Course project 2
C. Becher
2025-03-11
Title: Impact of Severe Weather Events on Public Health and the Economy in the U.S. (1950–2011)
Synopsis
Severe weather events pose significant risks to both public health and economic stability in the United States. This analysis explores data from the NOAA Storm Database, covering events from 1950 to 2011, to identify which weather events have had the greatest impact.
Two key aspects are analyzed: (1) the impact of weather events on population health, measured by fatalities and injuries, and (2) the economic consequences, assessed through property and crop damage estimates.The findings indicate that tornadoes are the most harmful weather events in terms of human health, accounting for the highest number of combined fatalities and injuries. Excessive heat, flash floods, and thunderstorms also contribute significantly to public health risks.
Regarding economic impact, floods cause the greatest financial damage, followed by hurricanes/typhoons, storm surges, and tornadoes. These events result in substantial property and agricultural losses, emphasizing their financial burden on communities and government agencies.
This report provides insights into the most impactful weather events, aiding policymakers and emergency management in resource allocation and disaster preparedness.
Data processing
1. Load the dataset
data <- read.csv("//vf-lumc-home.lumcnet.prod.intern/lumc-home$/cbecher/MyDocs/R/repdata_data_StormData.csv")2. Briefly inspect the dataset
Take a quick look at its structure to understand which columns are there
str(data)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : chr "4/18/1950 0:00:00" "4/18/1950 0:00:00" "2/20/1951 0:00:00" "6/8/1951 0:00:00" ...
## $ BGN_TIME : chr "0130" "0145" "1600" "0900" ...
## $ TIME_ZONE : chr "CST" "CST" "CST" "CST" ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: chr "MOBILE" "BALDWIN" "FAYETTE" "MADISON" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : chr "" "" "" "" ...
## $ BGN_LOCATI: chr "" "" "" "" ...
## $ END_DATE : chr "" "" "" "" ...
## $ END_TIME : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ END_LOCATI: chr "" "" "" "" ...
## $ 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: chr "K" "K" "K" "K" ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr "" "" "" "" ...
## $ WFO : chr "" "" "" "" ...
## $ STATEOFFIC: chr "" "" "" "" ...
## $ ZONENAMES : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...summary(data)## STATE__ BGN_DATE BGN_TIME TIME_ZONE
## Min. : 1.0 Length:902297 Length:902297 Length:902297
## 1st Qu.:19.0 Class :character Class :character Class :character
## Median :30.0 Mode :character Mode :character Mode :character
## Mean :31.2
## 3rd Qu.:45.0
## Max. :95.0
##
## COUNTY COUNTYNAME STATE EVTYPE
## Min. : 0.0 Length:902297 Length:902297 Length:902297
## 1st Qu.: 31.0 Class :character Class :character Class :character
## Median : 75.0 Mode :character Mode :character Mode :character
## Mean :100.6
## 3rd Qu.:131.0
## Max. :873.0
##
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE
## Min. : 0.000 Length:902297 Length:902297 Length:902297
## 1st Qu.: 0.000 Class :character Class :character Class :character
## Median : 0.000 Mode :character Mode :character Mode :character
## Mean : 1.484
## 3rd Qu.: 1.000
## Max. :3749.000
##
## END_TIME COUNTY_END COUNTYENDN END_RANGE
## Length:902297 Min. :0 Mode:logical Min. : 0.0000
## Class :character 1st Qu.:0 NA's:902297 1st Qu.: 0.0000
## Mode :character Median :0 Median : 0.0000
## Mean :0 Mean : 0.9862
## 3rd Qu.:0 3rd Qu.: 0.0000
## Max. :0 Max. :925.0000
##
## END_AZI END_LOCATI LENGTH WIDTH
## Length:902297 Length:902297 Min. : 0.0000 Min. : 0.000
## Class :character Class :character 1st Qu.: 0.0000 1st Qu.: 0.000
## Mode :character Mode :character Median : 0.0000 Median : 0.000
## Mean : 0.2301 Mean : 7.503
## 3rd Qu.: 0.0000 3rd Qu.: 0.000
## Max. :2315.0000 Max. :4400.000
