Impact Of Natural Disasters On The Economy And The Public Health Of The United States

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.

Synopsis

This report consists in analyzing the NOAA storm database containing data on extreme climate events. This data was collected during the period from 1950 through 2011. The purpose of this analysis is to answer the following two questions:
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?

I used the weather events specified in the documentation (paragraphs 7.1 - 7.48).
Main conclusions of the study:
1. Tornado is the most hazordous climate event with more than 5600 deaths and 91400 injuries.
2. Floods have caused the most significant economic damage - more than 157 billion USD.

Basic Settings

echo = TRUE           # Always make code visible
options(scipen = 1)   # Turn off scientific notations for numbers
library(grid)
library(ggplot2)
library(plyr)
require(gridExtra)
## Loading required package: gridExtra

Data Processing

The analysis was performed on Storm Events Database, provided by National Climatic Data Center. The data is from a comma-separated-value file available here.
There is also some documentation of the data available here.

First, we download the data file and unzip it.

setwd("E:/JHU Data Science/rfiles/course 5/Assignment 2/")
if (!"StormData.csv.bz2" %in% dir("./")) {
    print("Downloading File.....")
    download.file("http://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", destfile = "StormData.csv.bz2")
}

Then, we read the generated csv file. If the data already exists in the working environment, we do not need to load it again. Otherwise, we read the csv file.

if (!"storm" %in% ls()) {
    storm <- read.csv(bzfile("StormData.csv.bz2"), sep = ",", header = TRUE, stringsAsFactors = FALSE)
}
dim(storm)
## [1] 902297     37

Extract data corresponding to the 48 events as described in the documentation paragraphs 7.1 through 7.48

  1. Vector of 48 events as defined in the documentation:
events <- c("Astronomical Low Tide", "Avalanche", "Blizzard", "Coastal Flood", "Cold/Wind Chill", "Debris Flow", "Dense Fog", "Dense Smoke", "Drought", "Dust Devil", "Dust Storm", "Excessive Heat", "Extreme cold/Wind Chill", "Flash Flood", "Flood", "Freezing", "Frost/Freeze", "Funnel Cloud", "Hail", "Heat", "Heavy Rain", "Heavy Snow", "High Surf", "High Wind", "Hurricane/Typhoon", "Ice Storm", "Lakeshore Flood", "Lake-Effect Snow", "Lightning", "Marine Hail", "Marine High Wind", "Marine Strong Wind", "Marine Thunderstorm Wind", "Rip Current", "Seiche", "Sleet", "Storm Tide", "Strong Wind", "Thunderstorm Wind", "Tornado", "Tropical Depression", "Tropical Storm", "Tsunami", "Volcanic Ash", "Waterspout", "Wildfire", "Winter Storm", "Winter Weather")
  1. Some events are combined events separated with a slash (e.g ‘Hurricane/Typhoon’). I will use regular expressions to extract either a combined event (Hurricane/Typhoon) or any part of it (Hurricane or Typhoon)
events_regex <- c("Astronomical Low Tide|Low Tide", "Avalanche", "Blizzard", "Coastal Flood", "Cold/Wind Chill", "Debris Flow", "Dense Fog", "Dense Smoke", "Drought", "Dust Devil", "Dust Storm", "Excessive Heat", "Extreme cold/Wind Chill|Extreme Cold|Wind Chill", "Flash Flood", "Flood", "Freezing", "Frost/Freeze|Frost|Freeze", "Funnel Cloud", "Hail", "Heat", "Heavy Rain", "Heavy Snow", "High Surf", "High Wind", "Hurricane/Typhoon|Hurricane|Typhoon", "Ice Storm", "Lakeshore Flood", "Lake-Effect Snow", "Lightning", "Marine Hail", "Marine High Wind", "Marine Strong Wind", "Marine Thunderstorm Wind|Marine tstm Wind", "Rip Current", "Seiche", "Sleet", "Storm Tide", "Strong Wind", "Thunderstorm Wind|tstm wind", "Tornado", "Tropical Depression", "Tropical Storm", "Tsunami", "Volcanic Ash", "Waterspout", "Wildfire", "Winter Storm", "Winter Weather")
  1. The next step is to extract rows corresponding to the event from the documentation, I will also choose the columns which are relevant to our analysis:
  • EVTYPE -> Type of event
  • FATALITIES -> Number of fatalities
  • INJURIES -> Number of injuries
  • PROPDMG -> Amount of property damage in orders of magnitude
  • PROPDMGEXP -> Order of magnitude for property damage (e.g. K for thousands)
  • CROPDMG -> Amount of crop damage in orders of magnitude
  • PROPDMGEXP -> Order of magnitude for crop damage (e.g. M for millions)
options(scipen = 999)  # force fixed notation of numbers instead of scientific
cleandata <- data.frame(EVTYPE = character(0), FATALITIES = numeric(0), INJURIES = numeric(0), PROPDMG = numeric(0), PROPDMGEXP = character(0), CROPDMG = numeric(0), CROPDMGEXP = character(0))  
for (i in 1:length(events)) {
    rows <- storm[grep(events_regex[i], ignore.case = TRUE, storm$EVTYPE), ]
    rows <- rows[, c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")]
    CLEANNAME <- c(rep(events[i], nrow(rows)))
    rows <- cbind(rows, CLEANNAME)
    cleandata <- rbind(cleandata, rows)
}
  1. Take into account the order of magnitude of property and crop damage (H = hundreds, K = thousands, M = millions, B= billions)
# convert letter exponents to integers
cleandata[(cleandata$PROPDMGEXP == "K" | cleandata$PROPDMGEXP == "k"), ]$PROPDMGEXP <- 3
cleandata[(cleandata$PROPDMGEXP == "M" | cleandata$PROPDMGEXP == "m"), ]$PROPDMGEXP <- 6
cleandata[(cleandata$PROPDMGEXP == "B" | cleandata$PROPDMGEXP == "b"), ]$PROPDMGEXP <- 9
cleandata[(cleandata$CROPDMGEXP == "K" | cleandata$CROPDMGEXP == "k"), ]$CROPDMGEXP <- 3
cleandata[(cleandata$CROPDMGEXP == "M" | cleandata$CROPDMGEXP == "m"), ]$CROPDMGEXP <- 6
cleandata[(cleandata$CROPDMGEXP == "B" | cleandata$CROPDMGEXP == "b"), ]$CROPDMGEXP <- 9
  1. Compute combined economic damage (property damage + crops damage)
# multiply property and crops damage by 10 raised to the power of the exponent
suppressWarnings(cleandata$PROPDMG <- cleandata$PROPDMG * 10^as.numeric(cleandata$PROPDMGEXP))  
suppressWarnings(cleandata$CROPDMG <- cleandata$CROPDMG * 10^as.numeric(cleandata$CROPDMGEXP))  
# compute combined economic damage (property damage + crops damage)
suppressWarnings(TOTECODMG <- cleandata$PROPDMG + cleandata$CROPDMG)
cleandata <- cbind(cleandata, TOTECODMG)
# delete 'PROPDMGEXP' and 'CROPDMGEXP'columns which have become unnecessary after conversion
cleandata <- cleandata[, c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "CROPDMG", "CLEANNAME", "TOTECODMG")]

