Impact of Severe Weather Events on Population Health and Economy

Synopsis

This analysis examines the NOAA Storm Database to determine which weather events have the greatest impact on population health and economic damage. Tornadoes cause the most fatalities and injuries, while floods cause the highest economic losses.

Data Processing

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, union

library(ggplot2)

data <- read.csv("stormdata.csv.bz2")

dim(data)

## [1] 902297     37

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              6

Results

Population Health Impact

health_data <- data %>%
  group_by(EVTYPE) %>%
  summarise(
    fatalities = sum(FATALITIES, na.rm = TRUE),
    injuries = sum(INJURIES, na.rm = TRUE)
  )

top_health <- health_data %>%
  mutate(total = fatalities + injuries) %>%
  arrange(desc(total)) %>%
  head(10)

library(ggplot2)
ggplot(top_health, aes(x = reorder(EVTYPE, total), y = total)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(title = "Top 10 Events Harmful to Population Health",
       x = "Event Type",
       y = "Total Fatalities and Injuries")

Tornadoes are the most harmful events to population health, followed by excessive heat and floods.

---

Economic Impact

convert_exp <- function(exp) {
  ifelse(exp == "K", 1e3,
  ifelse(exp == "M", 1e6,
  ifelse(exp == "B", 1e9, 1)))
}

data$PROPDMGEXP <- convert_exp(data$PROPDMGEXP)
data$CROPDMGEXP <- convert_exp(data$CROPDMGEXP)

data$PROPDMG_TOTAL <- data$PROPDMG * data$PROPDMGEXP
data$CROPDMG_TOTAL <- data$CROPDMG * data$CROPDMGEXP

econ_data <- data %>%
  group_by(EVTYPE) %>%
  summarise(
    property = sum(PROPDMG_TOTAL, na.rm = TRUE),
    crop = sum(CROPDMG_TOTAL, na.rm = TRUE)
  )

top_econ <- econ_data %>%
  mutate(total = property + crop) %>%
  arrange(desc(total)) %>%
  head(10)

ggplot(top_econ, aes(x = reorder(EVTYPE, total), y = total)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(title = "Top 10 Events with Greatest Economic Damage",
       x = "Event Type",
       y = "Damage (USD)")

Floods and hurricanes cause the greatest economic damage.