Analysis of U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database
Ajay Manikandan Sankanran
June 8, 2017
Synopsis
This project is to analyse the natural disaster effect on human population and economic growth from data obtained from U.S. National Oceanic and Atmospheric Administration’s(NOAA) storm database. The database tracks and records data of major storm and weather events across the United States. Analysis of the effect of this natural disaster on human population and economic growth is performed with estimates like fatalities, injuries, property damage, crop damage.
Data reading
Load required libraries
# Load the libraries required to perform the analysis
library(ggplot2)
library(gridExtra)Reading the data
Download the data to your working directory. Load the data into dataframe weatherdata
# Read data if it is not already read. Use cache=TRUE while starting this
weatherdata <- read.csv("repdata%2Fdata%2FStormData.csv.bz2", sep = ",")Summary of the data
To get a quick synopsis of the avaliable data.
summary(weatherdata)## 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) :588295Data processing
The data is optimized for the analysis. The estimates required for the analysis is filtered into the dataframe stormdata
Stormdata <- weatherdata[,c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "CROPDMG")]The top ten major natural disaster which causes these estimates fatalities, injuries, property damage and crop damage are filtered for the analysis.
- Fatalities data
fatalitiesdata <- aggregate(Stormdata$FATALITIES, by = list(Stormdata$EVTYPE),
FUN = sum, na.rm = TRUE)
colnames(fatalitiesdata) <- c("Event Type", "Fatality")
fatalitiesdata <- fatalitiesdata[order(-fatalitiesdata$Fatality),]
topfatalitiesdata <- fatalitiesdata[1:10,]
topfatalitiesdata$`Event Type` <- factor(topfatalitiesdata$`Event Type`,
levels = topfatalitiesdata$`Event Type`,
ordered = TRUE)- Injury data
injurydata <- aggregate(Stormdata$INJURIES, by = list(Stormdata$EVTYPE),
FUN = sum, na.rm = TRUE)
colnames(injurydata) <- c("Event Type", "Injury")
injurydata <- injurydata[order(-injurydata$Injury),]
topinjurydata <- injurydata[1:10, ]
topinjurydata$`Event Type` <- factor(topinjurydata$`Event Type`,
levels = topinjurydata$`Event Type`,
ordered = TRUE)- Property damage data
propdata <- aggregate(Stormdata$PROPDMG, by = list(Stormdata$EVTYPE),
FUN = sum, na.rm = TRUE)
colnames(propdata) <- c("Event Type", "Property Damage")
propdata <- propdata[order(-propdata$`Property Damage`),]
toppropdata <- propdata[1:10, ]
toppropdata$`Event Type` <- factor(toppropdata$`Event Type`,
levels = toppropdata$`Event Type`,
ordered = TRUE)- Crop damage data
cropdata <- aggregate(Stormdata$CROPDMG, by = list(Stormdata$EVTYPE),
FUN = sum, na.rm = TRUE)
colnames(cropdata) <- c("Event Type", "Crop Damage")
cropdata <- cropdata[order(-cropdata$`Crop Damage`),]
topcropdata <- cropdata[1:10, ]
topcropdata$`Event Type` <- factor(topcropdata$`Event Type`,
levels = topcropdata$`Event Type`,
ordered = TRUE)Result
The data is analysed using data visualization. Estimates are plotted using the ggplot2 library.
ggplot(data = topfatalitiesdata, aes(x = `Event Type`, y = Fatality, fill = `Event Type`)) +
geom_bar(stat = "identity") +
xlab("Event Type") + ylab("Total Fatalities")+
ggtitle("Fatalities by event type")+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggplot(data = topinjurydata, aes(x = `Event Type`, y = Injury, fill = `Event Type`)) +
geom_bar(stat = "identity") +
xlab("Event Type") + ylab("Total Injury")+
ggtitle("Injury by event type")+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
We can analyze that tornado is the major cause of fatalities and injuries affecting the human population.
propplot <- ggplot(data = toppropdata, aes(x = `Event Type`, y = `Property Damage`, fill = `Event Type`)) +
geom_bar(stat = "identity", show.legend = F) +
xlab("Event Type") + ylab("Total Property Damage")+
ggtitle("Property Damage by event type")+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
cropplot <- ggplot(data = topcropdata, aes(x = `Event Type`, y = `Crop Damage`, fill = `Event Type`)) +
geom_bar(stat = "identity", show.legend = F) +
xlab("Event Type") + ylab("Total Crop Damage")+
ggtitle("Crop Damage by event type")+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
grid.arrange(propplot, cropplot, ncol = 2)
When we analyse the property damage and crop damage estimate plots we see that tornado and hail are responsible for affecting the economic growth and human population.
The above analysis will help the government to prepare and allocate funds to tackle the effect of these natural disaster on the human population and economic growth.