Thetazero Pubs
lgyhz123

Research on the impact of severe weather events on health and economic

Synopsis

In this research, we use data from the National Weather Service of National Oceanic & Atmospheric Administration. The data collects severe weather events details from year 1950 to 2011. In order to analyze the impact of each type of weather event, I aggregated the the fatalities, injueries, property damage and corp damage by event types. The total number of fatalities and injueries represent the population health impact of each events. And the total bumber of property and corp damage represent the economic consequence. Through this process, I can quantify the impact of different kinds of events in terms of health and economy.


Data Processing

First, I adjusted the number of fatalities, injueries, property damage and corp damage by taking the magnitute of the number in to consideration. Then, in order to better show the impact of each type of weather event, I aggreated the number by each weather event type.

data<-read.csv("repdata-data-StormData.csv") 



data$PROPDMGEXP <- gsub("B", "1000000000", data$PROPDMGEXP)
data$PROPDMGEXP <- gsub("b", "1000000000", data$PROPDMGEXP)
data$PROPDMGEXP <- gsub("M", "1000000", data$PROPDMGEXP)
data$PROPDMGEXP <- gsub("m", "1000000", data$PROPDMGEXP)
data$PROPDMGEXP <- gsub("K", "1000", data$PROPDMGEXP)
data$PROPDMGEXP <- gsub("k", "1000", data$PROPDMGEXP)
data$PROPDMGEXP <- as.numeric(data$PROPDMGEXP )
## Warning: NAs introduced by coercion
data$PROPDMGEXP[is.na(data$PROPDMGEXP)] <- 1
data$PROPDMG <- data$PROPDMG*data$PROPDMGEXP

data$CROPDMGEXP <- gsub("B", "1000000000", data$CROPDMGEXP)
data$CROPDMGEXP <- gsub("M", "1000000", data$CROPDMGEXP)
data$CROPDMGEXP <- gsub("m", "1000000", data$CROPDMGEXP)
data$CROPDMGEXP <- gsub("K", "1000", data$CROPDMGEXP)
data$CROPDMGEXP <- as.numeric(data$CROPDMGEXP)
## Warning: NAs introduced by coercion
data$CROPDMGEXP[is.na(data$CROPDMGEXP)] <- 1
data$CROPDMG <- data$CROPDMG*data$CROPDMGEXP



Data_health<- aggregate( INJURIES + FATALITIES ~ EVTYPE, data=data,mean)



Data_economic<- aggregate( PROPDMG + CROPDMG ~ EVTYPE, data=data,mean)

Result

As for the health impact by severe weather events, here is summerized information from our dataset. The fowllowing chat shows the impact to population health of each type of weather event. We can see there are some events that have significantly more impact on population health.

library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.2.3
colnames(Data_health)<-c("EVTYPE","Count")

healthplot <- ggplot(data=Data_health[order(-Data_health[,2]),][1:5,], aes(x=EVTYPE, y=Count)) + geom_bar(stat = "identity",width = .5)+coord_flip()

healthplot

Data_health[order(-Data_health[,2]),][1,]
##        EVTYPE Count
## 277 Heat Wave    70

In particular, Heat Wave is the most severe weather event that cause 70 injuries and fatalies in average.

For the economic impact by severe weather events, here are summerized information from our dataset. A chat is also created to shown the impact of weather events on economy.


colnames(Data_economic)<-c("EVTYPE","Value")

economicplot <- ggplot(data=Data_economic[order(-Data_economic[,2]),][1:5,], aes(x=EVTYPE, y=Value)) + geom_bar(stat = "identity",width = .5) +coord_flip()

economicplot

Data_economic[order(-Data_economic[,2]),][1,]
##                         EVTYPE      Value
## 842 TORNADOES, TSTM WIND, HAIL 1602500000

In particular, TORNADOES, TSTM WIND, HAIL is the most severe weather event that cause $1602500000 damage in average.

Copyright © 2016 thetazero.com All Rights Reserved. Privacy Policy