Impacts of Hostile Weather Conditions on Public Health & Economy in US
Ashish Dalal
Sunday, September 14, 2014
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.
The analysis on the storm event database reveals that tornadoes are the most dangerous weather event to the population health. The second most dangerous event type is the excessive heat. The economic impact of weather events is also analyzed. Flash floods and thunderstorm winds is found to cause billions of dollars in property damages between 1950 and 2011. The largest crop damage is found to be caused by drought, followed by flood and hails.
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.
The first step is to read the data into a data frame.
library(R.utils)## Loading required package: R.oo
## Loading required package: R.methodsS3
## R.methodsS3 v1.6.1 (2014-01-04) successfully loaded. See ?R.methodsS3 for help.
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## cat, commandArgs, getOption, inherits, isOpen, parse, warningslibrary(gridExtra)## Loading required package: gridlibrary(lattice)
library(knitr)opts_chunk$set(eval = TRUE)Creating data directory if it does not exist already
if(!file.exists("data"))
{
dir.create("data")
}file URL and destination file
fileUrl <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
destfile <- "./data/repdata-data-StormData.csv.bz2"download the file and note the time
if(!file.exists("repdata-data-StormData.csv.bz2")){
download.file(fileUrl, destfile = destfile,method ="curl")
dateDownloaded <- date()
}Unzipping the file
if(!file.exists("repdata-data-StormData.csv")){
bunzip2("repdata-data-StormData.csv.bz2", "repdata-data-StormData.csv", overwrite = TRUE, remove = FALSE)
}Reading the file
dataset <- read.csv("repdata-data-StormData.csv")Before the analysis, the data needs some preprocessing. Event types don’t have a specific format. For instance, there are events with types Frost/Freeze, FROST/FREEZE which obviously refer to the same type of event.
number of unique event types
length(unique(dataset$EVTYPE))## [1] 985translate all letters to lowercase
event_types <- tolower(dataset$EVTYPE)replace all punct. characters with a space
event_types <- gsub("[[:blank:][:punct:]+]", " ", event_types)
length(unique(event_types))## [1] 874update the data frame
dataset$EVTYPE <- event_typesNo further data preprocessing was performed although the event type field can be processed further to merge event types such as tstm wind and thunderstorm wind. After the cleaning, as expected, the number of unique event types reduce significantly. For further analysis, the cleaned event types are used.
Ruthless Events apropos of Public Health
To find the event types that are most harmful to population health, the number of casualties are aggregated by the event type.
library(plyr)
casualties <- ddply(dataset, .(EVTYPE), summarize,
fatalities = sum(FATALITIES),
injuries = sum(INJURIES))Find events that caused most death and injury
fatal_events <- head(casualties[order(casualties$fatalities, decreasing = T), ], 10)
injury_events <- head(casualties[order(casualties$injuries, decreasing = T), ], 10)Top 10 events that caused largest number of deaths are
fatal_events[, c("EVTYPE", "fatalities")]## EVTYPE fatalities
## 741 tornado 5633
## 116 excessive heat 1903
## 138 flash flood 978
## 240 heat 937
## 410 lightning 816
## 762 tstm wind 504
## 154 flood 470
## 515 rip current 368
## 314 high wind 248
## 19 avalanche 224Top 10 events that caused most number of injuries are
injury_events[, c("EVTYPE", "injuries")]## EVTYPE injuries
## 741 tornado 91346
## 762 tstm wind 6957
## 154 flood 6789
## 116 excessive heat 6525
## 410 lightning 5230
## 240 heat 2100
## 382 ice storm 1975
## 138 flash flood 1777
## 671 thunderstorm wind 1488
## 209 hail 1361Economic Impacts of Catastrophic Weather Events
To analyze the impact of weather events on the economy, available property damage and crop damage reportings/estimates were used.
In the raw data, the property damage is represented with two fields, a number PROPDMG in dollars and the exponent PROPDMGEXP. Similarly, the crop damage is represented using two fields, CROPDMG and CROPDMGEXP. The first step in the analysis is to calculate the property and crop damage for each event. h -> hundred, k -> thousand, m -> million, b -> billion
exp_transform <- function(e) {
if (e %in% c('h', 'H'))
return(2)
else if (e %in% c('k', 'K'))
return(3)
else if (e %in% c('m', 'M'))
return(6)
else if (e %in% c('b', 'B'))
return(9)
else if (!is.na(as.numeric(e))) # if a digit
return(as.numeric(e))
else if (e %in% c('', '-', '?', '+'))
return(0)
else {
stop("Invalid exponent value.")
