Reproducible Research Project 2
Raamses
2022-12-16
Course Project 2
Reproducible Research Course Project 2
Synonpsis
Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severe events can result in fatalities, injuries, and property damage, and preventing such outcomes to the extent possible is a key concern.
This project involves exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. This database tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage.
The storm database includes weather events from 1950 through the year 2011 and contains data estimates such as the number fatalities and injuries for each weather event as well as economic cost damage to properties and crops for each weather event.
The estimates for fatalities and injuries were used to determine weather events with the most harmful impact to population health. Property damage and crop damage cost estimates were used to determine weather events with the greatest economic consequences.
Environment Setup
Load packages used in this analysis.
if (!require(ggplot2)) {
install.packages("ggplot2")
library(ggplot2)
}## Loading required package: ggplot2if (!require(dplyr)) {
install.packages("dplyr")
library(dplyr, warn.conflicts = FALSE)
}## Loading required package: 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, unionif (!require(xtable)) {
install.packages("xtable")
library(xtable, warn.conflicts = FALSE)
}## Loading required package: xtableDisplay session information.
sessionInfo()## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Linux Mint 20.3
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=es_VE.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=es_VE.UTF-8 LC_COLLATE=es_VE.UTF-8
## [5] LC_MONETARY=es_VE.UTF-8 LC_MESSAGES=es_VE.UTF-8
## [7] LC_PAPER=es_VE.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=es_VE.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] xtable_1.8-4 dplyr_1.0.9 ggplot2_3.3.6
##
## loaded via a namespace (and not attached):
## [1] bslib_0.4.1 compiler_3.6.3 pillar_1.7.0 jquerylib_0.1.4
## [5] tools_3.6.3 digest_0.6.29 jsonlite_1.8.0 evaluate_0.15
## [9] lifecycle_1.0.1 tibble_3.1.7 gtable_0.3.0 pkgconfig_2.0.3
## [13] rlang_1.0.2 DBI_1.1.2 cli_3.3.0 rstudioapi_0.13
## [17] yaml_2.3.5 xfun_0.31 fastmap_1.1.0 withr_2.5.0
## [21] stringr_1.4.0 knitr_1.39 generics_0.1.2 sass_0.4.2
## [25] vctrs_0.4.1 tidyselect_1.1.2 grid_3.6.3 glue_1.6.2
## [29] R6_2.5.1 fansi_1.0.3 rmarkdown_2.14 purrr_0.3.4
## [33] magrittr_2.0.3 scales_1.1.1 htmltools_0.5.2 ellipsis_0.3.2
## [37] assertthat_0.2.1 colorspace_2.0-3 utf8_1.2.2 stringi_1.7.8
## [41] munsell_0.5.0 cachem_1.0.6 crayon_1.5.1Load Data
Firstly, we found locally the compressed data file and then load the
compressed data file via read.csv. We validate the data
file and loaded dataset by checking the file size and dimensions
respectively.
setwd("/home/raamses/Descargas/Archivos Particulares/Books for Study/Computational Science/R/Course Project 2 Reproducible Research")
StormDataFile <- "repdata_data_StormData.csv.bz2"
stormData <- read.csv(StormDataFile, sep = ",", header = TRUE)
stopifnot(file.size(StormDataFile) == 49177144)
stopifnot(dim(stormData) == c(902297,37))The dataset summary is as follow
names(stormData)## [1] "STATE__" "BGN_DATE" "BGN_TIME" "TIME_ZONE" "COUNTY"
## [6] "COUNTYNAME" "STATE" "EVTYPE" "BGN_RANGE" "BGN_AZI"
## [11] "BGN_LOCATI" "END_DATE" "END_TIME" "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE" "END_AZI" "END_LOCATI" "LENGTH" "WIDTH"
## [21] "F" "MAG" "FATALITIES" "INJURIES" "PROPDMG"
## [26] "PROPDMGEXP" "CROPDMG" "CROPDMGEXP" "WFO" "STATEOFFIC"
## [31] "ZONENAMES" "LATITUDE" "LONGITUDE" "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS" "REFNUM"str(stormData)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : Factor w/ 16335 levels "1/1/1966 0:00:00",..: 6523 6523 4242 11116 2224 2224 2260 383 3980 3980 ...
## $ BGN_TIME : Factor w/ 3608 levels "00:00:00 AM",..: 272 287 2705 1683 2584 3186 242 1683 3186 3186 ...
## $ TIME_ZONE : Factor w/ 22 levels "ADT","AKS","AST",..: 7 7 7 7 7 7 7 7 7 7 ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: Factor w/ 29601 levels "","5NM E OF MACKINAC BRIDGE TO PRESQUE ISLE LT MI",..: 13513 1873 4598 10592 4372 10094 1973 23873 24418 4598 ...
## $ STATE : Factor w/ 72 levels "AK","AL","AM",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ EVTYPE : Factor w/ 985 levels " HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : Factor w/ 35 levels ""," N"," NW",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_LOCATI: Factor w/ 54429 levels ""," Christiansburg",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_DATE : Factor w/ 6663 levels "","1/1/1993 0:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_TIME : Factor w/ 3647 levels ""," 0900CST",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 : Factor w/ 24 levels "","E","ENE","ESE",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_LOCATI: Factor w/ 34506 levels ""," CANTON"," TULIA",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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: Factor w/ 19 levels "","-","?","+",..: 17 17 17 17 17 17 17 17 17 17 ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: Factor w/ 9 levels "","?","0","2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ WFO : Factor w/ 542 levels ""," CI","%SD",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ STATEOFFIC: Factor w/ 250 levels "","ALABAMA, Central",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ ZONENAMES : Factor w/ 25112 levels ""," "| __truncated__,..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 : Factor w/ 436781 levels "","\t","\t\t",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...head(stormData)Data Processing
In the case of processing a large dataset, compute performance can be
improved by taking a subset of the variables required for the analysis.
