Analysis of U.S. Storm Event Data and it’s Impact on Public Health and the Economy
Gaurab Kundu
2022-10-19
Synopsis
In this project, we analyze the storm database taken from the U.S. National Oceanic and Atmospheric Administration (NOAA). We estimate the fatalities, injuries, property damage, and crop damage for each type of event (e.g., Flood, Typhoon, Tornado, Hail, Hurricane, etc.). Our goal is to determine which event is most harmful to US population (health) and which event has the largest economic consequences. Our analysis on Fatalities and Injuries conclude that Tornado is the most harmful event in respect to the US health (population). On the other hand, based on the Property and Cost damage, we conclude that Flood has the greatest economic consequences to the US.
Introduction
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 following link:
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.
Questions
The data analysis address the following 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?
Data Processing
Loading required libraries
library(data.table)
library(ggplot2)Loading the Data into R
data <- read.csv("repdata_data_StormData.csv.bz2", header = TRUE, sep=",")Inspecting the Data
colnames(data) #checking column names## [1] "STATE__" "BGN_DATE" "BGN_TIME" "TIME_ZONE" "COUNTY"
## [6] "COUNTYNAME" "STATE" "EVTYPE" "BGN_RANGE" "BGN_AZI"
## [11] "BGN_LOCATI" "END_DATE" "END_TIME" "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE" "END_AZI" "END_LOCATI" "LENGTH" "WIDTH"
## [21] "F" "MAG" "FATALITIES" "INJURIES" "PROPDMG"
## [26] "PROPDMGEXP" "CROPDMG" "CROPDMGEXP" "WFO" "STATEOFFIC"
## [31] "ZONENAMES" "LATITUDE" "LONGITUDE" "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS" "REFNUM"Subsetting the Data
We are only interested in the column related to health and economic impacts. Therefore, only the following columns are needed and we can remove the rest.
EVTYPE
FATALITIES
INJURIES
PROPDMG
PROPDMGEXP
CROPDMG
CROPDMGEXP
selection <- c('EVTYPE', 'FATALITIES', 'INJURIES', 'PROPDMG', 'PROPDMGEXP', 'CROPDMG', 'CROPDMGEXP')
data <- data[, selection]
summary(data)## EVTYPE FATALITIES INJURIES PROPDMG
## Length:902297 Min. : 0.0000 Min. : 0.0000 Min. : 0.00
## Class :character 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.00
## Mode :character Median : 0.0000 Median : 0.0000 Median : 0.00
## Mean : 0.0168 Mean : 0.1557 Mean : 12.06
## 3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.: 0.50
## Max. :583.0000 Max. :1700.0000 Max. :5000.00
## PROPDMGEXP CROPDMG CROPDMGEXP
## Length:902297 Min. : 0.000 Length:902297
## Class :character 1st Qu.: 0.000 Class :character
## Mode :character Median : 0.000 Mode :character
## Mean : 1.527
## 3rd Qu.: 0.000
## Max. :990.000We also only need to use the data where fatalities, injuries, or damages occured.
data <- as.data.table(data)
data <- data[(EVTYPE != "?" & (INJURIES > 0 | FATALITIES > 0 | PROPDMG > 0 | CROPDMG > 0)),
c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")]Converting the exponent columns (PROPDMGEXP and CROPDMGEXP)
We need to convert the exponent values from K, M, B to 1000, 1000000, and 1000000000.
cols <- c("PROPDMGEXP", "CROPDMGEXP")
data[, (cols) := c(lapply(.SD, toupper)), .SDcols = cols]
PROPDMGKey <- c("\"\"" = 10^0,
"-" = 10^0, "+" = 10^0, "0" = 10^0, "1" = 10^1, "2" = 10^2, "3" = 10^3,
"4" = 10^4, "5" = 10^5, "6" = 10^6, "7" = 10^7, "8" = 10^8, "9" = 10^9,
"H" = 10^2, "K" = 10^3, "M" = 10^6, "B" = 10^9)
CROPDMGKey <- c("\"\"" = 10^0, "?" = 10^0, "0" = 10^0, "K" = 10^3, "M" = 10^6, "B" = 10^9)
data[, PROPDMGEXP := PROPDMGKey[as.character(data[,PROPDMGEXP])]]
data[is.na(PROPDMGEXP), PROPDMGEXP := 10^0 ]
data[, CROPDMGEXP := CROPDMGKey[as.character(data[,CROPDMGEXP])] ]
data[is.na(CROPDMGEXP), CROPDMGEXP := 10^0 ]Creating two new columns of Property Cost and Crop Cost
Combining the coefficient (mantissa) and exponent part of Property and Crop Damage.
data <- data[, .(EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, PROPCOST = PROPDMG * PROPDMGEXP, CROPDMG, CROPDMGEXP, CROPCOST = CROPDMG * CROPDMGEXP)]Analysis
Estimating the total of Fatalities and Injuries (Health Impacts)
In order to know the health impact, we estimate the total of Fatalities and Injuries for each event.
