csv("repdata-data-StormData.csv.bz2")
## Initial look at the data
dim(StormData)## [1] 902297 37head(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 6str(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 ...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) :588295The property damage and crop damage data has been provided on two fields each. The first field has magnitude of the damage (upto 3 significant digits) and the second field has the degree of the damage (i.e. thousands, million, billion, etc.). We will create a field tht will combine both to provde one numerical value each for property damage and crop damage.
We use the table command to understand all the different degrees of damage.
table(StormData$PROPDMGEXP)##
## - ? + 0 1 2 3 4 5
## 465934 1 8 5 216 25 13 4 4 28
## 6 7 8 B h H K m M
## 4 5 1 40 1 6 424665 7 11330StormData$PROPDAMAGE[StormData$PROPDMGEXP == "H" | StormData$PROPDMGEXP == "h"] <- StormData$PROPDMG[StormData$PROPDMGEXP == "H" | StormData$PROPDMGEXP == "h"]*100
StormData$PROPDAMAGE[StormData$PROPDMGEXP == "K"] <- StormData$PROPDMG[StormData$PROPDMGEXP == "K"]*1000
StormData$PROPDAMAGE[StormData$PROPDMGEXP == "M" | StormData$PROPDMGEXP == "m"] <- StormData$PROPDMG[StormData$PROPDMGEXP == "M" | StormData$PROPDMGEXP == "m"]*1000000
StormData$PROPDAMAGE[StormData$PROPDMGEXP == "B"] <- StormData$PROPDMG[StormData$PROPDMGEXP == "B"]*1000000000
summary(StormData$PROPDAMAGE)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00e+00 0.00e+00 1.00e+03 9.80e+05 1.00e+04 1.15e+11 466248table(StormData$CROPDMGEXP)##
## ? 0 2 B k K m M
## 618413 7 19 1 9 21 281832 1 1994StormData$CROPDAMAGE[StormData$CROPDMGEXP == "H" | StormData$CROPDMGEXP == "h"] <- StormData$CROPDMG[StormData$CROPDMGEXP == "H" | StormData$CROPDMGEXP == "h"]*100
StormData$CROPDAMAGE[StormData$CROPDMGEXP == "K"] <- StormData$CROPDMG[StormData$CROPDMGEXP == "K"]*1000
StormData$CROPDAMAGE[StormData$CROPDMGEXP == "M" | StormData$CROPDMGEXP == "m"] <- StormData$CROPDMG[StormData$CROPDMGEXP == "M" | StormData$CROPDMGEXP == "m"]*1000000
StormData$CROPDAMAGE[StormData$CROPDMGEXP == "B"] <- StormData$CROPDMG[StormData$CROPDMGEXP == "B"]*1000000000
summary(StormData$CROPDAMAGE)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00e+00 0.00e+00 0.00e+00 1.73e+05 0.00e+00 5.00e+09 618461Next, we calculate the total damage including property damage and crop damage. We will consider the total damage from a weather event as its economic consequence.
StormData$TOTDAMAGE <- rowSums(cbind(StormData$PROPDAMAGE, StormData$CROPDAMAGE), na.rm = TRUE)We then calculate the sum of fatalities and injuries. We will consider this as the weather event’s health consequence. In doing so, we recognize that both fatalities and injuries as health events, though they might have different levels of severity.
StormData$HEALTHDAMAGE <- rowSums(cbind(StormData$FATALITIES, StormData$INJURIES), na.rm = TRUE)Analysis
Health Impact of Storms
As mentioned in the data transformation, we consider death and injury equally as a health impact. We now proceeed to understand the health impact by each event type. We aggregate the newly created total health damage variable by each event type and find the top events that caused the most health impact
HealthDamageSummary <- aggregate(StormData$HEALTHDAMAGE, by = list(StormData$EVTYPE), FUN = sum)
HealthDamageSummary <- HealthDamageSummary[order(-HealthDamageSummary[,2]),]
names(HealthDamageSummary) <- c("EVTYPE", "HEALTHDAMAGE")
rownames(HealthDamageSummary) <- seq(length = nrow(HealthDamageSummary))We use the ggplot plotting mechanism to plot the 8 severe event types interms of health impact. Tornadoes by far had the highest health impact.
library(ggplot2)## Warning: package 'ggplot2' was built under R version 3.1.3qplot(EVTYPE, HEALTHDAMAGE, data = head(HealthDamageSummary, 10), geom = "bar", stat = "identity", xlab = "Event Type", ylab = "Total Health Impact (Fatalities + Injuries)", main = "Health Impact Assessment due to US Storms")
Economic Impact of Storms
Next, we perform a similar analysis for economic impact. Using the newly created variable on the dataframe that contains the total damage to property and crops, we identify the top 8 event types that caused the most economic impact.
EconDamageSummary <- aggregate(StormData$TOTDAMAGE, by = list(StormData$EVTYPE), FUN = sum)
EconDamageSummary <- EconDamageSummary[order(-EconDamageSummary[,2]),]
names(EconDamageSummary) <- c("EVTYPE", "ECONDAMAGE")
rownames(EconDamageSummary) <- seq(length = nrow(EconDamageSummary))We use the ggplot plotting mechanism to plot the 8 severe event types interms of economic impact. Floods by far had the highest economic impact.
qplot(EVTYPE, ECONDAMAGE, data = head(EconDamageSummary, 10), geom = "bar", stat = "identity", xlab = "Event Type", ylab = "Total Economic Impact (Property + Crops) in USD", main = "Economic Impact Assessment due to US Storms")
Results
From the above analysis, we can conclude: 1. Tornadoes by far cause the highest damage to the health of the population. 2. Floods cause the highest economic damage. 3. Tornadoes are the severest in terms of both health and economic damage. 4. Some of the event types which cause economic impact are not as deadly on the health front (like drought, river flood, etc.) as they provide time to evacuate the affected population.