Health and Economic Impacts of Weather in USA
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
The analysis on the storm event database revealed 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 was also analyzed. Flash floods and thunderstorm winds caused billions of dollars in property damages between 1950 and 2011. The largest crop damage caused by drought, followed by flood and hails.
Data Processing
The Analysis is provided on Storm event Database provided by National Climatic Data Center.The Data is provided in the form of comma seperated values (CSV). The first step is to read the data into a data frame.
storm <- read.csv(bzfile("repdata-data-StormData.csv.bz2"))Before the analysis, the data need some preprocessing. Event types don’t have a specific format. For instance, there are events with types Frost/Freeze, FROST/FREEZE and FROST\FREEZE which obviously refer to the same type of event.
# number of unique event types
length(unique(storm$EVTYPE))## [1] 985# translate all letters to lowercase
event_types <- tolower(storm$EVTYPE)
# replace all punct. characters with a space
event_types <- gsub("[[:blank:][:punct:]+]", " ", event_types)
length(unique(event_types))## [1] 874# update the data frame
storm$EVTYPE <- event_typesDangerous Events with respect to Population Health
To find the most fatal of events , the number of casualities are aggregated to a larger number.
library(plyr)
casualties <- ddply(storm, .(EVTYPE), summarize,
fatalities = sum(FATALITIES),
injuries = sum(INJURIES))
# Find events that caused most death and injury
fatal_events <- head(casualties[order(casualties$fatalities, decreasing = T), ], 10)
injury_events <- head(casualties[order(casualties$injuries, decreasing = T), ], 10)So The top ten of such events most fatale are ::
fatal_events[, c("EVTYPE", "fatalities")]## EVTYPE fatalities
## 741 tornado 5633
## 116 excessive heat 1903
## 138 flash flood 978
## 240 heat 937
## 410 lightning 816
## 762 tstm wind 504
## 154 flood 470
## 515 rip current 368
## 314 high wind 248
## 19 avalanche 224And The top 10 of such events which cause the most amount of injuries are ::
injury_events[, c("EVTYPE", "injuries")]## EVTYPE injuries
## 741 tornado 91346
## 762 tstm wind 6957
## 154 flood 6789
## 116 excessive heat 6525
## 410 lightning 5230
## 240 heat 2100
## 382 ice storm 1975
## 138 flash flood 1777
## 671 thunderstorm wind 1488
## 209 hail 1361Effects of Weather on Economy
Now we will try to analyse the effects of weather on Economy by submitting the set of events with the most Economical Impacts
exp_transform <- function(e) {
# h -> hundred, k -> thousand, m -> million, b -> billion
if (e %in% c('h', 'H'))
return(2)
else if (e %in% c('k', 'K'))
return(3)
else if (e %in% c('m', 'M'))
return(6)
else if (e %in% c('b', 'B'))
return(9)
else if (!is.na(as.numeric(e))) # if a digit
return(as.numeric(e))
else if (e %in% c('', '-', '?', '+'))
return(0)
else {
stop("Invalid exponent value.")
}
}prop_dmg_exp <- sapply(storm$PROPDMGEXP, FUN=exp_transform)
storm$prop_dmg <- storm$PROPDMG * (10 ** prop_dmg_exp)
crop_dmg_exp <- sapply(storm$CROPDMGEXP, FUN=exp_transform)
storm$crop_dmg <- storm$CROPDMG * (10 ** crop_dmg_exp)# Compute the economic loss by event type
library(plyr)
econ_loss <- ddply(storm, .(EVTYPE), summarize,
prop_dmg = sum(prop_dmg),
crop_dmg = sum(crop_dmg))
# filter out events that caused no economic loss
econ_loss <- econ_loss[(econ_loss$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 Ten events the most property damage (in term of Dollars) ::
prop_dmg_events[, c("EVTYPE", "prop_dmg")]## EVTYPE prop_dmg
## 138 flash flood 6.820237e+13
## 697 thunderstorm winds 2.086532e+13
## 741 tornado 1.078951e+12
## 209 hail 3.157558e+11
## 410 lightning 1.729433e+11
## 154 flood 1.446577e+11
## 366 hurricane typhoon 6.930584e+10
## 166 flooding 5.920825e+10
## 585 storm surge 4.332354e+10
## 270 heavy snow 1.793259e+10The most damage to the crops ::
crop_dmg_events[, c("EVTYPE", "crop_dmg")]## EVTYPE crop_dmg
## 84 drought 13972566000
## 154 flood 5661968450
## 519 river flood 5029459000
## 382 ice storm 5022113500
## 209 hail 3025974480
## 357 hurricane 2741910000
## 366 hurricane typhoon 2607872800
## 138 flash flood 1421317100
## 125 extreme cold 1312973000
## 185 frost freeze 1094186000Results
Health impacts of weather events
The following plots the top ten fatal weather events
library(ggplot2)
library(gridExtra)## Warning: package 'gridExtra' was built under R version 3.1.3## Loading required package: grid# 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 of weather events
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.