Health/Economic Consequences in U.S. caused by Storms ans Weather Events

GitHub: GitHub NOAA Project

1 - Introduction

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 Data Science 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.

Look to link: Storm Data Documentation. At pag.51 of this document, there is an example, evolving Hurricane Andrew where the powerful winds resulted in 4 fatalities, 50 injuries, $13B in property damage and $ 750M in crop damage (Notation Example pag.51: FLZ018-021 >023 24 0325EST 4 50 13B 750M Hurricane/Typhoon 0900EST)

2 - Synopsis

This report address questions related to Weather Events and Storms in U.S. that are most damaging in terms of Fatalities, Injuries and damages to properties and crop.

The two mainly questions to be answered are:

1 - Which types of events are most harmful with respect to population health?

2 - Which types of events have the greatest economic consequences?

3 - Loading the Data

3.1 - R libraries

getwd()
## [1] "D:/New folder/Data Science/Projects/Reproducible REsearch/RepData_PeerAssessment2"
library(knitr)
library(markdown)
library(rmarkdown)
library(plyr)
library(stats)

3.2 - Loading NOAA data into R

storm <- read.csv(file = "StormData.csv", header = TRUE, sep = ",")
dim(storm)
## [1] 902297     37
names(storm)
##  [1] "STATE__"    "BGN_DATE"   "BGN_TIME"   "TIME_ZONE"  "COUNTY"    
##  [6] "COUNTYNAME" "STATE"      "EVTYPE"     "BGN_RANGE"  "BGN_AZI"   
## [11] "BGN_LOCATI" "END_DATE"   "END_TIME"   "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE"  "END_AZI"    "END_LOCATI" "LENGTH"     "WIDTH"     
## [21] "F"          "MAG"        "FATALITIES" "INJURIES"   "PROPDMG"   
## [26] "PROPDMGEXP" "CROPDMG"    "CROPDMGEXP" "WFO"        "STATEOFFIC"
## [31] "ZONENAMES"  "LATITUDE"   "LONGITUDE"  "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS"    "REFNUM"
str(storm)
## 'data.frame':    902297 obs. of  37 variables:
##  $ STATE__   : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ BGN_DATE  : Factor w/ 16335 levels "1/1/1966 0:00:00",..: 6523 6523 4242 11116 2224 2224 2260 383 3980 3980 ...
##  $ BGN_TIME  : Factor w/ 3608 levels "00:00:00 AM",..: 272 287 2705 1683 2584 3186 242 1683 3186 3186 ...
##  $ TIME_ZONE : Factor w/ 22 levels "ADT","AKS","AST",..: 7 7 7 7 7 7 7 7 7 7 ...
##  $ COUNTY    : num  97 3 57 89 43 77 9 123 125 57 ...
##  $ COUNTYNAME: Factor w/ 29601 levels "","5NM E OF MACKINAC BRIDGE TO PRESQUE ISLE LT MI",..: 13513 1873 4598 10592 4372 10094 1973 23873 24418 4598 ...
##  $ STATE     : Factor w/ 72 levels "AK","AL","AM",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ EVTYPE    : Factor w/ 985 levels "   HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
##  $ BGN_RANGE : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BGN_AZI   : Factor w/ 35 levels "","  N"," NW",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ BGN_LOCATI: Factor w/ 54429 levels "","- 1 N Albion",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ END_DATE  : Factor w/ 6663 levels "","1/1/1993 0:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ END_TIME  : Factor w/ 3647 levels ""," 0900CST",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ 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   : Factor w/ 24 levels "","E","ENE","ESE",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ END_LOCATI: Factor w/ 34506 levels "","- .5 NNW",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ 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: Factor w/ 19 levels "","-","?","+",..: 17 17 17 17 17 17 17 17 17 17 ...
##  $ CROPDMG   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ CROPDMGEXP: Factor w/ 9 levels "","?","0","2",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WFO       : Factor w/ 542 levels ""," CI","$AC",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ STATEOFFIC: Factor w/ 250 levels "","ALABAMA, Central",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ ZONENAMES : Factor w/ 25112 levels "","                                                                                                               "| __truncated__,..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ 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   : Factor w/ 436774 levels "","-2 at Deer Park\n",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ REFNUM    : num  1 2 3 4 5 6 7 8 9 10 ...

3.3 - Wrangling the Data

Defining variables that will be used:

  • EVTYPE: Event Type (Tornados, Flood, ….)

