Economic and Population Health Analytics of Storm Events
Cristhian Toribio Amaro
6/9/2020
Overview
Storms and other severe weather events have huge impact on public health and economic problems. This analysis present which types of events are most harmful with respect to population health and which have the greatest economic consequences analyzing the cuantitative daamges of all events.
Download and examine the dataset
I’m going to use The U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database which tracks characteristics of major storms and weather events in the United States. This dataset comes from the Internet.
Download file from the Internet:
link <- "http://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(url = link, destfile = "StormData")Read a file in table format:
StormData <- read.csv(bzfile("StormData"),sep = ",",header=TRUE)A view a little structure 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) :588295Data Processing
Property damage estimates were entered as actual dollar amounts (the variable PROPDMG). But they were rounded to three significant digits, followed by an alphabetical character signifying the magnitude of the number, i.e., 1.55B for $1,550,000,000. Alphabetical characters used to signify magnitude include ?K? for thousands, ?M? for millions, and ?B? for billions. So I created a new variable PROPDMGEXP2 and assigned conditionally “K” = 1000, “M” = 1000000, “B” = 1000000000, in other cases 1. These variables are multiplied in the next step.
table(StormData$PROPDMGEXP)##
## - ? + 0 1 2 3 4 5 6
## 465934 1 8 5 216 25 13 4 4 28 4
## 7 8 B h H K m M
## 5 1 40 1 6 424665 7 11330StormData$PROPDMGEXP2 <- 1
StormData$PROPDMGEXP2[which(StormData$PROPDMGEXP == "K")] <- 1000
StormData$PROPDMGEXP2[which(StormData$PROPDMGEXP == "M" | StormData$PROPDMGEXP == "m")] <- 1000000
StormData$PROPDMGEXP2[which(StormData$PROPDMGEXP == "B")] <- 1000000000table(StormData$PROPDMGEXP2)##
## 1 1000 1e+06 1e+09
## 466255 424665 11337 40Analyzing the impact to population health
Fatalities and injuries have the most impact on public health, so I will present what types of severe weather are the most dangerous.
The first plot presents a Death toll by Event type
StormData %>%
select(FATALITIES, EVTYPE) %>%
group_by(EVTYPE) %>%
summarise(SumFATALITIES = sum(FATALITIES)) %>%
top_n(n = 8, wt = SumFATALITIES) %>%
ggplot(aes(y = SumFATALITIES, x = reorder(x = EVTYPE, X = SumFATALITIES), fill=EVTYPE))+
geom_bar(stat = "identity", show.legend = FALSE) +
#geom_text(aes(label=SumFATALITIES), size = 4, hjust = 0.5, vjust = -0.1) +
xlab(label = "") +
ylab(label = "Death toll") +
coord_flip() +
theme_light()## `summarise()` ungrouping output (override with `.groups` argument)
The second plot presents Injuries by Event type
StormData %>%
select(INJURIES, EVTYPE) %>%
group_by(EVTYPE) %>%
summarise(SumINJURIES = sum(INJURIES)) %>%
top_n(n = 8, wt = SumINJURIES) %>%
ggplot(aes(y = SumINJURIES, x = reorder(x = EVTYPE, X = SumINJURIES), fill=EVTYPE))+
geom_bar(stat = "identity", show.legend = FALSE) +
#geom_text(aes(label=SumINJURIES), size = 4, hjust = 0.5, vjust = -0.1) +
xlab(label = "") +
ylab(label = "INJURIES") +
coord_flip() +
theme_light()## `summarise()` ungrouping output (override with `.groups` argument)
Analyzing the economics impact
This plot shows Property damage estimates by Event type
StormData %>%
select(PROPDMG, PROPDMGEXP2, EVTYPE) %>%
group_by(EVTYPE) %>%
mutate(SumPROPDMGEXP = (PROPDMG * PROPDMGEXP2)) %>%
summarise(SumPROPDMGEXP2 = sum(SumPROPDMGEXP)) %>%
top_n(n = 8, wt = SumPROPDMGEXP2) %>%
ggplot(aes(y = SumPROPDMGEXP2, x = reorder(x = EVTYPE, X = SumPROPDMGEXP2), fill=EVTYPE))+
geom_bar(stat = "identity", show.legend = FALSE) +
#geom_text(aes(label=SumFATALITIES), size = 4, hjust = 0.5, vjust = -0.1) +
xlab(label = "") +
ylab(label = "Property damage estimates") +
coord_flip() +
theme_light()## `summarise()` ungrouping output (override with `.groups` argument)
Conclusion
As you can see above flood has the greatest economic consequences. Tornado is the most harmful to population health because caused the most death tolls and injuries.
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