Weather impacts on Human health and Economics
Synopsis
Human Life Impact
- Weather events can cause signifact impact to Human lives which includes fatalities and injuries.
- It was discovered via analysis of NOAA dataset that “Tornado”, “Excessive heat”, “TSTM Wind” impacts the maximum human life.
- Tornado has caused the most human fatalities and injuries between 1950 and 2011 across the entire Unites States. It has accounted for more than 5000 fatalities and 90,000 injuries
Economic Impact
- Weather events can cause signifact impact on Economic factors like Property and Crop damages
- It was discovered via analysis of NOAA dataset that “Flood”, “Hurricane/Typhoon”, “Tornado”, “Drought” are the most impacting weather events on Economy causing Billions of Dollars in damage
- “Flood” has caused nearly 150 Billions of Dollars of damage on Properties and Crops out of which about 144 Billions of Dollars worth of damage was caused on properties alone
- “Drought” has caused nearly 13.5 Billions of Dollars worth of damage on crops making it the most impactful weather event on the crops
- “Hurricane” and “Tornado” occupies 2nd and 3rd positions respectively regarding the most impactful events on Property damages
- Overall from the data analysis, “Tornado” and “Flood” are the events causing maximum impacts on both human population and economic factors. They have impacted maximum humans causing billions of dollars of damage on properties and crops
Data Processing
Loading the NOAA data
The NOAA data is downloaded from it’s website and loaded using “read.csv” function. Downloading the data directly from the website makes this effort reproducible
#Download the data from the URL
fileUrl <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(fileUrl, "./noaa_storm_data.bz2")
#Read the data and load it into a dataframe
dataset <- read.csv("./noaa_storm_data.bz2")Preprocessing the NOAA data
There isn’t whole lot of preprocessing required for this dataset. The dataset is well structured and doesn’t need any cleanup or tidying. Before further processing, a quick look into a subset of data and their info would help
head(dataset)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 0
## 2 TORNADO 0 0
## 3 TORNADO 0 0
## 4 TORNADO 0 0
## 5 TORNADO 0 0
## 6 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14.0 100 3 0 0
## 2 NA 0 2.0 150 2 0 0
## 3 NA 0 0.1 123 2 0 0
## 4 NA 0 0.0 100 2 0 0
## 5 NA 0 0.0 150 2 0 0
## 6 NA 0 1.5 177 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## 3 2 25.0 K 0
## 4 2 2.5 K 0
## 5 2 2.5 K 0
## 6 6 2.5 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2
## 3 3340 8742 0 0 3
## 4 3458 8626 0 0 4
## 5 3412 8642 0 0 5
## 6 3450 8748 0 0 6 str(dataset)## '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 ""," Christiansburg",..: 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 ""," CANTON"," TULIA",..: 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","%SD",..: 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 "","\t","\t\t",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...Results
Most Harmful Events impacting Human Population Health
- To understand the impact on Population health, two variables “FATALITIES” and “INJURIES” had to be explored. They specify the count of human population impacted by the given event type. Sum of their values per event type would indicate the total human population impact. Below were some of the observations from this analysis
- “Tornado”, “Excessive Heat”, “TSTM Wind” are the top three most impacting events on the human population respectively
- “Tornado” is the most deadliest event impacting more than 96,000 humans
- “Tornado” is responsible for more than 5000 human fatalities
- “Tornado” is responsible for more than 91,000 human injuries
- The below plot provides information about the top 10 most impactful events
plot_human_impact_events <- function(top_10_most_harmful_events) {
harmful_events_melted <- melt(top_10_most_harmful_events, id.vars = 'EVTYPE')
harmful_events_melted <- harmful_events_melted %>% mutate(row = row_number())
harmful_events_plot <- ggplot(harmful_events_melted, aes(x = reorder(EVTYPE,row), y = value, fill = variable)) +
geom_bar(aes(fill = variable), position = 'dodge', stat = "identity") +