Health_Impact <- data[, .(FATALITIES = sum(FATALITIES), INJURIES = sum(INJURIES), TOTAL_HEALTH_IMPACTS = sum(FATALITIES) + sum(INJURIES)), by = .(EVTYPE)]
Health_Impact <- Health_Impact[order(-TOTAL_HEALTH_IMPACTS), ]
Health_Impact <- Health_Impact[1:10, ]
head(Health_Impact, 10)## EVTYPE FATALITIES INJURIES TOTAL_HEALTH_IMPACTS
## 1: TORNADO 5633 91346 96979
## 2: EXCESSIVE HEAT 1903 6525 8428
## 3: TSTM WIND 504 6957 7461
## 4: FLOOD 470 6789 7259
## 5: LIGHTNING 816 5230 6046
## 6: HEAT 937 2100 3037
## 7: FLASH FLOOD 978 1777 2755
## 8: ICE STORM 89 1975 2064
## 9: THUNDERSTORM WIND 133 1488 1621
## 10: WINTER STORM 206 1321 1527Estimating the total of Property Cost and Crop Cost (Economic Impacts)
In order to know the economic impact, we estimate the total of Property Cost and Crop Cost for each event.
Eco_Impact <- data[, .(PROPCOST = sum(PROPCOST), CROPCOST = sum(CROPCOST), TOTAL_ECO_IMPACTS = sum(PROPCOST) + sum(CROPCOST)), by = .(EVTYPE)]
Eco_Impact <- Eco_Impact[order(-TOTAL_ECO_IMPACTS), ]
Eco_Impact <- Eco_Impact[1:10, ]
head(Eco_Impact, 10)## EVTYPE PROPCOST CROPCOST TOTAL_ECO_IMPACTS
## 1: FLOOD 144657709807 5661968450 150319678257
## 2: HURRICANE/TYPHOON 69305840000 2607872800 71913712800
## 3: TORNADO 56947380677 414953270 57362333947
## 4: STORM SURGE 43323536000 5000 43323541000
## 5: HAIL 15735267513 3025954473 18761221986
## 6: FLASH FLOOD 16822673979 1421317100 18243991079
## 7: DROUGHT 1046106000 13972566000 15018672000
## 8: HURRICANE 11868319010 2741910000 14610229010
## 9: RIVER FLOOD 5118945500 5029459000 10148404500
## 10: ICE STORM 3944927860 5022113500 8967041360Results
Question 1: Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Answer
We generate histogram to find the top 10 weather events that are most harmful to US population.
Health_Consequences <- melt(Health_Impact, id.vars = "EVTYPE", variable.name = "Fatalities_or_Injuries")
ggplot(Health_Consequences, aes(x = reorder(EVTYPE, -value), y = value)) +
geom_bar(stat = "identity", aes(fill = Fatalities_or_Injuries), position = "dodge") +
ylab("Total Injuries/Fatalities") +
xlab("Event Type") +
theme(axis.text.x = element_text(angle=45, hjust=1)) +
ggtitle("Top 10 US Weather Events that are Most Harmful to Population") +
theme(plot.title = element_text(hjust = 0.5))
Question 2: Events that have the greatest economic consequences
Answer
We generate histogram to find the top 10 weather events that have largest cost to US economy.
Eco_Consequences <- melt(Eco_Impact, id.vars = "EVTYPE", variable.name = "Damage_Type")
ggplot(Eco_Consequences, aes(x = reorder(EVTYPE, -value), y = value/1e9)) +
geom_bar(stat = "identity", aes(fill = Damage_Type), position = "dodge") +
ylab("Cost/Damage (in billion USD)") +
xlab("Event Type") +
theme(axis.text.x = element_text(angle=45, hjust=1)) +
ggtitle("Top 10 US Weather Events that have the Greatest Economic consequences") +
theme(plot.title = element_text(hjust = 0.5))