US NOAA - Storm Data Analysis to assess impact On Health and Economy
Prabeeti Bulani
9/18/2019
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 goal of this report is to identify what weather event(s) has the biggest the impact on
1.Health 2.Economy
Data Processing
NoaaStormData <- read.csv("repdata_data_StormData.csv.bz2",header = TRUE, sep = ",")
# Analysis of Data to understand the which attributes are needed for analysis of impact on health and economy
summary(NoaaStormData)## 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) :588295head(NoaaStormData)dim(NoaaStormData)## [1] 902297 37str(NoaaStormData)## '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/ 436781 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 ...# After analysing the data, selecting few attributes required to answer fatalities, injuries,crop damage and property damage. Also selecting data from Jan 1st 1996, as prior to January 1996, not all data type events were recorded.
NoaaStormData$BGN_DATE <- as.Date(NoaaStormData$BGN_DATE, "%m/%d/%Y")
NoaaStormData_sub <- filter(NoaaStormData, BGN_DATE >= "1996-01-01")
Subset_NoaaStormData <- subset(NoaaStormData_sub, select = c(BGN_DATE,COUNTYNAME,STATE, EVTYPE,END_DATE,FATALITIES,INJURIES,PROPDMG,PROPDMGEXP,CROPDMG,CROPDMGEXP))
head(Subset_NoaaStormData)Data Processing for -which types of events are most harmful with respect to population health
HarmfulEvent <- subset(Subset_NoaaStormData, !Subset_NoaaStormData$FATALITIES == 0 & !Subset_NoaaStormData$INJURIES ==
0, select = c(EVTYPE, FATALITIES, INJURIES))
# Grouping fatalities and injuries for top 10 events
no_of_fatalities <- aggregate(HarmfulEvent$FATALITIES, list(HarmfulEvent$EVTYPE),sum)
names(no_of_fatalities) <- c("Event_Type","Fatalities_count")
no_of_fatalities <- no_of_fatalities[order(-no_of_fatalities$Fatalities_count),][1:10,]
no_of_injuries <- aggregate(HarmfulEvent$INJURIES, list(HarmfulEvent$EVTYPE),sum)
names(no_of_injuries) <- c("Event_Type","Injuries_count")
no_of_injuries <- no_of_injuries[order(-no_of_injuries$Injuries_count),][1:10,]Results - Health Impact
plot top 10 events which has impacted health
#Fatalities_plot
ggplot() + geom_bar(data = no_of_fatalities, aes(x = Event_Type ,
y = Fatalities_count, fill = interaction(Fatalities_count, Event_Type)), stat = "identity",
show.legend = F) + theme(axis.text.x = element_text(angle = 30, hjust = 1)) +
xlab("Harmful Fatal Events") + ylab("Fatalities count") + ggtitle("Top 10 Fatal events") +
theme(axis.text.x = element_text(angle = 30, hjust = 1))
#Injuries_plot
ggplot() + geom_bar(data = no_of_injuries, aes(x = Event_Type, y = Injuries_count,
fill = interaction(Injuries_count, Event_Type)), stat = "identity", show.legend = F) +
theme(axis.text.x = element_text(angle = 30, hjust = 1)) + xlab("Harmful Events - Injuries") +
ylab("Injuries Count") + ggtitle("Top 10 Injury causing events") +
theme(axis.text.x = element_text(angle = 30, hjust = 1))
# Analysis - Tornado is the major cause for causing fatalities and injuries.Data Processing for -which types of events have the greatest economic consequences
# Select data for Property Damage and Crop Damage
EconomicImpact <- subset(Subset_NoaaStormData, !Subset_NoaaStormData$PROPDMG == 0 & !Subset_NoaaStormData$CROPDMG ==
0, select = c(EVTYPE, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP))
EconomicImpact <- subset(EconomicImpact, EconomicImpact$PROPDMGEXP == "K" | EconomicImpact$PROPDMGEXP ==
"k" | EconomicImpact$PROPDMGEXP == "M" | EconomicImpact$PROPDMGEXP == "m" |
EconomicImpact$PROPDMGEXP == "B" | EconomicImpact$PROPDMGEXP == "b")
EconomicImpact <- subset(EconomicImpact, EconomicImpact$CROPDMGEXP == "K" | EconomicImpact$CROPDMGEXP ==
"k" | EconomicImpact$CROPDMGEXP == "M" | EconomicImpact$CROPDMGEXP == "m" |
EconomicImpact$CROPDMGEXP == "B" | EconomicImpact$CROPDMGEXP == "b")
# Convert ecnomic values to number
EconomicImpact$PROPDMGEXP <- gsub("m", 1e+06, EconomicImpact$PROPDMGEXP, ignore.case = TRUE)
EconomicImpact$PROPDMGEXP <- gsub("k", 1000, EconomicImpact$PROPDMGEXP, ignore.case = TRUE)
EconomicImpact$PROPDMGEXP <- gsub("b", 1e+09, EconomicImpact$PROPDMGEXP, ignore.case = TRUE)
EconomicImpact$PROPDMGEXP <- as.numeric(EconomicImpact$PROPDMGEXP)
EconomicImpact$CROPDMGEXP <- gsub("m", 1e+06, EconomicImpact$CROPDMGEXP, ignore.case = TRUE)
EconomicImpact$CROPDMGEXP <- gsub("k", 1000, EconomicImpact$CROPDMGEXP, ignore.case = TRUE)
EconomicImpact$CROPDMGEXP <- gsub("b", 1e+09, EconomicImpact$CROPDMGEXP, ignore.case = TRUE)
EconomicImpact$CROPDMGEXP <- as.numeric(EconomicImpact$CROPDMGEXP)
EconomicImpact$PROPDMGEXP <- as.numeric(EconomicImpact$PROPDMGEXP)
# then sum the damages by each event type
EconomicImpact$Total_damage <- (EconomicImpact$CROPDMG * EconomicImpact$CROPDMGEXP) +
(EconomicImpact$PROPDMG * EconomicImpact$PROPDMGEXP)
EconomicImpact <- aggregate(EconomicImpact$Total_damage, list(EconomicImpact$EVTYPE),sum)
names(EconomicImpact) <- c("Event_Type","Total_damage")
EconomicImpact <- EconomicImpact[order(-EconomicImpact$Total_damage),][1:10,]Results - For Economic Impact
plot top 10 events which has impacted Economy
# Now plot the graph
ggplot() + geom_bar(data = EconomicImpact, aes(x = Event_Type, y = Total_damage, fill = interaction(Total_damage,
Event_Type)), stat = "identity", show.legend = F) + theme(axis.text.x = element_text(angle = 30,
hjust = 1)) + xlab("Event Type") + ylab("Total Damage")+ ggtitle("Top 10 event impacting economy")
# Analysis - Flood is the major cause with respect to cost of damage.Summary
Tornado are most harmful with respect to population health. Flood have the greatest negative impact on economy.
(Comment - Data processing and Results for Economic Impact is after this section)