Storm_Analysis
Sergio Cordero
April 27, 2016
Synopsis
This research is meant to address 2 questions:
- Across the United States, which types of events are most harmful with respect to population health?
- Across the United States, which types of events have the greatest economic consequences?
This questions will be answered so that we can better prioritize natural disaster preparations.
Dependencies
library(ggplot2)
library(dplyr)Data Processing
- Download data from [https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz
- Import CSV into R
dt <- read.csv(bzfile("repdata_data_StormData.csv.bz2"))Data Understanding
dim(dt)## [1] 902297 37head(dt, 1)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14 100 3 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1summary(dt)## 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 original idea was to calculate the severity of each natural disaster based on duration so that there is some reference to population exposure to said event. This is because a natural disaster with greater length will be more difficult to defend from. However, after seeing the summary of the dataframe, we see that END_DATE has 243,411 empty strings, and since that is about 30% of the database, time won’t be as reliable for reference.
Results
We will start by assigning a weight of 5x for fatalities, since we consider fatalitiews to be a more serious accident than injuries. After that, we just group the sum of the injuries and 5xfatalities by EVTYPE so we can see which event had more effect on human health.
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, unionlibrary(ggplot2)
alfa <- data.frame(dt[,"FATALITIES"]*5)
colnames(alfa) <- c("WEIGHTED FATALITIES")
dt <- cbind(dt, alfa)
h_cost <- dt %>%
group_by(EVTYPE) %>%
summarise(FATALITIES_COST = sum(`WEIGHTED FATALITIES`), INJURIES_COST = sum(INJURIES)) %>%
mutate(COST = FATALITIES_COST+INJURIES_COST) %>%
arrange(desc(COST))
head(h_cost)## Source: local data frame [6 x 4]
##
## EVTYPE FATALITIES_COST INJURIES_COST COST
## (fctr) (dbl) (dbl) (dbl)
## 1 TORNADO 28165 91346 119511
## 2 EXCESSIVE HEAT 9515 6525 16040
## 3 TSTM WIND 2520 6957 9477
## 4 LIGHTNING 4080 5230 9310
## 5 FLOOD 2350 6789 9139
## 6 HEAT 4685 2100 6785ggplot(head(h_cost, 10)) +
geom_point(aes(EVTYPE, COST), color=c("red"), shape=c(17), size=10) +
geom_point(aes(EVTYPE, INJURIES_COST), color=c("blue"), shape=c(10), size=4) +
geom_point(aes(EVTYPE, FATALITIES_COST), color=c("green"), shape=c(10), size=5)
We can clearly see that on the subject of health, the tornado is the most harmful by far. Perhaps for future research a good idea would be to map this damage on area, so we can pinpoint areas of opportunity since most tornadoes occur on tornado alley. On second place excessive heat could be an interesting problem to attack, since preparation is more passive and difficult to alocate. However, in this case, perhaps some research into time of the year where we find excessive heat to better prepare the population could be a future development.
Now, for economic consequences, we follow a similar path. However, here we have property damage and crop damage columns, which will be used to calculate economic cost of each disaster and map it in a similar way.
library(dplyr)
library(ggplot2)
e_cost <- dt %>%
group_by(EVTYPE) %>%
summarise(PROPERTY_DAMAGE = sum(PROPDMG), CROP_DAMAGE = sum(CROPDMG)) %>%
mutate(COST = PROPERTY_DAMAGE+CROP_DAMAGE) %>%
arrange(desc(COST))
head(e_cost)## Source: local data frame [6 x 4]
##
## EVTYPE PROPERTY_DAMAGE CROP_DAMAGE COST
## (fctr) (dbl) (dbl) (dbl)
## 1 TORNADO 3212258.2 100018.52 3312276.7
## 2 FLASH FLOOD 1420124.6 179200.46 1599325.1
## 3 TSTM WIND 1335965.6 109202.60 1445168.2
## 4 HAIL 688693.4 579596.28 1268289.7
## 5 FLOOD 899938.5 168037.88 1067976.4
## 6 THUNDERSTORM WIND 876844.2 66791.45 943635.6ggplot(head(e_cost, 10)) +
geom_point(aes(EVTYPE, COST), color=c("red"), shape=c(17), size=10) +
geom_point(aes(EVTYPE, PROPERTY_DAMAGE), color=c("yellow"), shape=c(10), size=4) +
geom_point(aes(EVTYPE, CROP_DAMAGE), color=c("orange"), shape=c(10), size=4)
Once again, we find that tornado is by far the most dangerous natural disaster. However, here we find that flash flood is a major force in economic damage, being half of that of a tornado (in comparison to health cost, this is quite a lot). Perhaps an incidence mapping will help identify troublesome rivers and lakes, so as to better prepare the nearby settlements. Thunderstorm winds, flood, hail, and thunderstorms are also quite troublesome, and considering flash flood and flood have similar preparations, flood preparations should be quite relevant.