Analysis of harmful weather events and their economic consequences between 1993 and 2011
Synopsis
In this report we aim to describe the most harmful and economic consequences from severe weather events in the United States between the years 1993 and 2011. We obtained the database from national oceanic and atmospheric administration website. The events in the database start in the year 1950 and end in November 2011. We decided to take the data since 1993 because of a lack of good records in previous years. We found that most harmful events and events with greatest economic consequences have low occurence.
Loading package
The following R packages were loaded:
library(lubridate)
library(dplyr)
library(ggplot2)Data processing
From the U.S. National Oceanic and Atmospheric Administration (NOAA) storm database we obtained data about characteristics of major storm and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damages.
Reading the data
The data is a comma-separated values (csv) file and missing values are coded as blank fields.
noaa <- read.csv(file="repdata-data-StormData.csv")After reading we perform a simple exploration on the dataset.
summary(noaa)## STATE__ BGN_DATE BGN_TIME
## Min. : 1.0 5/25/2011 0:00:00: 1202 12:00:00 AM: 10163
## 1st Qu.:19.0 4/27/2011 0:00:00: 1193 06:00:00 PM: 7350
## Median :30.0 6/9/2011 0:00:00 : 1030 04:00:00 PM: 7261
## Mean :31.2 5/30/2004 0:00:00: 1016 05:00:00 PM: 6891
## 3rd Qu.:45.0 4/4/2011 0:00:00 : 1009 12:00:00 PM: 6703
## Max. :95.0 4/2/2006 0:00:00 : 981 03:00:00 PM: 6700
## (Other) :895866 (Other) :857229
## TIME_ZONE COUNTY COUNTYNAME STATE
## CST :547493 Min. : 0.0 JEFFERSON : 7840 TX : 83728
## EST :245558 1st Qu.: 31.0 WASHINGTON: 7603 KS : 53440
## MST : 68390 Median : 75.0 JACKSON : 6660 OK : 46802
## PST : 28302 Mean :100.6 FRANKLIN : 6256 MO : 35648
## AST : 6360 3rd Qu.:131.0 LINCOLN : 5937 IA : 31069
## HST : 2563 Max. :873.0 MADISON : 5632 NE : 30271
## (Other): 3631 (Other) :862369 (Other):621339
## EVTYPE BGN_RANGE BGN_AZI
## HAIL :288661 Min. : 0.000 :547332
## TSTM WIND :219940 1st Qu.: 0.000 N : 86752
## THUNDERSTORM WIND: 82563 Median : 0.000 W : 38446
## TORNADO : 60652 Mean : 1.484 S : 37558
## FLASH FLOOD : 54277 3rd Qu.: 1.000 E : 33178
## FLOOD : 25326 Max. :3749.000 NW : 24041
## (Other) :170878 (Other):134990
## BGN_LOCATI END_DATE END_TIME
## :287743 :243411 :238978
## COUNTYWIDE : 19680 4/27/2011 0:00:00: 1214 06:00:00 PM: 9802
## Countywide : 993 5/25/2011 0:00:00: 1196 05:00:00 PM: 8314
## SPRINGFIELD : 843 6/9/2011 0:00:00 : 1021 04:00:00 PM: 8104
## SOUTH PORTION: 810 4/4/2011 0:00:00 : 1007 12:00:00 PM: 7483
## NORTH PORTION: 784 5/30/2004 0:00:00: 998 11:59:00 PM: 7184
## (Other) :591444 (Other) :653450 (Other) :622432
## COUNTY_END COUNTYENDN END_RANGE END_AZI
## Min. :0 Mode:logical Min. : 0.0000 :724837
## 1st Qu.:0 NA's:902297 1st Qu.: 0.0000 N : 28082
## Median :0 Median : 0.0000 S : 22510
## Mean :0 Mean : 0.9862 W : 20119
## 3rd Qu.:0 3rd Qu.: 0.0000 E : 20047
## Max. :0 Max. :925.0000 NE : 14606
## (Other): 72096
## END_LOCATI LENGTH WIDTH
## :499225 Min. : 0.0000 Min. : 0.000
## COUNTYWIDE : 19731 1st Qu.: 0.0000 1st Qu.: 0.000
## SOUTH PORTION : 833 Median : 0.0000 Median : 0.000
## NORTH PORTION : 780 Mean : 0.2301 Mean : 7.503
## CENTRAL PORTION: 617 3rd Qu.: 0.0000 3rd Qu.: 0.000
## SPRINGFIELD : 575 Max. :2315.0000 Max. :4400.000
## (Other) :380536
## F MAG FATALITIES INJURIES
## Min. :0.0 Min. : 0.0 Min. : 0.0000 Min. : 0.0000
## 1st Qu.:0.0 1st Qu.: 0.0 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median :1.0 Median : 50.0 Median : 0.0000 Median : 0.0000
## Mean :0.9 Mean : 46.9 Mean : 0.0168 Mean : 0.1557
## 3rd Qu.:1.0 3rd Qu.: 75.0 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :5.0 Max. :22000.0 Max. :583.0000 Max. :1700.0000
## NA's :843563
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Min. : 0.00 :465934 Min. : 0.000 :618413
