NOAA Storm Events Data Analysis
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 project involves exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. 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.
Data
The data for this assignment come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. You can download the file from the course web site:
- Storm Data [47Mb]
There is also some documentation of the database available. Here you will find how some of the variables are constructed/defined.
National Weather Service Storm Data Documentation
National Climatic Data Center Storm Events FAQ
The events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete.
Assignment
The basic goal of this assignment is to explore the NOAA Storm Database and answer the following basic questions about severe weather events.
1.Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
2.Across the United States, which types of events have the greatest economic consequences?
Data Processing
data <- read.csv("/home/ubuntu/R/data/repdata_data_StormData.csv.bz2")
head(data)## 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 6Extracting the required data
This dataset consists of lot of information most of which is not required for our present study. So, here is the code to extract the required data for health and economic impact analysis against weather.
data2 <- data.frame(data$EVTYPE, data$FATALITIES, data$INJURIES, data$PROPDMG,
data$PROPDMGEXP, data$CROPDMG, data$CROPDMGEXP, stringsAsFactors = FALSE)Agregating fatalities and injuries
It was observed that " most harmful to population health" events are fatalities and injuries.So,only those events with fatalities and injuries were selecetd.
# Aggregate for fatal
fatal <- aggregate(data.FATALITIES ~ data.EVTYPE, data2, FUN = sum)
colnames(fatal)<- c("EVTYPE","FATALITIES")
## Aggregate for injuries
injury <- aggregate(data.INJURIES ~ data.EVTYPE, data2, FUN = sum)
colnames(injury)<- c("EVTYPE","INJURIES")Plot showing types of events that are most harmful to population health
Highest fatalities and highest injuries for Top 8 events were calculated.
# Listing events with highest fatalities
fatal8 <- fatal[order(-fatal$FATALITIES), ][1:8, ]
# Listing events with highest injuries
injury8 <- injury[order(-injury$INJURIES), ][1:8, ]
# Plot for highest fatal and highest injuries
par(mfrow = c(1, 2))
barplot(fatal8$FATALITIES, las = 3, names.arg = fatal8$EVTYPE, main = "Events with Highest Fatalities", ylab = "Number of fatalities", col = "light pink")
barplot(injury8$INJURIES, las = 3, names.arg = injury8$EVTYPE, main = "Events with Highest Injuries", ylab = "Number of injuries", col = "light pink")
Assigning and Calculating property damage value
unique(data$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# Assigning values for the property exponent data
data$PROPEXP[data$PROPDMGEXP == "K"] <- 1000
data$PROPEXP[data$PROPDMGEXP == "M"] <- 1e+06
data$PROPEXP[data$PROPDMGEXP == ""] <- 1
data$PROPEXP[data$PROPDMGEXP == "B"] <- 1e+09
data$PROPEXP[data$PROPDMGEXP == "m"] <- 1e+06
data$PROPEXP[data$PROPDMGEXP == "0"] <- 1
data$PROPEXP[data$PROPDMGEXP == "5"] <- 1e+05
data$PROPEXP[data$PROPDMGEXP == "6"] <- 1e+06
data$PROPEXP[data$PROPDMGEXP == "4"] <- 10000
data$PROPEXP[data$PROPDMGEXP == "2"] <- 100
data$PROPEXP[data$PROPDMGEXP == "3"] <- 1000
data$PROPEXP[data$PROPDMGEXP == "h"] <- 100
data$PROPEXP[data$PROPDMGEXP == "7"] <- 1e+07
data$PROPEXP[data$PROPDMGEXP == "H"] <- 100
data$PROPEXP[data$PROPDMGEXP == "1"] <- 10
data$PROPEXP[data$PROPDMGEXP == "8"] <- 1e+08
# Assigning '0' to invalid exponent data
data$PROPEXP[data$PROPDMGEXP == "+"] <- 0
data$PROPEXP[data$PROPDMGEXP == "-"] <- 0
data$PROPEXP[data$PROPDMGEXP == "?"] <- 0
# Calculating the property damage value
data$PROPDMGVAL <- data$PROPDMG * data$PROPEXPAssigning and Calculating crop damage value
unique(data$CROPDMGEXP)## [1] M K m B ? 0 k 2
## Levels: ? 0 2 B k K m M# Assigning values for the crop exponent data
data$CROPEXP[data$CROPDMGEXP == "M"] <- 1e+06
data$CROPEXP[data$CROPDMGEXP == "K"] <- 1000
data$CROPEXP[data$CROPDMGEXP == "m"] <- 1e+06
data$CROPEXP[data$CROPDMGEXP == "B"] <- 1e+09
data$CROPEXP[data$CROPDMGEXP == "0"] <- 1
data$CROPEXP[data$CROPDMGEXP == "k"] <- 1000
data$CROPEXP[data$CROPDMGEXP == "2"] <- 100
data$CROPEXP[data$CROPDMGEXP == ""] <- 1
# Assigning '0' to invalid exponent data
data$CROPEXP[data$CROPDMGEXP == "?"] <- 0
# calculating the crop damage value
data$CROPDMGVAL <- data$CROPDMG * data$CROPEXPAgregating Property damage value and Crop damage value
It was observed that " most harmful to econamic problem“ events are Property and crop damages.So,only those events with property and crop damage were selecetd.
propdmg <- aggregate(PROPDMGVAL ~ EVTYPE, data, FUN = sum)
cropdmg <- aggregate(CROPDMGVAL ~ EVTYPE, data, FUN = sum)Plot showing types of events have the greatest economic consequences
# Finding events with highest property damage
propdmg8 <- propdmg[order(-propdmg$PROPDMGVAL), ][1:8, ]
# Finding events with highest crop damage
cropdmg8 <- cropdmg[order(-cropdmg$CROPDMGVAL), ][1:8, ]
par(mfrow = c(1, 2), mar = c(12, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(propdmg8$PROPDMGVAL/(10^9), las = 3, names.arg = propdmg8$EVTYPE,
main = "Events with Highest Property Damages",
ylab = "Damage Cost ($ billions)",col = "lightblue")
barplot(cropdmg8$CROPDMGVAL/(10^9), las = 3, names.arg = cropdmg8$EVTYPE,
main = "Events With Highest Crop Damages", ylab = "Damage Cost ($ billions)",
col = "lightblue")
Results
Tornados caused the maximum number of fatalities and injuries. It was followed by Excessive Heat for fatalities and Thunderstorm wind for injuries.
Floods caused the maximum property damage where as Drought caused the maximum crop damage. Second major events that caused the maximum damage was Hurricanes/Typhoos for property damage and Floods for crop damage.