Exploring the U.S. NOAA storm database
Shambhavi Malik
05/08/2020
Exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database for its Health and Economic Impacts
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 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.
The analysis of the data shows that tornadoes, by far, have the greatest health impact as measured by the number of injuries and fatalities The analysis also shows that floods cause the greatest economic impact as measured by property damage and crop damage.
Data Processing
The data for this assignment come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. You can download the file from the course web site:
There is also some documentation of the database available. Here you will find how some of the variables are constructed/defined.
- National Weather Service Storm Data Documentation
- National Climatic Data Center Storm Events FAQ
The events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete.
The data was downloaded via the above link and was then loaded to our R working directory. Then we unzip the contents of the file and load it into a new variable storm_data using the read.csv command. If object storm_data is already loaded, use that cached object insted of loading it each time the Rmd file is knitted.
if(!exists("storm_data")) {
storm_data <- read.csv(bzfile("repdata_data_StormData.csv.bz2"),header = TRUE)
}Now that the data is stored we analyze its contents.
dim(storm_data)## [1] 902297 37str(storm_data)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : chr "4/18/1950 0:00:00" "4/18/1950 0:00:00" "2/20/1951 0:00:00" "6/8/1951 0:00:00" ...
## $ BGN_TIME : chr "0130" "0145" "1600" "0900" ...
## $ TIME_ZONE : chr "CST" "CST" "CST" "CST" ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: chr "MOBILE" "BALDWIN" "FAYETTE" "MADISON" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : chr "" "" "" "" ...
## $ BGN_LOCATI: chr "" "" "" "" ...
## $ END_DATE : chr "" "" "" "" ...
## $ END_TIME : chr "" "" "" "" ...
## $ COUNTY_END: num 0 0 0 0 0 0 0 0 0 0 ...
## $ COUNTYENDN: logi NA NA NA NA NA NA ...
## $ END_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ END_AZI : chr "" "" "" "" ...
## $ END_LOCATI: chr "" "" "" "" ...
## $ LENGTH : num 14 2 0.1 0 0 1.5 1.5 0 3.3 2.3 ...
## $ WIDTH : num 100 150 123 100 150 177 33 33 100 100 ...
## $ F : int 3 2 2 2 2 2 2 1 3 3 ...
## $ MAG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ FATALITIES: num 0 0 0 0 0 0 0 0 1 0 ...
## $ INJURIES : num 15 0 2 2 2 6 1 0 14 0 ...
## $ PROPDMG : num 25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...
## $ PROPDMGEXP: chr "K" "K" "K" "K" ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr "" "" "" "" ...
## $ WFO : chr "" "" "" "" ...
## $ STATEOFFIC: chr "" "" "" "" ...
## $ ZONENAMES : chr "" "" "" "" ...
## $ LATITUDE : num 3040 3042 3340 3458 3412 ...
## $ LONGITUDE : num 8812 8755 8742 8626 8642 ...
## $ LATITUDE_E: num 3051 0 0 0 0 ...
## $ LONGITUDE_: num 8806 0 0 0 0 ...
## $ REMARKS : chr "" "" "" "" ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...Relevant variables
From a list of variables in storm_data, these are columns of interest:
- Health variables:
- FATALITIES: approx. number of deaths
- INJURIES: approx. number of injuries
- Economic variables:
- PROPDMG: approx. property damags
- PROPDMGEXP: the units for property damage value
- CROPDMG: approx. crop damages
- CROPDMGEXP: the units for crop damage value
- Events target variable:
- EVTYPE: weather event (Tornados, Wind, Snow, Flood, etc..)
Health Impact
To evaluate the health impact, the total fatalities and the total injuries for each event type (EVTYPE) are calculated.