}
}
prop_dmg_exp <- sapply(dataset$PROPDMGEXP, FUN=exp_transform)
dataset$prop_dmg <- dataset$PROPDMG * (10 ** prop_dmg_exp)
crop_dmg_exp <- sapply(dataset$CROPDMGEXP, FUN=exp_transform)
dataset$crop_dmg <- dataset$CROPDMG * (10 ** crop_dmg_exp)Compute the economic loss by event type
library(plyr)
econ_loss <- ddply(dataset, .(EVTYPE), summarize,
prop_dmg = sum(prop_dmg),
crop_dmg = sum(crop_dmg))filter out events that caused no economic loss
econ_loss <- econ_loss[(econ_loss$prop_dmg > 0 | econ_loss$crop_dmg > 0), ]
prop_dmg_events <- head(econ_loss[order(econ_loss$prop_dmg, decreasing = T), ], 10)
crop_dmg_events <- head(econ_loss[order(econ_loss$crop_dmg, decreasing = T), ], 10)Top 10 events that caused most property damage (in dollars) are as follows:
prop_dmg_events[, c("EVTYPE", "prop_dmg")]## EVTYPE prop_dmg
## 138 flash flood 6.820e+13
## 697 thunderstorm winds 2.087e+13
## 741 tornado 1.079e+12
## 209 hail 3.158e+11
## 410 lightning 1.729e+11
## 154 flood 1.447e+11
## 366 hurricane typhoon 6.931e+10
## 166 flooding 5.921e+10
## 585 storm surge 4.332e+10
## 270 heavy snow 1.793e+10Similarly, the events that caused biggest crop damage are
crop_dmg_events[, c("EVTYPE", "crop_dmg")]## EVTYPE crop_dmg
## 84 drought 1.397e+10
## 154 flood 5.662e+09
## 519 river flood 5.029e+09
## 382 ice storm 5.022e+09
## 209 hail 3.026e+09
## 357 hurricane 2.742e+09
## 366 hurricane typhoon 2.608e+09
## 138 flash flood 1.421e+09
## 125 extreme cold 1.313e+09
## 185 frost freeze 1.094e+09RESULTS
Health Impacts
The following plot shows top dangerous weather event types.
library(ggplot2)
library(gridExtra)Set the levels in order
p1 <- ggplot(data=fatal_events,
aes(x=reorder(EVTYPE, fatalities), y=fatalities, fill=fatalities)) +
geom_bar(stat="identity") +
coord_flip() +
ylab("Total number of fatalities") +
xlab("Event type") +
theme(legend.position="none")
p2 <- ggplot(data=injury_events,
aes(x=reorder(EVTYPE, injuries), y=injuries, fill=injuries)) +
geom_bar(stat="identity") +
coord_flip() +
ylab("Total number of injuries") +
xlab("Event type") +
theme(legend.position="none")
grid.arrange(p1, p2, main="Top deadly weather events in the US (1950-2011)")
Tornadoes cause most number of deaths and injuries among all event types. There are more than 5,000 deaths and more than 10,000 injuries in the last 60 years in US, due to tornadoes. The other event types that are most dangerous with respect to population health are excessive heat and flash floods.
Economic Impacts
The following plot shows the most severe weather event types with respect to economic cost that they have costed since 1950s.
library(ggplot2)
library(gridExtra)Set the levels in order
p1 <- ggplot(data=prop_dmg_events,
aes(x=reorder(EVTYPE, prop_dmg), y=log10(prop_dmg), fill=prop_dmg )) +
geom_bar(stat="identity") +
coord_flip() +
xlab("Event type") +
ylab("Property damage in dollars (log-scale)") +
theme(legend.position="none")
p2 <- ggplot(data=crop_dmg_events,
aes(x=reorder(EVTYPE, crop_dmg), y=crop_dmg, fill=crop_dmg)) +
geom_bar(stat="identity") +
coord_flip() +
xlab("Event type") +
ylab("Crop damage in dollars") +
theme(legend.position="none")
grid.arrange(p1, p2, main="Weather costs to the US economy (1950-2011)")
Property damages are given in logarithmic scale due to large range of values. The data shows that flash floods and thunderstorm winds cost the largest property damages among weather-related natural diseasters. Note that, due to untidy nature of the available data, type flood and flash flood are separate values and should be merged for more accurate data-driven conclusions.
The most severe weather event in terms of crop damage is the drought. In the last half century, the drought has caused more than 10 billion dollars damage. Other severe crop-damage-causing event types are floods and hails.