Title: Weather Events - Storm - in the US
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
From the analysis on the NOAA storm database, we found that tornadoes and excessive heat are the 2 most dangerous weather events. We also learnt that flash floods and thunderstorm caused billions of dollars in property damages between 1950 and 2011. Lastly, we learnt that crop damages was mostly caused by drought, flood and hails.
Data Processing
The analysis was performed on the storm database provided in StormData.csv.bz2
We read and load the data into a data frame.
storm <- read.csv(bzfile("StormData.csv.bz2"))Next we do some data preprocessing to organise the event types into the proper form by grouping similar event types together.
# 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$EVTYPE <- event_typesEvents most harmful with respect to Population Health
We aggreate the number of casualties by the event type.
library(plyr)
casualties <- ddply(storm, .(EVTYPE), summarize,
fatalities = sum(FATALITIES),
injuries = sum(INJURIES))
# Find events that caused most death and injury
fatal_events <- head(casualties[order(casualties$fatalities, decreasing = T), ], 10)
injury_events <- head(casualties[order(casualties$injuries, decreasing = T), ], 10)Top 10 events that caused largest number of deaths are
fatal_events[, c("EVTYPE", "fatalities")]## EVTYPE fatalities
## 741 tornado 5633
## 116 excessive heat 1903
## 138 flash flood 978
## 240 heat 937
## 410 lightning 816
## 762 tstm wind 504
## 154 flood 470
## 515 rip current 368
## 314 high wind 248
## 19 avalanche 224Top 10 events that caused most number of injuries are
injury_events[, c("EVTYPE", "injuries")]## EVTYPE injuries
## 741 tornado 91346
## 762 tstm wind 6957
## 154 flood 6789
## 116 excessive heat 6525
## 410 lightning 5230
## 240 heat 2100
## 382 ice storm 1975
## 138 flash flood 1777
## 671 thunderstorm wind 1488
## 209 hail 1361Greatest economic consequences of weather events
We need to look at the property damage and crop damage data reportings. From the data, the property damage is represented with two fields, a number PROPDMG in dollars and the exponent PROPDMGEXP. Similarly, the crop damage is represented using two fields, CROPDMG and CROPDMGEXP. The first step in the analysis is to calculate the property and crop damage for each event.
exp_transform <- function(e) {
# h -> hundred, k -> thousand, m -> million, b -> billion
if (e %in% c('h', 'H'))
return(2)
else if (e %in% c('k', 'K'))
return(3)
else if (e %in% c('m', 'M'))
return(6)
else if (e %in% c('b', 'B'))
return(9)
else if (!is.na(as.numeric(e))) # if a digit
return(as.numeric(e))
else if (e %in% c('', '-', '?', '+'))
return(0)
else {
stop("Invalid exponent value.")
}
}prop_dmg_exp <- sapply(storm$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 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 6.834763e+12
## 697 thunderstorm winds 2.088094e+12
## 741 tornado 1.591385e+11
## 154 flood 1.446577e+11
## 366 hurricane typhoon 6.930584e+10
## 209 hail 4.573462e+10
## 585 storm surge 4.332354e+10
## 410 lightning 1.813012e+10
## 357 hurricane 1.186832e+10
## 755 tropical storm 7.703891e+09Similarly, the events that caused biggest crop damage are
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 3025956480
## 357 hurricane 2741910000
## 366 hurricane typhoon 2607872800
## 138 flash flood 1421317100
## 125 extreme cold 1312973000
## 185 frost freeze 1094186000Results
Health Impact
The following plot shows top dangerous weather event types.
library(ggplot2)
library(gridExtra)## 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)")
Conclusions
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 impact 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)")
Conclusion
The data shows that flash floods and thunderstorm winds cost the largest property damages among weather-related natural diseasters.
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.