Reproducible Research Course Project 2: Health and Economic Impacts of US Severe Weather
giovanni barrero ortiz
3/10/2021
Exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database - Health and Economic Impacts
Synopsis
Public health and econimic problems are affected by a number of reasons i am now going to analysize how storms and other severe weather events play a part. Storms and severe weather conditions causes fatalities and injuries and substantial property damage. Hence to minimze damages we should analyse the given data. 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 (https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2) [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 (https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf) National Climatic Data Center Storm Events FAQ (https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2FNCDC%20Storm%20EventsFAQ%20Page.pdf) 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
Preparation / Load the required libraries
Loading and preprocessing the data The source data file is downloaded from https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2 (https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2). It covers wheather events between 1950 and 2011. Comprehensive documentation for the dataset is available: https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf (https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf) https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2FNCDC%20Storm%20Events-FAQ%20Page.pdf (https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2FNCDC%20Storm%20EventsFAQ%20Page.pdf)
0. Setup (load libraries)
library(data.table)
library(ggplot2)1. Loading and preprocessing the data
if (!file.exists("StormData.csv.bz2")) {
fileUrl<-"https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(fileUrl, destfile="StormData.csv.bz2", method="curl")
# Exit if the file is not available
if (!file.exists("StormData.csv.bz2")) {
stop("Can't locate file 'StormData.csv.bz2'!")
}
}
# Load the dataset
data <- read.csv("StormData.csv.bz2")2. Inspecting the data
Use colnames to check the column names:
colnames(data)## [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"This analysis takes the following variables into consideration: BGN_DATE : a date variable, used to subset the data set for observations between 1996 and 2011. EVTYPE : a variable indicating the event type of the particular observation, used to categorise per event type. FATALATIES : a variable indicating the number of fatalities caused by the particular observation, used to determine event types with the most negative consequences on population health. INJURIES : a variable indicating the number of injuries caused by the particular observation, used to determine event types with the most negative consequences on population health. PROPDMG : a variable indicating the estimated monetary value of damage to property caused by the particular observation, used to determine event types with the most negative consequences on the economy, rounded to three significant digits, in United States dollars. PROPDMGEXP : a variable indicating the multiplier for PROPDMG ; can be “K” for 1,000, “M” for 1,000,000 or “B” for 1,000,000,000 as per NWS Directive 10-1605. CROPDMG : a variable indicating the estimated monetary value of damage to agricultural property (crops) caused by the particular observation, used to determine event types with the most negative consequences on the economy, rounded to three significant digits, in United States dollars. CROPDMGEXP : a variable indicating the multiplier for CROPDMG ; can be “K” for 1,000, “M” for 1,000,000 or “B” for 1,000,000,000 as per NWS Directive 10-1605.
3. 3. 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")]4. 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 ]5. 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
1. Estimating the total of Fatalities and Injuries (Health Impacts)
n 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 15272. Estimating the total of Property Cost and Crop Cost (EconomicImpacts)
n 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
1. Answer to Question 1: Events that are most harmful with respect to population health
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 high 10 US Weather Events that are Most Harmful to Population") +
theme(plot.title = element_text(hjust = 0.5))
2. Answer to Question 2: Events that have the greatest economic consequences
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 High 10 US Weather Events that have the Greatest Economic consequences") +
theme(plot.title = element_text(hjust = 0.5))
CONCLUSION
To inform policy on preventative measures against harmful and damaging weather events in the United States, this analysis used data provided by the National Oceanic and Atmospheric Administration (NOAA) and produced two rankings, each listing the most dangerous and most damaging weather event types observed in the United States between 1996 and 2011 respectively. This analysis has found that excessive heat, floods, lightning and tornadoes rank as some of the weather event types with the most negative consequences on population health in the United States, whereas floods, hail, hurricanes and tornadoes rank as some of the weather event types with the most negative consequences on the economy of the United States