geom_text(aes(label = value), position = position_dodge(width = 0.9), vjust = -0.25, size = 3) +
theme( legend.title = element_blank(), axis.title.x = element_blank(),
axis.title.y = element_blank() ) +
labs( x = "Event", y = "Total Injuries + Fatalities") +
labs(title = "Top 10 Events causing most Human Impact") +
theme(plot.title = element_text(hjust = 0.5))
print(harmful_events_plot)
}
#Group By Event Type and find the sum of Fatalities and Injuries
human_impact_dataset <- dataset %>% group_by(EVTYPE) %>%
summarise(totalFatalities = sum(FATALITIES), totalInjuries = sum(INJURIES),
totalHumanImpact = sum(totalFatalities, totalInjuries))
top_10_most_harmful_events <- human_impact_dataset %>% arrange(desc(totalHumanImpact)) %>% slice(1:10)
plot_human_impact_events(top_10_most_harmful_events)
Most Harmful Events impacting Economic entities
- To understand the economic impact, two variables had to be explored. PROPDMG and CROPDMG variables which indicated the impact on properties and crops represented in monetary units (Billions, Millions, Thousands). Sum of their values indicates the total impact on Properties and Crops per event type. Below were some of the observations from this analysis:
- “Flood”, “Hurricane/Typhoon”, “Tornado” were the most impactful events on economy
- Across U.S, “Flood” alone caused about 150 Billion Dollars worth of damage on Properties and Crops
- Out of 150, “Flood” caused around 144 Billion Dollars worth of damage on Properties alone
- “Flood” also caused about 5 Billion Dollars worth of damage on Crops
- “Flood” is the most destructive weather event in terms of Economic damage
- The below plot provides information about the top 10 most impactful events on economy
plot_economic_impact_events <- function(top_10_economic_impactful_events) {
library(ggplot2)
library(reshape2)
economic_impactful_events_melted <- melt(top_10_economic_impactful_events, id.vars = 'EVTYPE')
economic_impactful_events_melted <- economic_impactful_events_melted %>% mutate(row = row_number())
#View(economic_impactful_events_melted)
economic_impactful_events_plot <- ggplot(economic_impactful_events_melted, aes(x = reorder(EVTYPE,row), y = value, fill = variable)) +
geom_bar(aes(fill = variable), position = 'dodge', stat = "identity") +
geom_text(aes(label = value), position = position_dodge(width = 0.9), vjust = -0.25, size = 3) +
theme( legend.title = element_blank(), axis.title.x = element_blank(),
axis.title.y = element_blank() ) +
labs( x = "Event", y = "Total Property + Crop Damages") +
labs(title = "Top 10 Events causing most Economic Impact in Billions of Dollars") +
theme(plot.title = element_text(hjust = 0.5))
print(economic_impactful_events_plot)
}
#Replace the values of PROPDMGEXP and CROPDMGEXP with their letter interpretations
#Letters in PROPDMGEXP and CROPDMGEXP follows below guidelines
# 'K' - Thousands
# 'M' - Millions
# 'B' - Billions
dataset <- dataset %>% mutate(PROPDMGEXP_VALUE = case_when(
PROPDMGEXP == 'K' ~ 1000,
PROPDMGEXP == 'M' ~ 1000000,
PROPDMGEXP == 'B' ~ 1000000000,
TRUE ~ 0))
dataset <- dataset %>% mutate(CROPDMGEXP_VALUE = case_when(
CROPDMGEXP == 'K' ~ 1000,
CROPDMGEXP == 'M' ~ 1000000,
CROPDMGEXP == 'B' ~ 1000000000,
TRUE ~ 0))
#Normalize columns PROPDMG and CROPDMG with PROPDMGEXP_VALUE and CROPDMGEXP_VALUE
dataset <- dataset %>% mutate(PROPDMG_NORM = PROPDMG * PROPDMGEXP_VALUE)
dataset <- dataset %>% mutate(CROPDMG_NORM = CROPDMG * CROPDMGEXP_VALUE)
#Group By Event Type and find the sum of PROPDMG_NORM and CROPDMG_NORM
economic_impact_dataset <- dataset %>% group_by(EVTYPE) %>%
summarise(totalPropertyDamage = sum(PROPDMG_NORM),
totalCropDamage = sum(CROPDMG_NORM),
totalEconomicImpact = sum(totalPropertyDamage, totalCropDamage))
#Represent totalPropertyDamage, totalCropDamage, totalEconomicImpact in Billions of Dollars
#Also Round them off to just 2 decimals
economic_impact_dataset <- economic_impact_dataset %>%
mutate(totalPropertyDamage = round(totalPropertyDamage / 1000000000, 2)) %>%
mutate(totalCropDamage = round(totalCropDamage / 1000000000, 2)) %>%
mutate(totalEconomicImpact = round(totalEconomicImpact / 1000000000, 2))
top_10_economic_impactful_events <- economic_impact_dataset %>% arrange(desc(totalEconomicImpact)) %>% slice(1:10)