## 1st Qu.: 0.00 K :424665 1st Qu.: 0.000 K :281832
## Median : 0.00 M : 11330 Median : 0.000 M : 1994
## Mean : 12.06 0 : 216 Mean : 1.527 k : 21
## 3rd Qu.: 0.50 B : 40 3rd Qu.: 0.000 0 : 19
## Max. :5000.00 5 : 28 Max. :990.000 B : 9
## (Other): 84 (Other): 9
## WFO STATEOFFIC
## :142069 :248769
## OUN : 17393 TEXAS, North : 12193
## JAN : 13889 ARKANSAS, Central and North Central: 11738
## LWX : 13174 IOWA, Central : 11345
## PHI : 12551 KANSAS, Southwest : 11212
## TSA : 12483 GEORGIA, North and Central : 11120
## (Other):690738 (Other) :595920
## ZONENAMES
## :594029
## :205988
## GREATER RENO / CARSON CITY / M - GREATER RENO / CARSON CITY / M : 639
## GREATER LAKE TAHOE AREA - GREATER LAKE TAHOE AREA : 592
## JEFFERSON - JEFFERSON : 303
## MADISON - MADISON : 302
## (Other) :100444
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_
## Min. : 0 Min. :-14451 Min. : 0 Min. :-14455
## 1st Qu.:2802 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0
## Median :3540 Median : 8707 Median : 0 Median : 0
## Mean :2875 Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.:4019 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. :9706 Max. : 17124 Max. :9706 Max. :106220
## NA's :47 NA's :40
## REMARKS REFNUM
## :287433 Min. : 1
## : 24013 1st Qu.:225575
## Trees down.\n : 1110 Median :451149
## Several trees were blown down.\n : 568 Mean :451149
## Trees were downed.\n : 446 3rd Qu.:676723
## Large trees and power lines were blown down.\n: 432 Max. :902297
## (Other) :588295After that the following fields were transformed to date format:
- BGN_DATE
- END_DATE
noaa$BGN_DATE <- mdy_hms(noaa$BGN_DATE)
noaa$END_DATE <- mdy_hms(noaa$END_DATE)We add a new variable in the dataset which represents the beginning year of the event.
noaa <- noaa %>%
mutate(BGN_YEAR = year(BGN_DATE))
tot.by.year <- noaa %>%
group_by(BGN_YEAR) %>%
summarise(count = n())Before performing the data analysis, the graph of the occurrence of the different types of events at the different years was analyzed.
g <- ggplot(data = tot.by.year,
aes( x= BGN_YEAR, y = count ))
g <- g + geom_line(size = 1.0)
g <- g + xlab("Year") + ylab("Total of events")
g <- g + geom_vline(xintercept = 1993)
print(g)
The above graph shows the total of events by year. We decided to take the data since 1993 (vertical line in the graph) due to the amount of events in this period is significantly greater than the previous period (there is a lack of good records).
We selected the data from 1993 to 2011.
noaa <- noaa %>%
filter(BGN_YEAR >= 1993) Results
To answer the first question:
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Firstly, we calculate the count of injuries by evtype.
total.injuries.evtype <- noaa %>%
group_by(EVTYPE) %>%
summarise(count = sum(INJURIES))The histogram of the number of injuries by evtype is presented below:
g <- ggplot(total.injuries.evtype, aes(x=count)) +
geom_histogram(colour="firebrick", fill="white")
g <- g + xlab("Occurence") + ylab("Frecuency (number of injuries by evtype)")
print(g)
The histogram of injuries by evtype reveals that the majority of events have low occurence. Frequency falls steeply with increasing ocurrence. The first 10 type of events in descending order by occurrence are shown below.
arrange(total.injuries.evtype, desc(count))## Source: local data frame [985 x 2]
##
## EVTYPE count
## 1 TORNADO 23310
## 2 FLOOD 6789
## 3 EXCESSIVE HEAT 6525
## 4 LIGHTNING 5230
## 5 TSTM WIND 3631
## 6 HEAT 2100
## 7 ICE STORM 1975
## 8 FLASH FLOOD 1777
## 9 THUNDERSTORM WIND 1488
## 10 WINTER STORM 1321
## .. ... ...To answer the second question:
Across the United States, which types of events have the greatest economic consequences?
