Analysis of Storm-induced Damage in US
Haoyu Qi
October 20, 2015
Synopsis
In this report, we analyze the population health and economic damages caused by storm in US. The data is provided by NOAA storm database, which 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 from 1950 till now. More specifically, we address the questions that which type of events causes the greatest human health and economic damage.
Data processing
First we read the .csv data file into a dataframe.
stormData <- read.csv("~/Notes/reproducibe/repdata-data-StormData.csv")Show the first several rows of the data.
head(stormData)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 0
## 2 TORNADO 0 0
## 3 TORNADO 0 0
## 4 TORNADO 0 0
## 5 TORNADO 0 0
## 6 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14.0 100 3 0 0
## 2 NA 0 2.0 150 2 0 0
## 3 NA 0 0.1 123 2 0 0
## 4 NA 0 0.0 100 2 0 0
## 5 NA 0 0.0 150 2 0 0
## 6 NA 0 1.5 177 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## 3 2 25.0 K 0
## 4 2 2.5 K 0
## 5 2 2.5 K 0
## 6 6 2.5 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2
## 3 3340 8742 0 0 3
## 4 3458 8626 0 0 4
## 5 3412 8642 0 0 5
## 6 3450 8748 0 0 6To manipulate the data, we need the dplyr package.
library(dplyr)- Q1
To study the harmfulness of human health, we first sum the number of injuries and fatalities into new column “HARM”. Then we create a subset of the full data only including the “EVTYPE” and “HARM”,
healthData <- stormData %>%
mutate(HARM = INJURIES + FATALITIES) %>%
select(EVTYPE, HARM)
head(healthData)## EVTYPE HARM
## 1 TORNADO 15
## 2 TORNADO 0
## 3 TORNADO 2
## 4 TORNADO 2
## 5 TORNADO 2
## 6 TORNADO 6as shown above.
To reflect the level of harmfulness, we need to sum the total number of injuries for each type of event.
healthSum <- healthData %>%
group_by(EVTYPE) %>%
summarise(tot_harm = sum(HARM, na.rm = TRUE)) %>%
arrange(desc(tot_harm))
head(healthSum)## Source: local data frame [6 x 2]
##
## EVTYPE tot_harm
## 1 TORNADO 96979
## 2 EXCESSIVE HEAT 8428
## 3 TSTM WIND 7461
## 4 FLOOD 7259
## 5 LIGHTNING 6046
## 6 HEAT 3037At the last step we rearrange the order according to the total number of harms. The other processes are similar to what we did for question 1.
- Q2
To study the economic loss, we need to sum both property damage and crop damage.
econData <- stormData %>%
mutate(DMG = PROPDMG + CROPDMG) %>%
select(EVTYPE, DMG)
econSum <- econData %>%
group_by(EVTYPE) %>%
summarise(tot_DMG = sum(DMG, na.rm = TRUE)) %>%
arrange(desc(tot_DMG))
head(econSum)## Source: local data frame [6 x 2]
##
## EVTYPE tot_DMG
## 1 TORNADO 3312276.7
## 2 FLASH FLOOD 1599325.1
## 3 TSTM WIND 1445168.2
## 4 HAIL 1268289.7
## 5 FLOOD 1067976.4
## 6 THUNDERSTORM WIND 943635.6Results
- Across the United States, which types of events are most harmful with respect to population health?
As we can see from dataframe healthSum, tornado, which causes 91346 injuries since 1950 (recorded), is most harmful with respect to human health. To make the results intuitively, we plot the total number of injuries for the top 5 types of events.
library(ggplot2)
ggplot(data = healthSum[1:5,], aes(x = EVTYPE, y = tot_harm)) +
geom_bar(stat = "identity", aes(fill = EVTYPE)) +
ylab("Total number of reported injuries and fatalities") +
xlab("Type of events")+
ggtitle("Top 5 types of events being harmful to human health")
As we can see, the number of reported injures caused by tornade is significantly higher than other type of events.
- Across the United States, which types of events have the greatest economic consequences?
As we can see from the dataframe econSum, again tornado causes the greateset economic loss since 1950. Again we plot the top 5 types of events ranked by the economic loss induced.
ggplot(data = econSum[1:5,], aes(x = EVTYPE, y = tot_DMG)) +
geom_bar(stat = "identity", aes(fill = EVTYPE)) +
ylab("Total amount of damage to properites and crops") +
xlab("Type of events")+
ggtitle("Top 5 types of events having the greatest economic consequences")
As we can see, compared to the human health damage, the differences between the tornade and other top types for economic damage are less significant.
Summary
In summary, across the US, tornado is thetype of events which is most harmful to population health and have greatest economic consequence.