plot_economic_impact_events(top_10_economic_impactful_events)
Total impacts - Breakup
- Analysis was performed against each individual variable like Fatalities, Injuries, Property Damage, Crop Damage
- Below were some of the observations from the study:
- “Tornado” continues to be the most fatal and injurious weather event. It has caused about 5000 fatalities and 91,000 injuries
- “Flood” continues to be the most impactful event on Property Damages. It has caused about 144 Billion Dollars worth of damage
- “Drought” happens to the most impactful event on Crop damages. It has caused nearly 14 Billion Dollars worth of damage
- Below plot provides top 10 most impactful events on Fatalities, Injuries, Property Damage, Crop Damage
plot_fatal_events <- function(top_10_fatal_events) {
library(ggplot2)
fatalites_plot <- ggplot(top_10_fatal_events, aes(x = reorder(EVTYPE, row), y = totalFatalities, fill = EVTYPE)) +
geom_bar(stat = "identity") +
geom_text(aes(x = EVTYPE, y = totalFatalities + 100 , label = totalFatalities), size = 3) +
labs( x = "Event", y = "Total Fatalities") +
labs(title = "Top 10 Events causing most fatalities") +
theme(plot.title = element_text(hjust = 0.5))
fatalites_plot
}
plot_injury_prone_events <- function(top_10_injury_prone_events) {
library(ggplot2)
injuries_plot <- ggplot(top_10_injury_prone_events, aes(x = reorder(EVTYPE, row), y = totalInjuries, fill = EVTYPE)) +
geom_bar(stat = "identity") +
geom_text(aes(x = EVTYPE, y = totalInjuries + 100 , label = totalInjuries), size = 3) +
labs( x = "Event", y = "Total Injuries") +
labs(title = "Top 10 Events causing most injuries") +
theme(plot.title = element_text(hjust = 0.5))
injuries_plot
}
plot_prop_damage_events <- function(top_10_prop_damage_events) {
library(ggplot2)
prop_damages_plot <- ggplot(top_10_prop_damage_events, aes(x = reorder(EVTYPE, row), y = totalPropertyDamage,
fill = EVTYPE)) +
geom_bar(stat = "identity") +
geom_text(aes(x = EVTYPE, y = totalPropertyDamage + 1 ,
label = totalPropertyDamage), size = 3) +
labs( x = "Event", y = "Total Property Damages in Billions of Dollars") +
labs(title = "Top 10 Events causing most property damages") +
theme(plot.title = element_text(hjust = 0.5))
prop_damages_plot
}
plot_crop_damage_events <- function(top_10_crop_damage_events) {
library(ggplot2)
crop_damages_plot <- ggplot(top_10_crop_damage_events, aes(x = reorder(EVTYPE, row), y = totalCropDamage,
fill = EVTYPE)) +
geom_bar(stat = "identity") +
geom_text(aes(x = EVTYPE, y = totalCropDamage + 0.25 , label = totalCropDamage), size = 3) +
labs( x = "Event", y = "Total Crop Damages in Billions of Dollars") +
labs(title = "Top 10 Events causing most crop damages") +
theme(plot.title = element_text(hjust = 0.5))
crop_damages_plot
}
################################## Human Impact Plots ##################################
top_10_fatal_events <- human_impact_dataset %>% arrange(desc(totalFatalities)) %>% slice(1:10) %>%
mutate(row = row_number())
fatalites_plot <- plot_fatal_events(top_10_fatal_events)
top_10_injury_prone_events <- human_impact_dataset %>% arrange(desc(totalInjuries)) %>% slice(1:10) %>%
mutate(row = row_number())
injuries_plot <- plot_injury_prone_events(top_10_injury_prone_events)
human_impact_plot_list <- list(fatalites_plot, injuries_plot)
#########################################################################################
################################## Economic Impact Plots ###################################
top_10_prop_damage_events <- economic_impact_dataset %>% select(EVTYPE, totalPropertyDamage) %>%
arrange(desc(totalPropertyDamage)) %>%
slice(1:10) %>%
mutate(row = row_number())
prop_damages_plot <- plot_prop_damage_events(top_10_prop_damage_events)
top_10_crop_damage_events <- economic_impact_dataset %>% select(EVTYPE, totalCropDamage) %>%
arrange(desc(totalCropDamage)) %>%
slice(1:10) %>%
mutate(row = row_number())
crop_damages_plot <- plot_crop_damage_events(top_10_crop_damage_events)
economic_impact_plot_list <- list(prop_damages_plot, crop_damages_plot)
############################################################################################
#Plot Human and Economic Plots in 2 x 2 panels
require(gridExtra)
grid.arrange(grobs = c(human_impact_plot_list, economic_impact_plot_list), ncol = 2)