An Analysis Report of Health and Economic Impact by Severe Weather Events - Based on NOAA Storm Database

Synopsis

Storm and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severs events can results in fatalities, injuries and property damage. Preventing such outcomes to the extent possible is a key concern. The U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database tracks characteristics of major storms and weather events in the United States, include when and where they occur, aswell as estimates of any fatalities, injuries and property damage. This report contains the exploratory analysis results on the health and economic impact by the severe weather events based on the data from NOAA database.

Data Processing

The file is first downloaded from the URL, then it was unzip and then loaded into R.

# download file from URL
if (!file.exists("C:/Users/sue/Documents/GitHub/RepData_PeerAssessment2/repdata-data-StormData.csv.bz2")) {
    download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", 
        "C:/Users/sue/Documents/GitHub/RepData_PeerAssessment2/repdata-data-StormData.csv.bz2")
}
# unzip file
if (!file.exists("C:/Users/sue/Documents/GitHub/RepData_PeerAssessment2/repdata-data-StormData.csv")) {
    library(R.utils)
    bunzip2("C:/Users/sue/Documents/GitHub/RepData_PeerAssessment2/repdata-data-StormData.csv.bz2", "C:/Users/sue/Documents/GitHub/RepData_PeerAssessment2/repdata-data-StormData.csv", remove = FALSE)
}
# load data into R
storm <- read.csv("C:/Users/sue/Documents/GitHub/RepData_PeerAssessment2/repdata-data-StormData.csv")
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              6

2. Checking out the data that contains the weather event, health and economic impact data

# exploring the data contents
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              6
# subset the data to health and economic impact analysis against weather
# event
mycol <- c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", 
    "CROPDMGEXP")
mydata <- storm[mycol]

Preparing the property damage data

# exploring the property exponent
unique(mydata$PROPDMGEXP)
##  [1] K M   B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels:  - ? + 0 1 2 3 4 5 6 7 8 B h H K m M
# Sorting the property exponent data
mydata$PROPEXP[mydata$PROPDMGEXP == "K"] <- 1000
mydata$PROPEXP[mydata$PROPDMGEXP == "M"] <- 1e+06
mydata$PROPEXP[mydata$PROPDMGEXP == ""] <- 1
mydata$PROPEXP[mydata$PROPDMGEXP == "B"] <- 1e+09
mydata$PROPEXP[mydata$PROPDMGEXP == "m"] <- 1e+06
mydata$PROPEXP[mydata$PROPDMGEXP == "0"] <- 1
mydata$PROPEXP[mydata$PROPDMGEXP == "5"] <- 1e+05
mydata$PROPEXP[mydata$PROPDMGEXP == "6"] <- 1e+06
mydata$PROPEXP[mydata$PROPDMGEXP == "4"] <- 10000
mydata$PROPEXP[mydata$PROPDMGEXP == "2"] <- 100
mydata$PROPEXP[mydata$PROPDMGEXP == "3"] <- 1000
mydata$PROPEXP[mydata$PROPDMGEXP == "h"] <- 100
mydata$PROPEXP[mydata$PROPDMGEXP == "7"] <- 1e+07
mydata$PROPEXP[mydata$PROPDMGEXP == "H"] <- 100
mydata$PROPEXP[mydata$PROPDMGEXP == "1"] <- 10
mydata$PROPEXP[mydata$PROPDMGEXP == "8"] <- 1e+08
# give 0 to invalid exponent data, so they not count in
mydata$PROPEXP[mydata$PROPDMGEXP == "+"] <- 0
mydata$PROPEXP[mydata$PROPDMGEXP == "-"] <- 0
mydata$PROPEXP[mydata$PROPDMGEXP == "?"] <- 0
# compute the property damage value
mydata$PROPDMGVAL <- mydata$PROPDMG * mydata$PROPEXP

Preparing the crop damage data

# exploring the crop exponent data
unique(mydata$CROPDMGEXP)
## [1]   M K m B ? 0 k 2
## Levels:  ? 0 2 B k K m M
# Sorting the property exponent data
mydata$CROPEXP[mydata$CROPDMGEXP == "M"] <- 1e+06
mydata$CROPEXP[mydata$CROPDMGEXP == "K"] <- 1000
mydata$CROPEXP[mydata$CROPDMGEXP == "m"] <- 1e+06
mydata$CROPEXP[mydata$CROPDMGEXP == "B"] <- 1e+09
mydata$CROPEXP[mydata$CROPDMGEXP == "0"] <- 1
mydata$CROPEXP[mydata$CROPDMGEXP == "k"] <- 1000
mydata$CROPEXP[mydata$CROPDMGEXP == "2"] <- 100
mydata$CROPEXP[mydata$CROPDMGEXP == ""] <- 1
# give 0 to invalid exponent data, so they not count in
mydata$CROPEXP[mydata$CROPDMGEXP == "?"] <- 0
# compute the crop damage value
mydata$CROPDMGVAL <- mydata$CROPDMG * mydata$CROPEXP

Aggregate the data by event

# aggregate the data by event
fatal <- aggregate(FATALITIES ~ EVTYPE, data = mydata, FUN = sum)
injury <- aggregate(INJURIES ~ EVTYPE, data = mydata, FUN = sum)
propdmg <- aggregate(PROPDMGVAL ~ EVTYPE, data = mydata, FUN = sum)
cropdmg <- aggregate(CROPDMGVAL ~ EVTYPE, data = mydata, FUN = sum)

Results

Across the United States, whic types of events are most harmful with respect to population health?

# get top10 event with highest fatalities
fatal10 <- fatal[order(-fatal$FATALITIES), ][1:10, ]
# get top10 event with highest injuries
injury10 <- injury[order(-injury$INJURIES), ][1:10, ]
par(mfrow = c(1, 2), mar = c(12, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(fatal10$FATALITIES, las = 3, names.arg = fatal10$EVTYPE, main = "Weather Events With The Top 10 Highest Fatalities", 
    ylab = "number of fatalities", col = "red")
barplot(injury10$INJURIES, las = 3, names.arg = injury10$EVTYPE, main = "Weather Events With the Top 10 Highest Injuries", 
    ylab = "number of injuries", col = "red")

From the graph the most harmful weather event to population health is Tornado. It has caused the highest fatalities and the highest injuries across the United States.

Across the United States, which types of events have the greatest economic consequences?

# get top 10 events with highest property damage
propdmg10 <- propdmg[order(-propdmg$PROPDMGVAL), ][1:10, ]
# get top 10 events with highest crop damage
cropdmg10 <- cropdmg[order(-cropdmg$CROPDMGVAL), ][1:10, ]
par(mfrow = c(1, 2), mar = c(12, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(propdmg10$PROPDMGVAL/(10^9), las = 3, names.arg = propdmg10$EVTYPE, 
    main = "Top 10 Events with Greatest Property Damages", ylab = "Cost of damages ($ billions)", 
    col = "red")
barplot(cropdmg10$CROPDMGVAL/(10^9), las = 3, names.arg = cropdmg10$EVTYPE, 
    main = "Top 10 Events With Greatest Crop Damages", ylab = "Cost of damages ($ billions)", 
    col = "red")

The weather event caused the greatest economic consequences. They are flood, drought, tornado and typhoon.

Across the United States, flood, tornado and typhoon have caused the greatest damage to properties. While drought and flood was the reason that caused the greatest damage to the crops.