At this stage clean data is ready for plotting graphs.

Results

Question 01 : Across the United States, which types of events are most harmful with respect to population health?

Fatalities and Injuries

As for the impact on public health, we have got two sorted lists of severe weather events below by the number of people badly affected.

  • Aggregate Data for Fatalities
fatalities <- aggregate(FATALITIES ~ CLEANNAME, data = cleandata, FUN = sum)
fatalities <- fatalities[order(fatalities$FATALITIES, decreasing = TRUE), ]
# 10 most harmful causes of fatalities
MaxFatalities <- fatalities[1:10, ]
print(MaxFatalities)  
##                  CLEANNAME FATALITIES
## 38                 Tornado       5661
## 19                    Heat       3138
## 11          Excessive Heat       1922
## 14                   Flood       1525
## 13             Flash Flood       1035
## 28               Lightning        817
## 37       Thunderstorm Wind        753
## 33             Rip Current        577
## 12 Extreme cold/Wind Chill        382
## 23               High Wind        299
  • Aggregate Data for Injuries
injuries <- aggregate(INJURIES ~ CLEANNAME, data = cleandata, FUN = sum)
injuries <- injuries[order(injuries$INJURIES, decreasing = TRUE), ]
# 10 most harmful causes of injuries
MaxInjuries <- injuries[1:10, ]
print(MaxInjuries)
##            CLEANNAME INJURIES
## 38           Tornado    91407
## 37 Thunderstorm Wind     9493
## 19              Heat     9224
## 14             Flood     8604
## 11    Excessive Heat     6525
## 28         Lightning     5232
## 25         Ice Storm     1992
## 13       Flash Flood     1802
## 23         High Wind     1523
## 18              Hail     1467

And the following is a pair of graphs of Total Fatalities and Total Injuries caused by these Severe Weather Events.

par(mfrow = c(1, 2), mar = c(15, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(MaxFatalities$FATALITIES, las = 3, names.arg = MaxFatalities$CLEANNAME, main = "Weather Events With\n The Top 10 Highest Fatalities", ylab = "Number of Fatalities", col = "grey")
barplot(MaxInjuries$INJURIES, las = 3, names.arg = MaxInjuries$CLEANNAME, main = "Weather Events With\n The Top 10 Highest Injuries", ylab = "Number of Injuries", col = "grey")

Based on the above histograms, we find that Tornado and Heat had caused most fatalities and Tornado had caused most injuries in the United States from 1995 to 2011.