We calculate the total of crop damage (CROPDMG), property damage (PROPDMG) and the sum of both by type of event.
total.damage.evtype <- noaa %>%
group_by(EVTYPE) %>%
summarise(total = sum(CROPDMG + PROPDMG ) ) %>%
mutate(type = "Total")
total.damage.crop.evtype <- noaa %>%
group_by(EVTYPE) %>%
summarise(total = sum(CROPDMG) ) %>%
mutate(type = "Crop")
total.damage.prop.evtype <- noaa %>%
group_by(EVTYPE) %>%
summarise(total = sum(PROPDMG) ) %>%
mutate(type = "Property")
total.damage <- rbind(total.damage.evtype, total.damage.crop.evtype, total.damage.prop.evtype)
total.damage$type <- as.factor(total.damage$type)The histogram of crop damages, property damages and the sum of both by type of event (evtype) is presented below:
g <- ggplot(total.damage, aes(x=total)) +
geom_histogram(colour="firebrick", fill="white")
g <- g + xlab("Damages in dollars") + ylab("Frecuency (number of damages by evtype)")
g <- g + facet_grid(type ~ .)
print(g)
The histograms show that the majority of types of events have low economic damages. Frequency falls steeply with increasing economic damages.
The first 10 type of events in descending order by crop damages are shown below.
arrange(total.damage.crop.evtype, desc(total))## Source: local data frame [985 x 3]
##
## EVTYPE total type
## 1 HAIL 579596.28 Crop
## 2 FLASH FLOOD 179200.46 Crop
## 3 FLOOD 168037.88 Crop
## 4 TSTM WIND 109202.60 Crop
## 5 TORNADO 100018.52 Crop
## 6 THUNDERSTORM WIND 66791.45 Crop
## 7 DROUGHT 33898.62 Crop
## 8 THUNDERSTORM WINDS 18684.93 Crop
## 9 HIGH WIND 17283.21 Crop
## 10 HEAVY RAIN 11122.80 Crop
## .. ... ... ...The first 10 type of events in descending order by property damages are shown below.
arrange(total.damage.prop.evtype, desc(total))## Source: local data frame [985 x 3]
##
## EVTYPE total type
## 1 FLASH FLOOD 1420124.6 Property
## 2 TORNADO 1387757.1 Property
## 3 TSTM WIND 1335965.6 Property
## 4 FLOOD 899938.5 Property
## 5 THUNDERSTORM WIND 876844.2 Property
## 6 HAIL 688693.4 Property
## 7 LIGHTNING 603351.8 Property
## 8 THUNDERSTORM WINDS 446293.2 Property
## 9 HIGH WIND 324731.6 Property
## 10 WINTER STORM 132720.6 Property
## .. ... ... ...The first 10 type of events in descending order by crop damages and property damages are shown below.
arrange(total.damage.evtype, desc(total))## Source: local data frame [985 x 3]
##
## EVTYPE total type
## 1 FLASH FLOOD 1599325.1 Total
## 2 TORNADO 1487775.6 Total
## 3 TSTM WIND 1445168.2 Total
## 4 HAIL 1268289.7 Total
## 5 FLOOD 1067976.4 Total
## 6 THUNDERSTORM WIND 943635.6 Total
## 7 LIGHTNING 606932.4 Total
## 8 THUNDERSTORM WINDS 464978.1 Total
## 9 HIGH WIND 342014.8 Total
## 10 WINTER STORM 134699.6 Total
## .. ... ... ...Conclusions
The majority of harmful events have low occurence
The most harmful type of event is TORNADO
The majority of events associated with economic damages have low frequency
The type of event associated with the most economic damage in crop is HAIL
The type of event associated with the most economic damage in property is FLASH FLOOD
The type of event associated with the most economic damage in crop and property is FLASH FLOOD