Severe Weather Events Damage Report Across the United States
Synopsis
Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severe events can result in fatalities, injuries, and property damage, and preventing such outcomes to the extent possible is a key concern.
This report involves exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database (raw data here). This database tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage. So here two questions will be answered:
1. Across the United States, which types of events are most harmful with respect to population health?
2. Across the United States, which types of events have the greatest economic consequences?
Data Processing
- Set global environment and load necessary libraries.
require(knitr)
opts_chunk$set(echo = TRUE, cache=TRUE, fig.path = "figure/", fig.width = 6, fig.height = 6)
library(dplyr)- Read the raw data, and have a general look into it.
if(!file.exists("repdata_data_StormData.csv.bz2")) {
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2",
destfile = "repdata-data-StormData.csv.bz2")
}
zipData<- bzfile("repdata-data-StormData.csv.bz2")
StormData <- read.csv(zipData)
str(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 "","- 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/ 436781 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 ...- To answer the 1st question, both FATALITIES and INJURIES need to be counted together according to EVTYPE
psData <- mutate(StormData, HEALTH = FATALITIES + INJURIES)
HealthData <- psData %>%
group_by(EVTYPE) %>%
summarise(sumHealth = sum(HEALTH)) %>%
arrange(desc(sumHealth))- As for the 2nd question, the property damage need to be calculated with their exponents. And then calculate the sum according to EVTYPE
psData <- mutate(psData, PropDamage =
ifelse(toupper(PROPDMGEXP) == "H", PROPDMG *100,
ifelse(toupper(PROPDMGEXP) == "K", PROPDMG *10^3,
ifelse(toupper(PROPDMGEXP) == "M", PROPDMG * 10^6,
ifelse(toupper(PROPDMGEXP) == "B", PROPDMG * 10^9,
ifelse(is.numeric(PROPDMGEXP), PROPDMG * 10 ^ PROPDMGEXP,
PROPDMG))))))
DamageData <- psData %>%
group_by(EVTYPE) %>%
summarise(sumDamage = sum(PropDamage)) %>%
arrange(desc(sumDamage))Results
- Now we can look at the figure showing the top 10 severe weather evernts that cause more fatalities and injuries.
t10Health <- head(HealthData, 10)
print(t10Health)## Source: local data frame [10 x 2]
##
## EVTYPE sumHealth
## 1 TORNADO 96979
## 2 EXCESSIVE HEAT 8428
## 3 TSTM WIND 7461
## 4 FLOOD 7259
## 5 LIGHTNING 6046
## 6 HEAT 3037
## 7 FLASH FLOOD 2755
## 8 ICE STORM 2064
## 9 THUNDERSTORM WIND 1621
## 10 WINTER STORM 1527barplot(t10Health$sumHealth, beside = TRUE, names.arg = t10Health$EVTYPE, col = rainbow(10),
main = "Top 10 Severe Weather Events Harmful to Population Health",
ylab = "Number of Fatalities and Injuries",
cex.names = 0.6)
From the plot we can see obviously that tornado is the most harmful event accross the United States.
- For the top 10 events that cause more property damage, we can see the figure below
t10Damage <- head(DamageData, 10)
print(t10Damage)## Source: local data frame [10 x 2]
##
## EVTYPE sumDamage
## 1 FLOOD 144657908100
## 2 HURRICANE/TYPHOON 69305840450
## 3 TORNADO 56937381105
## 4 STORM SURGE 43323538150
## 5 FLASH FLOOD 16141344885
## 6 HAIL 15737184570
## 7 HURRICANE 11868320210
## 8 TROPICAL STORM 7703893675
## 9 WINTER STORM 6688595275
## 10 HIGH WIND 5270198485barplot(t10Damage$sumDamage/10^9, beside = TRUE, names.arg = t10Damage$EVTYPE, col = rainbow(10),
main = "Top 10 Severe Weather Events Cause Damage to Economy",
ylab = "Property Damage (billion $)",
cex.names = 0.6)
From this plot we can see that flood have the greatest economic consequences, followed by hurricane/typhone.