Note: I decided not to compute the total damage consisting of fatalities and injuries (fatalities + injuries) since they have a different order of magnitude (a damage related to 1 death is far greater than a damage related to a light injury, for example). Throughout this report, I have always presented the data relating to fatalities and injuries separately.

Question 02 : Across the United States, which types of events have the greatest economic consequences?

Property and Crops combined Economic Damage

As for the impact on economy, we have got two sorted lists below by the amount of money cost by damages.

  • Aggregate Data for Property Damage.
propdmg <- aggregate(PROPDMG ~ CLEANNAME, data = cleandata, FUN = sum)
propdmg <- propdmg[order(propdmg$PROPDMG, decreasing = TRUE), ]
# 5 most harmful causes of injuries
propdmgMax <- propdmg[1:10, ]
print(propdmgMax)
##            CLEANNAME      PROPDMG
## 14             Flood 168212215589
## 24 Hurricane/Typhoon  85356410010
## 38           Tornado  58603317864
## 18              Hail  17622990956
## 13       Flash Flood  17588791879
## 37 Thunderstorm Wind  11575228673
## 40    Tropical Storm   7714390550
## 45      Winter Storm   6749997251
## 23         High Wind   6166300000
## 44          Wildfire   4865614000
  • Aggregate Data for Crop Damage
cropdmg <- aggregate(CROPDMG ~ CLEANNAME, data = cleandata, FUN = sum)
cropdmg <- cropdmg[order(cropdmg$CROPDMG, decreasing = TRUE), ]
# 5 most harmful causes of injuries
cropdmgMax <- cropdmg[1:10, ]
print(cropdmgMax)
##                  CLEANNAME     CROPDMG
## 8                  Drought 13972621780
## 14                   Flood 12380109100
## 24       Hurricane/Typhoon  5516117800
## 25               Ice Storm  5022113500
## 18                    Hail  3114212870
## 16            Frost/Freeze  1997061000
## 13             Flash Flood  1532197150
## 12 Extreme cold/Wind Chill  1313623000
## 37       Thunderstorm Wind  1255947980
## 19                    Heat   904469280
  • Aggregate Total Economic Damage
ecodmg <- aggregate(TOTECODMG ~ CLEANNAME, data = cleandata, FUN = sum)
ecodmg <- ecodmg[order(ecodmg$TOTECODMG, decreasing = TRUE), ]
# 5 most harmful causes of property damage
ecodmgMax <- ecodmg[1:10, ]
print(ecodmgMax)
##            CLEANNAME    TOTECODMG
## 14             Flood 157764680787
## 24 Hurricane/Typhoon  44330000800
## 38           Tornado  18172843863
## 18              Hail  11681050140
## 13       Flash Flood   9224527227
## 37 Thunderstorm Wind   7098296330
## 25         Ice Storm   5925150850
## 44          Wildfire   3685468370
## 23         High Wind   3472442200
## 8            Drought   1886667000

And the following are graphs of Total Property Damages, Total Crop Damages and Total Economic Damages caused by these Severe Weather Events.

par(mfrow = c(1, 3), mar = c(15, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(propdmgMax$PROPDMG/(10^9), las = 3, names.arg = propdmgMax$CLEANNAME, main = "Top 10 Events with\n Greatest Property Damages", ylab = "Cost of damages ($ billions)", col = "grey")
barplot(cropdmgMax$CROPDMG/(10^9), las = 3, names.arg = cropdmgMax$CLEANNAME, main = "Top 10 Events With\n Greatest Crop Damages", ylab = "Cost of damages ($ billions)", col = "grey")
barplot(ecodmgMax$TOTECODMG/(10^9), las = 3, names.arg = ecodmgMax$CLEANNAME, main = "Top 10 Events With\n Greatest Economic Damages", ylab = "Cost of damages ($ billions)", col = "grey")

The weather events have the Greatest Economic Consequences are: Flood, Drought, Tornado and Typhoon.
Across the United States, Flood, Tornado and Typhoon have caused the Greatest Damage to Properties.
Drought and Flood had been the causes for the Greatest Damage to Crops.

Conclusion

From these data, we found that Excessive Heat and Tornado are most harmful with respect to Population Health, while Flood, Drought and Hurricane/Typhoon have the greatest Economic Consequences.