  • FATALITIES: Number of Fatalities

  • INJURIES: Number of Injuries

  • PROGDMG: Property Damage

  • PROPDMGEXP: Units for Property Damage (magnitudes - K,B,M)

  • CROPDMG: Crop Damage

  • CROPDMGEXP: Units for Crop Damage (magnitudes - K,BM,B)

varsNedeed <- c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")
storm <- storm[varsNedeed]
dim(storm)
## [1] 902297      7
names(storm)
## [1] "EVTYPE"     "FATALITIES" "INJURIES"   "PROPDMG"    "PROPDMGEXP"
## [6] "CROPDMG"    "CROPDMGEXP"
str(storm)
## 'data.frame':    902297 obs. of  7 variables:
##  $ EVTYPE    : Factor w/ 985 levels "   HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
##  $ 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: Factor w/ 19 levels "","-","?","+",..: 17 17 17 17 17 17 17 17 17 17 ...
##  $ CROPDMG   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ CROPDMGEXP: Factor w/ 9 levels "","?","0","2",..: 1 1 1 1 1 1 1 1 1 1 ...

3.3.1 - Calculating Total for Property Damage

  • Refactor of variable PROPDNGEXP
unique(storm$PROPDMGEXP)
##  [1] K M   B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels:  - ? + 0 1 2 3 4 5 6 7 8 B h H K m M
storm$PROPDMGEXP <- mapvalues(storm$PROPDMGEXP, from = c("K", "M","", "B", "m", "+", "0", "5", "6", "?", "4", "2", "3", "h", "7", "H", "-", "1", "8"), to = c(10^3, 10^6, 1, 10^9, 10^6, 0,1,10^5, 10^6, 0, 10^4, 10^2, 10^3, 10^2, 10^7, 10^2, 0, 10, 10^8))
storm$PROPDMGEXP <- as.numeric(as.character(storm$PROPDMGEXP))
storm$PROPDMGTOTAL <- (storm$PROPDMG * storm$PROPDMGEXP)/1000000000
  • Refactor of variable CROPDMGEXP variable
unique(storm$CROPDMGEXP)
## [1]   M K m B ? 0 k 2
## Levels:  ? 0 2 B k K m M
storm$CROPDMGEXP <- mapvalues(storm$CROPDMGEXP, from = c("","M", "K", "m", "B", "?", "0", "k","2"), to = c(1,10^6, 10^3, 10^6, 10^9, 0, 1, 10^3, 10^2))
storm$CROPDMGEXP <- as.numeric(as.character(storm$CROPDMGEXP))
storm$CROPDMGTOTAL <- (storm$CROPDMG * storm$CROPDMGEXP)/1000000000

4 - Processing the Data:

Lets answer the question about Which Types of Events are most Harmful for population HEALTH? The variables involved are FATALITIES and INJURIES.

4.1 - Events are most harmful to population Health?

The item 2.6 (page 9) of National Weather Service Storm Data documentation describes about Fatalities and Injuries. So, it is necessary to assess these Variables to define which of EVENTS (EVTYPE) are most harmful. Look to link: Storm Data Documentation

4.1.1 - Total Number of Fatalities per Event

sumFatalities <- aggregate(FATALITIES ~ EVTYPE, data = storm,  FUN="sum")
dim(sumFatalities)  ## 985 observations
## [1] 985   2
  • Ordering Number of Fatalities and defining the top 10 Weather events
fatalities10events <- sumFatalities[order(-sumFatalities$FATALITIES), ][1:10, ]
dim(fatalities10events)
## [1] 10  2
fatalities10events
##             EVTYPE FATALITIES
## 834        TORNADO       5633
## 130 EXCESSIVE HEAT       1903
## 153    FLASH FLOOD        978
## 275           HEAT        937
## 464      LIGHTNING        816
## 856      TSTM WIND        504
## 170          FLOOD        470
## 585    RIP CURRENT        368
## 359      HIGH WIND        248
## 19       AVALANCHE        224
  • BarPlot of the 10 Fatalities Events most harmful to population Health.
par(mfrow = c(1,1), mar = c(12, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(fatalities10events$FATALITIES, names.arg = fatalities10events$EVTYPE, las = 3, main = "10 Fatalities Highest Events", ylab = "Number of Fatalities")

dev.copy(png, "fatalities-events.png", width = 480, height = 480)
## png 
##   3
dev.off()
## png 
##   2

4.1.2 - Total Number of Injuries per Event

Using the same reasoning of Fatalities, let’s evaluate the Number of Injuries per type of Event (EVTYPE)

sumInjuries <- aggregate(INJURIES ~ EVTYPE, data = storm,  FUN="sum")
dim(sumInjuries)  ## 985 observations
## [1] 985   2
  • Ordering Number of INJURIES and defining the top 10 Weather events in this category
injuries10events <- sumInjuries[order(-sumInjuries$INJURIES), ][1:10, ]
dim(injuries10events)
## [1] 10  2
injuries10events
##                EVTYPE INJURIES
## 834           TORNADO    91346
## 856         TSTM WIND     6957
## 170             FLOOD     6789
## 130    EXCESSIVE HEAT     6525
## 464         LIGHTNING     5230
## 275              HEAT     2100
## 427         ICE STORM     1975
## 153       FLASH FLOOD     1777
## 760 THUNDERSTORM WIND     1488
## 244              HAIL     1361
  • BarPlot of the 10 INJURIES Events most harmful to population Health.
par(mfrow = c(1,1), mar = c(12, 6, 3, 2), mgp = c(4, 1, 0), cex = 0.8)
barplot(injuries10events$INJURIES, names.arg = injuries10events$EVTYPE, las = 3, main = "10 Injuries Highest Events", ylab = "Number of Injuries")

dev.copy(png, "injuries-events.png", width = 480, height = 480)
## png 
##   3
dev.off()
## png 
##   2

4.2 - Which type of Events have the greatest Economic consequences?