##
## 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 Length:902297 Min. : 0.000 Length:902297
## 1st Qu.: 0.00 Class :character 1st Qu.: 0.000 Class :character
## Median : 0.00 Mode :character Median : 0.000 Mode :character
## Mean : 12.06 Mean : 1.527
## 3rd Qu.: 0.50 3rd Qu.: 0.000
## Max. :5000.00 Max. :990.000
##
## WFO STATEOFFIC ZONENAMES LATITUDE
## Length:902297 Length:902297 Length:902297 Min. : 0
## Class :character Class :character Class :character 1st Qu.:2802
## Mode :character Mode :character Mode :character Median :3540
## Mean :2875
## 3rd Qu.:4019
## Max. :9706
## NA's :47
## LONGITUDE LATITUDE_E LONGITUDE_ REMARKS
## Min. :-14451 Min. : 0 Min. :-14455 Length:902297
## 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0 Class :character
## Median : 8707 Median : 0 Median : 0 Mode :character
## Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. : 17124 Max. :9706 Max. :106220
## NA's :40
## REFNUM
## Min. : 1
## 1st Qu.:225575
## Median :451149
## Mean :451149
## 3rd Qu.:676723
## Max. :902297
## head(data)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE EVTYPE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL TORNADO
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL TORNADO
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL TORNADO
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL TORNADO
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL TORNADO
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL TORNADO
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END COUNTYENDN
## 1 0 0 NA
## 2 0 0 NA
## 3 0 0 NA
## 4 0 0 NA
## 5 0 0 NA
## 6 0 0 NA
## END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES INJURIES PROPDMG
## 1 0 14.0 100 3 0 0 15 25.0
## 2 0 2.0 150 2 0 0 0 2.5
## 3 0 0.1 123 2 0 0 2 25.0
## 4 0 0.0 100 2 0 0 2 2.5
## 5 0 0.0 150 2 0 0 2 2.5
## 6 0 1.5 177 2 0 0 6 2.5
## PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES LATITUDE LONGITUDE
## 1 K 0 3040 8812
## 2 K 0 3042 8755
## 3 K 0 3340 8742
## 4 K 0 3458 8626
## 5 K 0 3412 8642
## 6 K 0 3450 8748
## LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3051 8806 1
## 2 0 0 2
## 3 0 0 3
## 4 0 0 4
## 5 0 0 5
## 6 0 0 6Conclusion: The key variables relevant to answer the questions are: EVTYPE, FATALITIES, INJURIES, PROPDMG (progerty damage estimates), PROPDMGEXP (eponent for property damage), CROPDMG (crop damage estimates), and CROPDMGEXP (exponent for crop damage)
Further data processing steps include: - Coded exponential format (K for thousand, M for million, B for billion), need to be converted to numeric form. - Calculation of the total damage - Grouping the data by event type - Summary of total property and crop damages
library(dplyr)## Warning: package 'dplyr' was built under R version 4.3.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 4.3.3# Function to convert exponent values
convert_exp <- function(exp) {
# Ensure exp is treated as character
exp <- as.character(exp)
exp <- toupper(exp) # Convert to uppercase for consistency
# Check if exp is NA or an empty string and return 1 (meaning no multiplier)
if (is.na(exp) || exp == "") {
return(1)
}
# Map known exponents to their numerical values
if (exp == "B") {
return(1e9) # Billion
} else if (exp == "M") {
return(1e6) # Million
} else if (exp == "K") {
return(1e3) # Thousand
} else if (grepl("^[0-9]$", exp)) {
# If exp is a single digit (e.g., "2" means 10^2 = 100)
return(10^as.numeric(exp))
} else {
return(1) # Default: treat unknown values as 1
}
}
# Apply the function safely
data$PROPDMGEXP <- sapply(data$PROPDMGEXP, convert_exp)
data$CROPDMGEXP <- sapply(data$CROPDMGEXP, convert_exp)Results
3. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
The Top 10 weather events causing the highest number of fatalities and injuries are shown in Figure 1. Tornadoes have the most significant impact on population health, following by excessive heat and thunderstorms.
