Synopsis
This report analyzes the consequences of severe weather events in the United States, focusing on their impacts on population health and the economy. Using data from the NOAA Storm Database, we identify the most harmful storm types in terms of fatalities, injuries, property damage, and crop damage. The findings aim to inform disaster preparedness and mitigation strategies to reduce the adverse effects of severe weather events.
Data Processing
The dataset used in this analysis is sourced from the NOAA Storm Database, which contains detailed information about severe weather events in the United States. It includes data on event types, dates, locations, and associated consequences such as fatalities, injuries, and property damage. The dataset spans several decades, providing a comprehensive view of storm impacts over time. This rich dataset enables us to identify patterns and trends, assess the severity of different storm types, and evaluate their effects on public health and the economy. However, it is important to note that the dataset may have limitations, such as inconsistencies in event reporting and missing values, which are addressed during the data preprocessing stage.
# Load necessary libraries
library(dplyr)
# Decompress and load the data
file_path <- "repdata_data_StormData1.csv"
data <- read.csv(file_path)
# View summary of the data
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
## # Display structure of the data
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 ...Results
Population Health Consequences
Population health is the most important aspect of storm consequences, as it directly affects the well-being of individuals and communities. There are many factors that contribute to the impact of storms on population health, including fatalities and injuries. In this section, we will analyze the data to identify the most harmful storm types in terms of their effects on public health.
library(dplyr)
# Summarize fatalities and injuries by event type
health_data <- 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))
# Display top 10 event types harmful to population health
head(health_data, 10)## # A tibble: 10 x 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 1321# Plotting the top 10 event types harmful to population health
library(ggplot2)
ggplot(
health_data[1:10, ],
aes(
x = reorder(EVTYPE, total_fatalities + total_injuries),
y = total_fatalities + total_injuries
)
) +
geom_bar(stat = "identity", fill = "steelblue") +
labs(title = "Top 10 Event Types Harmful to Population Health",
x = "Event Type",
y = "Total Fatalities and Injuries") +
theme_minimal() +
coord_flip()
As the results show, tornadoes are the most harmful storm type in terms of population health, causing the highest number of fatalities and injuries. Other significant storm types include excessive heat, tstm wind, and flood. These findings highlight the need for targeted interventions and preparedness measures to mitigate the impacts of these severe weather events on public health.
Economic Consequences
In the other hand, economic consequences of storms are also significant, as they can lead to substantial financial losses for individuals, businesses, and governments. The economic impact of storms can be measured in terms of property damage and crop damage. In this section, we will analyze the data to identify the storm types that cause the most economic damage.
library(dplyr)
# Summarize property and crop damage by event type
economic_data <- data %>%
group_by(EVTYPE) %>%
summarise(
total_property_damage = sum(PROPDMG, na.rm = TRUE),
total_crop_damage = sum(CROPDMG, na.rm = TRUE)
) %>%
arrange(desc(total_property_damage + total_crop_damage))
# Display top 10 event types causing economic damage
head(economic_data, 10)## # A tibble: 10 x 3
## EVTYPE total_property_damage total_crop_damage
## <chr> <dbl> <dbl>
## 1 TORNADO 3212258. 100019.
## 2 FLASH FLOOD 1420125. 179200.
## 3 TSTM WIND 1335966. 109203.
## 4 HAIL 688693. 579596.
## 5 FLOOD 899938. 168038.
## 6 THUNDERSTORM WIND 876844. 66791.
## 7 LIGHTNING 603352. 3581.
## 8 THUNDERSTORM WINDS 446293. 18685.
## 9 HIGH WIND 324732. 17283.
## 10 WINTER STORM 132721. 1979.# Plotting the top 10 event types causing economic damage
library(scales)
ggplot(
economic_data[1:10, ],
aes(
x = reorder(EVTYPE, total_property_damage + total_crop_damage),
y = total_property_damage + total_crop_damage
)
) +
geom_bar(stat = "identity", fill = "darkgreen") +
labs(title = "Top 10 Event Types Causing Economic Damage",
x = "Event Type",
y = "Total Property and Crop Damage") +
scale_y_continuous(labels = scales::label_number_si()) +
theme_minimal() +
coord_flip()
The results indicate that the most economically damaging storm type is same as the most harmful to population health, which is tornadoes. However, flash flood and tstm wind also contribute significantly to economic losses. These findings underscore the importance of disaster preparedness and resource allocation to minimize the economic impacts of severe weather events.