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 document is a very preliminary study on the consequences of storms and other extreme natural events that shows the devastating effects, both on population health and on economy, that these events have.

The research conducted is completely reproducible because the data and the code are provided to the scientific community.

Data Processing

The data for the analysis can be downloaded from here.

library(dplyr)

## Warning: package 'dplyr' was built under R version 3.5.3

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(RColorBrewer)

# Disable scientific notation
options(scipen = 999)

# Data file name
datafilename  <- "./repdata_data_StormData.csv.bz2"
datafileurl <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"

# 1. Read dataset
if(!file.exists(datafilename)) {
    download.file(url=datafileurl, destfile = datafilename)
}
df <- read.csv(datafilename)

# Converts values in the PROPEXP and CROPEXP columns into the corresponding power of 10. 
# This indicates the multiplier of the values of the PROPDMG and CROPDMG columns.
# Symbols are mapped to values as follows:
#   h or H -> 100
#   k or K -> 1000
#   m or M -> 1000000
#   b or B -> 1000000000
#   from 1 to 8 -> 10^x
#   0, +, -, ? -> 1
convertExp <- function(x) {
    if(x[[1]] == "") 0
    else if(x == "h" | x == "H") 100
    else if(x == "k" | x == "K") 1000
    else if(x == "m" | x == "M") 1000000
    else if(x == "b" | x == "B") 1000000000
    else if(x == "0") 0
    else if(x == "1") 10
    else if(x == "2") 100
    else if(x == "3") 1000
    else if(x == "4") 10000
    else if(x == "5") 100000
    else if(x == "6") 1000000
    else if(x == "7") 10000000
    else if(x == "8") 100000000
    else 1
}

Results

In the following plot the number of casualties and the number of injuries are taken as a measure of the impact on population health.

par(mfrow=c(2,1), mar=c(2,3,2,1))
pal <- colorRampPalette(brewer.pal(8,"Set2"))
deaths <- df %>% group_by(EVTYPE) %>% summarise(FATALITIES=sum(FATALITIES)) %>% arrange(desc(FATALITIES)) %>% head(8)
barplot(deaths$FATALITIES, names.arg = deaths$EVTYPE, axisnames = T, col = pal(8), 
        main="Fatalities per event type", xlab="Event type")

pal <- colorRampPalette(brewer.pal(8,"Set1"))
injuries <- df %>% group_by(EVTYPE) %>% summarise(INJURIES=sum(INJURIES)) %>% arrange(desc(INJURIES)) %>% head(8)
barplot(injuries$INJURIES, names.arg = injuries$EVTYPE, axisnames = T, col = pal(8), 
        main="Injuries per event type", xlab="Event type")

Across the United States, the following events (as indicated in the EVTYPE variable) are most harmful with respect to population health:

Fatalities

head(deaths,8)

## # A tibble: 8 x 2
##   EVTYPE         FATALITIES
##   <fct>               <dbl>
## 1 TORNADO              5633
## 2 EXCESSIVE HEAT       1903
## 3 FLASH FLOOD           978
## 4 HEAT                  937
## 5 LIGHTNING             816
## 6 TSTM WIND             504
## 7 FLOOD                 470
## 8 RIP CURRENT           368

Injuries

head(injuries,8)

## # A tibble: 8 x 2
##   EVTYPE         INJURIES
##   <fct>             <dbl>
## 1 TORNADO           91346
## 2 TSTM WIND          6957
## 3 FLOOD              6789
## 4 EXCESSIVE HEAT     6525
## 5 LIGHTNING          5230
## 6 HEAT               2100
## 7 ICE STORM          1975
## 8 FLASH FLOOD        1777

Across the United States, which types of events have the greatest economic consequences? The economic consequences are calculated as the sum of the property damage and the crop damage.

# Calculate multipliers of the PROPDMG and CROPDMG values
propertyDamageMultipliers <- sapply(df$PROPDMGEXP, FUN = convertExp)
cropDamageMultipliers     <- sapply(df$CROPDMGEXP, FUN = convertExp)

# Calculate property and crop damage
propertyDamage <- df$PROPDMG * propertyDamageMultipliers
cropDamage     <- df$CROPDMG * cropDamageMultipliers

totalPropertyDamage <- round((sum(propertyDamage))/10^9, 2)
totalCropDamage     <- round((sum(cropDamage))/10^9, 2)
totalDamage         <- totalPropertyDamage + totalCropDamage

The following table shows the economic damage from 1950 to 2011 in the US.

Damage	Amount (billions of $)
Property	428.22
Crop	49.1
Total	477.32

The results suggest that it is advisable to pursue political decisions targeted at

prevention of natural disaster (for instance by fighting climate change)
reduction of the effects on population health and economic loss

Consequences of storms and other extreme natural events on population and economy

Claudio L.

5/8/2019

Synopsis

Data Processing

Results

Fatalities

Injuries