# Summarize total fatalities and injuries per event type
health_impact <- data %>%
group_by(EVTYPE) %>%
summarise(
total_fatalities = sum(FATALITIES, na.rm = TRUE),
total_injuries = sum(INJURIES, na.rm = TRUE)
) %>%
arrange(desc(total_fatalities + total_injuries))
# Show top 10 most harmful events
head(health_impact, 10)## # A tibble: 10 × 3
## EVTYPE total_fatalities total_injuries
## <chr> <dbl> <dbl>
## 1 TORNADO 5633 91346
## 2 EXCESSIVE HEAT 1903 6525
## 3 TSTM WIND 504 6957
## 4 FLOOD 470 6789
## 5 LIGHTNING 816 5230
## 6 HEAT 937 2100
## 7 FLASH FLOOD 978 1777
## 8 ICE STORM 89 1975
## 9 THUNDERSTORM WIND 133 1488
## 10 WINTER STORM 206 1321Let’s visualize this in a bar chart:
top_health_events <- head(health_impact, 10)
ggplot(top_health_events, aes(x = reorder(EVTYPE, -(total_fatalities + total_injuries)),
y = total_fatalities + total_injuries, fill = total_fatalities)) +
geom_bar(stat = "identity") +
coord_flip() +
labs(title = "Top 10 Most Harmful Weather Events to Population Health",
x = "Event Type",
y = "Total Fatalities and Injuries") +
theme_minimal()
Figure 1.Impact of weather event on population health: Representation of the top 10 weather event types that caused the highest humber of fatalities and injureis in the U.S between 1950 and 2011
4. Economic Consequences: Across the United States, which types of events have the greatest economic consequences?
# Compute total property and crop damage
data$prop_damage <- data$PROPDMG * data$PROPDMGEXP
data$crop_damage <- data$CROPDMG * data$CROPDMGEXP
# Summarize total economic impact per event type
economic_impact <- data %>%
group_by(EVTYPE) %>%
summarise(
total_property_damage = sum(prop_damage, na.rm = TRUE),
total_crop_damage = sum(crop_damage, na.rm = TRUE),
total_economic_loss = total_property_damage + total_crop_damage
) %>%
arrange(desc(total_economic_loss))
# Display top 10 most economically damaging events
head(economic_impact, 10)## # A tibble: 10 × 4
## EVTYPE total_property_damage total_crop_damage total_economic_loss
## <chr> <dbl> <dbl> <dbl>
## 1 FLOOD 144657709807 5661968450 150319678257
## 2 HURRICANE/TYPHOON 69305840000 2607872800 71913712800
## 3 TORNADO 56947380676. 414953270 57362333946.
## 4 STORM SURGE 43323536000 5000 43323541000
## 5 HAIL 15735267018. 3025954473 18761221491.
## 6 FLASH FLOOD 16822673978. 1421317100 18243991078.
## 7 DROUGHT 1046106000 13972566000 15018672000
## 8 HURRICANE 11868319010 2741910000 14610229010
## 9 RIVER FLOOD 5118945500 5029459000 10148404500
## 10 ICE STORM 3944927860 5022113500 8967041360top_economic_events <- head(economic_impact, 10)
ggplot(top_economic_events, aes(x = reorder(EVTYPE, -total_economic_loss), y = total_economic_loss)) +
geom_bar(stat = "identity", fill = "steelblue") +
coord_flip() +
labs(title = "Top 10 Weather Events Causing the Greatest Economic Damage",
x = "Event Type",
y = "Total Economic Damage (USD)") +
theme_minimal()
Figure 2. Economic Impact of Weather Events: Illustration of the top 10 weather event types that resulted in the greatest economic damage in the U.S. from 1950 to 2011