Analysis of NOAA Storm Database
Dr. Julia Maier
2025-07-04
Statistical analysis of Storm Events Database (1950 – 2024)
The database to be analysed currently contains data from January 1950 to December 2024, as entered by NOAA’s National Weather Service (NWS). Bulk data are available in comma-separated files (CSV). In the following, data will be analysed addressing two research questions:
- Across the United States, which types of events are most harmful with respect to population health?
- Across the United States, which types of events have the greatest economic consequences?
In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years can be considered more complete.
Data Processing
Data were loaded from the NOAA website via the provided link. They were then processed by use of RStudio.
# Load libraries
library(knitr)## Warning: Paket 'knitr' wurde unter R Version 4.4.3 erstellt library(dplyr)##
## Attache Paket: 'dplyr'## Die folgenden Objekte sind maskiert von 'package:stats':
##
## filter, lag## Die folgenden Objekte sind maskiert von 'package:base':
##
## intersect, setdiff, setequal, union library(ggplot2)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.1
## ✔ readr 2.1.5## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# Read dataset
StormData <- read_csv("Reproducible Research/repdata_data_StormData.csv")## Rows: 902297 Columns: 37
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (18): BGN_DATE, BGN_TIME, TIME_ZONE, COUNTYNAME, STATE, EVTYPE, BGN_AZI,...
## dbl (18): STATE__, COUNTY, BGN_RANGE, COUNTY_END, END_RANGE, LENGTH, WIDTH, ...
## lgl (1): COUNTYENDN
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Get data overview
summary(StormData)## 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
## # Transform to factor
StormData$EVTYPE <- as.factor(StormData$EVTYPE)Results
Impact of storm events on population health
First, consequences of storm events for population health are analysed. The following two plots illustrate the top ten events and the number of health impacts they cause. Concrete values can be inferred from the respective tables.
# Impact on population health
# Events causing the most injuries
injuries <- StormData %>%
group_by(EVTYPE) %>%
summarise(INJURIES = sum(INJURIES, na.rm = T)) %>%
arrange(desc(INJURIES))
#Barplot illustrating Events causing the most Injuries
x <- injuries$EVTYPE
y <- injuries$INJURIES[1:10]
ylog <- log(y)
barplot(ylog, names.arg = x[1:10],
main = "Sum of Injuries per Event",
xlab = "", ylab = "Number of Events (log)",
cex.names = 0.8,
las = 2,
col = "lightgreen")
# Events causing the most fatalities
fatalities <- StormData %>%
group_by(EVTYPE) %>%
summarise(FALTALITIES = sum(FATALITIES, na.rm = T)) %>%
arrange(desc(FALTALITIES))
# Bar plot illustrating most fatalities per event
a <- fatalities$EVTYPE
b <- fatalities$FALTALITIES[1:10]
barplot(b, names.arg = a[1:10],
main = "Sum of Fatalities per Event",
xlab = "", ylab = "Number of Events",
cex.names = 0.8,
las = 2,
col = "orange")
Impact of storm events on economy
In a second step, consequences of storm events for the economy are reported. Thereby, property and crop damages are considered. Results can be found in the table.
# Transform variables indicating property and crop damage
StormData_new <- StormData %>%
mutate(PROPDMG2 = case_when(
PROPDMGEXP == "K" ~ PROPDMG * 1000,
PROPDMGEXP == "M" ~ PROPDMG * 1000000,
PROPDMGEXP == "B" ~ PROPDMG * 1000000000,
TRUE ~ PROPDMG)) %>%
mutate(CROPDMG2 = case_when(
CROPDMGEXP == "K" ~ CROPDMG * 1000,
CROPDMGEXP == "M" ~ CROPDMG * 1000000,
PROPDMGEXP == "B" ~ PROPDMG * 1000000000,
TRUE ~ CROPDMG))
# Calculate mean value for economic damage
StormData_new <- StormData_new %>%
mutate(DMG = PROPDMG2 + CROPDMG2)
# Aggregate data per event type
events <- aggregate(DMG ~ EVTYPE, data = StormData_new, FUN = sum, na.rm = TRUE)
events$PROPDMG <- aggregate(PROPDMG2 ~ EVTYPE, data = StormData_new, FUN = sum, na.rm = TRUE)
events$CROPDMG <- aggregate(CROPDMG2 ~ EVTYPE, data = StormData_new, FUN = sum, na.rm = TRUE)
# Create table with top 10 damages
table_events <- cbind(events$EVTYPE, events$DMG, events$PROPDMG, events$CROPDMG)
colnames(table_events) <- c("Events", "Total_Damage", "Events2", "Property", "Events3", "Crop")
table_events <- table_events %>%
arrange(desc(Total_Damage))
top_10_eco <- table_events[1:10,]
top_10_eco %>%
select(Events, Total_Damage, Property, Crop) %>%
kable()| Events | Total_Damage | Property | Crop |
|---|---|---|---|
| FLOOD | 153319678257 | 144657709807 | 8661968450 |
| HURRICANE/TYPHOON | 117653712800 | 69305840000 | 48347872800 |
| STORM SURGE | 85883541000 | 43323536000 | 42560005000 |
| TORNADO | 57340614060 | 56925660790 | 414953270 |
| HAIL | 18752904943 | 15727367053 | 3025537890 |
| FLASH FLOOD | 17562179167 | 16140862067 | 1421317100 |
| HURRICANE | 14610229010 | 11868319010 | 2741910000 |
| TROPICAL STORM | 13532236550 | 7703890550 | 5828346000 |
| DROUGHT | 13518672002 | 1046106000 | 12472566002 |
| WINTER STORM | 11715441251 | 6688497251 | 5026944000 |