Lifes and Economical Impact of Storm-Related Disasters in the USA
Jesus A. Gonzalez
Tuesday, April 21, 2015
Synopsis
In this analysis we utilize the NOAA storm database to find the meteorological phenomena that causes more human and economical damage. Our analysis groups data with respect to the event type (i.e. flood) so that we are able to identify the event type that is more dangerous for humans or economical damage. We further divide our results for human damage in fatalities and injuries related to the event type. In the case of economical costs, we separate our results in properties and crops with respect to the event type. Our results show that the event that causes more human damages are tornadoes for both fatalities and injuries. In the case of economical costs tornadoes have higher impact on property while hail affects crops with higher intensity.
Processing
Our storm data file was read with the “read.csv” command. We used the “dplyr” and the “gridExtra” libraries to facilitate our processing step when we group and summarize data (with dplyr commands) and to print nice tables (with gridExtra).
This can be seen in the following code chunk.
library("dplyr")
library("gridExtra")This is a summary of the storm database provided by the National Oceanic and Atmospheric Administration (NOAA). It contains a total of 902,297 storm related events with 37 variables describing each of them. Although there are 37 variables in the database, we only use 5 of them for this study. These are:
- EVTYPE, the type of event (WE USE THIS VARIABLE IN OUR ANALYSIS to separate the damages by the types of event)
- FATALITIES, number of fatalities produced by the event
- INJURIES, number of injured people
- PROPDMG, cost of property damages
- CROPDMG, cost of crop damages
A summary of the storm dataset is presented as follows:
storm <- read.csv(bzfile('./data/repdata-data-StormData.csv.bz2'))
summary(storm)## 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) :588295In order to summarize our results with respect to the event type, we grouped the event data by type of event and then, for each of our analyzes, we summarized our data with respect to fatalities (FATALITIES), injuries (INJURIES), property damage (PROPDMG), and crop damage (CROPDMG) respectively. Finaly, we ordered the data produced by our summary in descending order to identify the events that caused the highest damage. We divided the main values by 1000 in order to obtain smaller values and express them in thousands (i.e. thousands of injuries).
This was performed with the following code:
storm_by_event <- group_by(storm, EVTYPE)
sum_fatalities <- summarise(storm_by_event, FATALITIES = sum(FATALITIES)/1000)
sum_fatalities <- sum_fatalities[with(sum_fatalities, order(-FATALITIES)),]
sum_injuries <- summarise(storm_by_event, INJURIES = sum(INJURIES)/1000)
sum_injuries <- sum_injuries[with(sum_injuries, order(-INJURIES)),]
sum_propdmg <- summarise(storm_by_event, PROPDMG = sum(PROPDMG)/1000)
sum_propdmg <- sum_propdmg[with(sum_propdmg, order(-PROPDMG)),]
sum_cropdmg <- summarise(storm_by_event, CROPDMG = sum(CROPDMG)/1000)
sum_cropdmg <- sum_cropdmg[with(sum_cropdmg, order(-CROPDMG)),]Results
Results from our data analysis over the NOAA storms dataset show that tornadoes are the event type that causes more damage to humans, for both, fatalities (5,600 people) and injuries (91,300 people). We also found that tornadoes are the event type that causes more costs related to properties (3,200,000 dollars) and hail for crops (579,000 dollars). We can observe these results in figure 1. Note that values are expressed in thousands. We can also see that there is high overlap in the types of events for fatalities, injuries, and property damage. We found smaller overlap (but still significant) with the events related to crop damages.
par(mfrow=c(4,1),mar=c(2,8,3,0))
m <- barplot(sum_fatalities$FATALITIES[1:5], las=1, main="Fatalities per Type of Event")
axis(1, at=m, labels=sum_fatalities$EVTYPE[1:5], las=1)
m <- barplot(sum_injuries$INJURIES[1:5], las=1, main="Injuries per Type of Event")
axis(1, at=m, labels=sum_injuries$EVTYPE[1:5], las=1)
m <- barplot(sum_propdmg$PROPDMG[1:5], las=1, main="Property Costs per Type of Event")
axis(1, at=m, labels=sum_propdmg$EVTYPE[1:5], las=1)
m <- barplot(sum_cropdmg$CROPDMG[1:5], las=1, main="Crop Costs per Type of Event")
axis(1, at=m, labels=sum_cropdmg$EVTYPE[1:5], las=1)
Figure 1. Human Fatalities, Human Injuries, Property Costs, and Crops Costs Impact Caused by Storm Events. Values are expressed in thousands.
The most harmful event types for humans fatalities are
- TORNADO
- EXCESSIVE HEAT
- FLASH FLOOD
- HEAT
- LIGHTNING
The most harmful event types for humans injuries are
- TORNADO
- TSTM WIND
- FLOOD
- EXCESSIVE HEAT
- LIGHTNING
The most harmful event types for economical cost of properties are
- TORNADO
- FLASH FLOOD
- TSTM WIND
- FLOOD
- THUNDERSTORM WIND
The most harmful event types for economical cost of crops are
- HAIL
- FLASH FLOOD
- FLOOD
- TSTM WIND
- TORNADO
According to these results, government should give priority to prevent damage from tornadoes, hail, floods, heat, flash floods, lightning, thunder storm, and tstm wind. Other events are also important but these are a priority.
Below we present tables showing the first 10 types of events that cause more damage to humans (for fatalities and injuries) and to properties and crops (for cost of damages).
Table 1. The 10 disasters that are more harmful for people with respect to fatalities:
grid.table(sum_fatalities[1:10,],gp=gpar(fontsize=8))
Table 2. The 10 disasters that are more harmful for people with respect to injuries:
grid.table(sum_injuries[1:10,],gp=gpar(fontsize=8))
Table 3. The 10 event types with more economical consequences on properties:
grid.table(sum_propdmg[1:10,],gp=gpar(fontsize=8))
Table 4. The 10 event types with more economical consequences on crops:
grid.table(sum_cropdmg[1:10,],gp=gpar(fontsize=8))