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionstorm_data.fatalities <- storm_data %>% select(EVTYPE, FATALITIES) %>% group_by(EVTYPE) %>% summarise(total.fatalities = sum(FATALITIES)) %>% arrange(-total.fatalities)## `summarise()` ungrouping output (override with `.groups` argument)head(storm_data.fatalities, 10) ## # A tibble: 10 x 2
## EVTYPE total.fatalities
## <chr> <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
## 9 HIGH WIND 248
## 10 AVALANCHE 224storm_data.injuries <- storm_data %>% select(EVTYPE, INJURIES) %>% group_by(EVTYPE) %>% summarise(total.injuries = sum(INJURIES)) %>% arrange(-total.injuries)## `summarise()` ungrouping output (override with `.groups` argument)head(storm_data.injuries, 10)## # A tibble: 10 x 2
## EVTYPE total.injuries
## <chr> <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
## 9 THUNDERSTORM WIND 1488
## 10 HAIL 1361Economic Impact
The data provides two types of economic impact, namely property damage (PROPDMG) and crop damage (CROPDMG). The actual damage in $USD is indicated by PROPDMGEXP and CROPDMGEXP parameters. According to this link, the index in the PROPDMGEXP and CROPDMGEXP can be interpreted as the following:-
- H, h -> hundreds = x100
- K, K -> kilos = x1,000
- M, m -> millions = x1,000,000
- B,b -> billions = x1,000,000,000
- (+) -> x1
- (-) -> x0
- (?) -> x0
- blank -> x0
storm_data.damage <- storm_data %>% select(EVTYPE,PROPDMG,PROPDMGEXP,CROPDMG,CROPDMGEXP)
Symbol <- sort(unique(as.character(storm_data.damage$PROPDMGEXP)))
Multiplier <- c(0,0,0,1,10,10,10,10,10,10,10,10,10,10^9,10^2,10^2,10^3,10^6,10^6)
convert.Multiplier <- data.frame(Symbol, Multiplier)
storm_data.damage$Prop.Multiplier <- convert.Multiplier$Multiplier[match(storm_data.damage$PROPDMGEXP, convert.Multiplier$Symbol)]
storm_data.damage$Crop.Multiplier <- convert.Multiplier$Multiplier[match(storm_data.damage$CROPDMGEXP, convert.Multiplier$Symbol)]
storm_data.damage <- storm_data.damage %>% mutate(PROPDMG = PROPDMG*Prop.Multiplier) %>% mutate(CROPDMG = CROPDMG*Crop.Multiplier) %>% mutate(TOTAL.DMG = PROPDMG+CROPDMG)
storm_data.damage.total <- storm_data.damage %>% group_by(EVTYPE) %>% summarize(TOTAL.DMG.EVTYPE = sum(TOTAL.DMG))%>% arrange(-TOTAL.DMG.EVTYPE) ## `summarise()` ungrouping output (override with `.groups` argument)head(storm_data.damage.total,10)## # A tibble: 10 x 2
## EVTYPE TOTAL.DMG.EVTYPE
## <chr> <dbl>
## 1 FLOOD 150319678250
## 2 HURRICANE/TYPHOON 71913712800
## 3 TORNADO 57352117607
## 4 STORM SURGE 43323541000
## 5 FLASH FLOOD 17562132111
## 6 DROUGHT 15018672000
## 7 HURRICANE 14610229010
## 8 RIVER FLOOD 10148404500
## 9 ICE STORM 8967041810
## 10 TROPICAL STORM 8382236550Result
Health Impact
The top 10 events with the highest total fatalities and injuries are shown graphically.
library(ggplot2)
g <- ggplot(storm_data.fatalities[1:10,], aes(x= EVTYPE, y=total.fatalities,fill=EVTYPE))+theme(axis.text.x = element_text(angle = 30,
hjust = 1))+geom_bar(stat="identity") +ggtitle("Top 10 Events with Highest Total Fatalities") +labs(x="EVENT TYPE", y="Total Fatalities")
g
g <- ggplot(storm_data.injuries[1:10,], aes(x= EVTYPE, y=total.injuries,fill=EVTYPE))+theme(axis.text.x = element_text(angle = 30,
hjust = 1))+geom_bar(stat="identity") +ggtitle("Top 10 Events with Highest Total Injuries") +labs(x="EVENT TYPE", y="Total Injuries")
g
We can see that Tornado causes the highest number of both injuries and fatalities.
Economic Impact
The top 10 events with the highest total economic damages (property and crop combined) are shown graphically.
g <- ggplot(storm_data.damage.total[1:10,], aes(x=EVTYPE, y=TOTAL.DMG.EVTYPE, fill=EVTYPE))+geom_bar(stat="identity") + theme(axis.text.x = element_text(angle=30, hjust=1))+ggtitle("Top 10 Events with Highest Economic Impact") +labs(x="EVENT TYPE", y="Total Economic Impact ($USD)")
g
We can see that Flood causes the highest economic damage.