Analysis of NOAA Storm Events: Population Health and Economic Impacts
Synopsis
This report analyzes the U.S. National Oceanic and Atmospheric Administration (NOAA) storm database to identify which types of severe weather events have the greatest impact on population health and economic damage across the United States. The analysis uses storm data collected from 1950 through November 2011 and evaluates fatalities, injuries, property damage, and crop damage. Results show that tornadoes are responsible for the highest number of fatalities and injuries, while floods, hurricanes/typhoons, and storm surges cause the greatest economic losses. These findings help public officials prioritize resources for preparedness and mitigation.
Data Processing
Load the raw storm data
The analysis begins from the original raw compressed
file (.csv.bz2).
Make sure this file is in your working directory before knitting.
storm <- read.csv("repdata_data_StormData.csv", stringsAsFactors = FALSE)
dim(storm)## [1] 902297 37head(storm)## 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 6Subset the required variables
We keep only the variables needed for the analysis.
storm_clean <- storm[, c("EVTYPE", "FATALITIES", "INJURIES",
"PROPDMG", "PROPDMGEXP",
"CROPDMG", "CROPDMGEXP")]Convert property and crop damage to actual dollar amounts
The NOAA database uses multipliers such as K, M, B.
exp_map <- c("K" = 1e3, "k" = 1e3,
"M" = 1e6, "m" = 1e6,
"B" = 1e9, "b" = 1e9)
storm_clean$PROPDMGEXP[!(storm_clean$PROPDMGEXP %in% names(exp_map))] <- "0"
storm_clean$CROPDMGEXP[!(storm_clean$CROPDMGEXP %in% names(exp_map))] <- "0"
storm_clean$prop_mult <- ifelse(storm_clean$PROPDMGEXP == "0", 0,
exp_map[storm_clean$PROPDMGEXP])
storm_clean$crop_mult <- ifelse(storm_clean$CROPDMGEXP == "0", 0,
exp_map[storm_clean$CROPDMGEXP])
storm_clean$prop_damage <- storm_clean$PROPDMG * storm_clean$prop_mult
storm_clean$crop_damage <- storm_clean$CROPDMG * storm_clean$crop_multAggregate population health impacts
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionhealth <- storm_clean %>%
group_by(EVTYPE) %>%
summarise(fatalities = sum(FATALITIES, na.rm = TRUE),
injuries = sum(INJURIES, na.rm = TRUE)) %>%
arrange(desc(fatalities + injuries))Top 10 events:
head(health, 10)## # A tibble: 10 × 3
## EVTYPE fatalities 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 1321Aggregate economic consequences
economic <- storm_clean %>%
group_by(EVTYPE) %>%
summarise(property = sum(prop_damage, na.rm = TRUE),
crop = sum(crop_damage, na.rm = TRUE),
total = property + crop) %>%
arrange(desc(total))Top 10 events:
head(economic, 10)## # A tibble: 10 × 4
## EVTYPE property crop total
## <chr> <dbl> <dbl> <dbl>
## 1 FLOOD 144657709800 5661968450 150319678250
## 2 HURRICANE/TYPHOON 69305840000 2607872800 71913712800
## 3 TORNADO 56937160480 414953110 57352113590
## 4 STORM SURGE 43323536000 5000 43323541000
## 5 HAIL 15732266720 3025954450 18758221170
## 6 FLASH FLOOD 16140811510 1421317100 17562128610
## 7 DROUGHT 1046106000 13972566000 15018672000
## 8 HURRICANE 11868319010 2741910000 14610229010
## 9 RIVER FLOOD 5118945500 5029459000 10148404500
## 10 ICE STORM 3944927810 5022113500 8967041310Results
1. Events most harmful to population health
top_health <- health[1:10, ]
barplot(top_health$fatalities,
names.arg = top_health$EVTYPE,
las = 2,
main = "Top 10 Weather Events by Fatalities",
cex.names = 0.7)
Figure 1. Tornadoes cause the highest number of fatalities in the U.S.
barplot(top_health$injuries,
names.arg = top_health$EVTYPE,
las = 2,
main = "Top 10 Weather Events by Injuries",
cex.names = 0.7)
Figure 2. Tornadoes also cause the most injuries by a significant margin.
2. Events with greatest economic consequences
top_econ <- economic[1:10, ]
barplot(top_econ$total,
names.arg = top_econ$EVTYPE,
las = 2,
main = "Top 10 Weather Events by Economic Damage",
ylab = "Damage (USD)",
cex.names = 0.7)
Figure 3. Floods, hurricanes/typhoons, and storm surges are the most economically damaging.
Conclusion
- Tornadoes are the most harmful to population
health, causing the highest number of fatalities and injuries.
- Floods, hurricanes, and
storm surges create the greatest economic losses.
- These findings can help municipalities prioritize preparedness and mitigation resources.