The item 2.7 (page 12 and the APPENDIX B) of National Weather Service Storm Data documentation describes about Damage. Two variables, PROPDMG (for Property Damage) and CROPDMG (for Crop Damage) are used to represent these losts. If you want, read more about theses damages, please connect with National Weather Service using the link Storm Data Documentation

4.2.1 - Property Damage

  • Calculating Property Damage for type of Event
sumPropertyDamage <- aggregate(PROPDMGTOTAL ~ EVTYPE, data = storm,  FUN="sum")
dim(sumPropertyDamage)  ## 985 observations
## [1] 985   2
  • We have 985 observations, which is a great number of Events to present in a Plot.
  • Lets stay with the 10 most Property Damage Events
propdmg10Total <- sumPropertyDamage[order(-sumPropertyDamage$PROPDMGTOTAL), ][1:10, ]
propdmg10Total
##                EVTYPE PROPDMGTOTAL
## 170             FLOOD   144.657710
## 411 HURRICANE/TYPHOON    69.305840
## 834           TORNADO    56.947381
## 670       STORM SURGE    43.323536
## 153       FLASH FLOOD    16.822674
## 244              HAIL    15.735268
## 402         HURRICANE    11.868319
## 848    TROPICAL STORM     7.703891
## 972      WINTER STORM     6.688497
## 359         HIGH WIND     5.270046
  • BarPlot of the 10 Proprietary Damage Events most harmful to population economic.
par(mfrow = c(1,1), mar = c(12, 6, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(propdmg10Total$PROPDMGTOTAL, names.arg = propdmg10Total$EVTYPE, las = 3, main = "10 Property Damages Highest Events", ylab = "Damage Property Values (in Billions)")

dev.copy(png, "propdmg-total.png", width = 480, height = 480)
## png 
##   3
dev.off()
## png 
##   2

4.2.2 - Crop Damage

  • Calculating Crop Damage for type of Event
sumCropDamage <- aggregate(CROPDMGTOTAL ~ EVTYPE, data = storm,  FUN="sum")
dim(sumCropDamage) ## 985 observations
## [1] 985   2
  • We have 985 observations, which is a great number of Events to present in a Plot.
  • Lets stay with the 10 most Crop Damage Events
cropdmg10Total <- sumCropDamage[order(-sumCropDamage$CROPDMGTOTAL), ][1:10, ]
cropdmg10Total
##                EVTYPE CROPDMGTOTAL
## 95            DROUGHT    13.972566
## 170             FLOOD     5.661968
## 590       RIVER FLOOD     5.029459
## 427         ICE STORM     5.022113
## 244              HAIL     3.025954
## 402         HURRICANE     2.741910
## 411 HURRICANE/TYPHOON     2.607873
## 153       FLASH FLOOD     1.421317
## 140      EXTREME COLD     1.292973
## 212      FROST/FREEZE     1.094086
  • BarPlot of the 10 Crop Damage Events most harmful to population economic.
par(mfrow = c(1,1), mar = c(10, 6, 3, 2), mgp = c(3, 1, 0), cex = 0.6)
barplot(cropdmg10Total$CROPDMGTOTAL, names.arg = cropdmg10Total$EVTYPE, las = 2, main = "10 Crop Damages Highest Events", ylab = "Damage Crop Values (in Billions) ")

dev.copy(png, "cropdmg-total.png", width = 480, height = 480)
## png 
##   3
dev.off()
## png 
##   2

5 - Results and Conclusions

5.1 - Answering Question 1

As demonstrated by the Graphs, Tornados causes the greatest number of Fatalities and Injuries.

Specifically in FATALITIES, after Tornados, EXCESSIVE HEAT, FLASH FLOOD and HEAT are the next ones.

Specifically in INJURIES, after tornados we have TSTM WIND, FLOOD and EXCESSIVE HEAT.

5.2 - Answering Question 2

Floods are the Weather Event that cause most Property Damage, followed by Hurrucanes.

Drought are the Weather Event that causes most Crop damages, follwed by Flood.

5.3 - Conclusions

Based on evidences demonstrated previously, tornados, floods and droughts have priorities to minize the impact in human and economic costs of Weather Events. Government and society have to be alert and prepared for each type of events. For safety, it’s important to population to know what to do during these events.