For the present analysis, the dataset will be trimmed to only include
the necessary variables (listed below). In addition, only observations
with value > 0 will be included.
| Variable | Characteristics |
|---|---|
| EVTYPE | Event type (Flood, Heat, Hurricane, Tornado, …) |
| FATALITIES | Number of fatalities resulting from event |
| INJURIES | Number of injuries resulting from event |
| PROPDMG | Property damage in USD |
| PROPDMGEXP | Unit multiplier for property damage (K, M, or B) |
| CROPDMG | Crop damage in USD |
| CROPDMGEXP | Unit multiplier for property damage (K, M, or B) |
| BGN_DATE | Begin date of the event |
| END_DATE | End date of the event |
| STATE | State where the event occurred |
stormDataTidy <- subset(stormData, EVTYPE != "?"
&
(FATALITIES > 0 | INJURIES > 0 | PROPDMG > 0 | CROPDMG > 0),
select = c("EVTYPE",
"FATALITIES",
"INJURIES",
"PROPDMG",
"PROPDMGEXP",
"CROPDMG",
"CROPDMGEXP",
"BGN_DATE",
"END_DATE",
"STATE"))
dim(stormDataTidy)## [1] 254632 10sum(is.na(stormDataTidy))## [1] 0Which yields 10 variables, a number of observations of 254632, and contains no missing values.
Clean Event Type Data
length(unique(stormDataTidy$EVTYPE))## [1] 487There are a total of 487 unique Event Type values in the current tidy dataset.
Now, exploring the Event Type data revealed many values that appeared
to be similar; nonetheless, they were entered with different spellings,
pluralization, mixed case and even misspellings. For example,
Strong Wind, STRONG WIND,
Strong Winds, and STRONG WINDS.
The dataset was normalized by converting all Event Type values to UPPERCASE and combining similar Event Type values into unique categories.
stormDataTidy$EVTYPE <- toupper(stormDataTidy$EVTYPE)# AVALANCHE
stormDataTidy$EVTYPE <- gsub('.*AVALANCE.*', 'AVALANCHE', stormDataTidy$EVTYPE)
# BLIZZARD
stormDataTidy$EVTYPE <- gsub('.*BLIZZARD.*', 'BLIZZARD', stormDataTidy$EVTYPE)
# CLOUD
stormDataTidy$EVTYPE <- gsub('.*CLOUD.*', 'CLOUD', stormDataTidy$EVTYPE)
# COLD
stormDataTidy$EVTYPE <- gsub('.*COLD.*', 'COLD', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*FREEZ.*', 'COLD', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*FROST.*', 'COLD', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*ICE.*', 'COLD', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*LOW TEMPERATURE RECORD.*', 'COLD', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*LO.*TEMP.*', 'COLD', stormDataTidy$EVTYPE)
# DRY
stormDataTidy$EVTYPE <- gsub('.*DRY.*', 'DRY', stormDataTidy$EVTYPE)
# DUST
stormDataTidy$EVTYPE <- gsub('.*DUST.*', 'DUST', stormDataTidy$EVTYPE)
# FIRE
stormDataTidy$EVTYPE <- gsub('.*FIRE.*', 'FIRE', stormDataTidy$EVTYPE)
# FLOOD
stormDataTidy$EVTYPE <- gsub('.*FLOOD.*', 'FLOOD', stormDataTidy$EVTYPE)
# FOG
stormDataTidy$EVTYPE <- gsub('.*FOG.*', 'FOG', stormDataTidy$EVTYPE)
# HAIL
stormDataTidy$EVTYPE <- gsub('.*HAIL.*', 'HAIL', stormDataTidy$EVTYPE)
# HEAT
stormDataTidy$EVTYPE <- gsub('.*HEAT.*', 'HEAT', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*WARM.*', 'HEAT', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*HIGH.*TEMP.*', 'HEAT', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*RECORD HIGH TEMPERATURES.*', 'HEAT', stormDataTidy$EVTYPE)
# HYPOTHERMIA/EXPOSURE
stormDataTidy$EVTYPE <- gsub('.*HYPOTHERMIA.*', 'HYPOTHERMIA/EXPOSURE', stormDataTidy$EVTYPE)
# LANDSLIDE
stormDataTidy$EVTYPE <- gsub('.*LANDSLIDE.*', 'LANDSLIDE', stormDataTidy$EVTYPE)
# LIGHTNING
stormDataTidy$EVTYPE <- gsub('^LIGHTNING.*', 'LIGHTNING', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('^LIGNTNING.*', 'LIGHTNING', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('^LIGHTING.*', 'LIGHTNING', stormDataTidy$EVTYPE)
# MICROBURST
stormDataTidy$EVTYPE <- gsub('.*MICROBURST.*', 'MICROBURST', stormDataTidy$EVTYPE)
# MUDSLIDE
stormDataTidy$EVTYPE <- gsub('.*MUDSLIDE.*', 'MUDSLIDE', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*MUD SLIDE.*', 'MUDSLIDE', stormDataTidy$EVTYPE)
# RAIN
stormDataTidy$EVTYPE <- gsub('.*RAIN.*', 'RAIN', stormDataTidy$EVTYPE)
# RIP CURRENT
stormDataTidy$EVTYPE <- gsub('.*RIP CURRENT.*', 'RIP CURRENT', stormDataTidy$EVTYPE)
# STORM
stormDataTidy$EVTYPE <- gsub('.*STORM.*', 'STORM', stormDataTidy$EVTYPE)
# SUMMARY
stormDataTidy$EVTYPE <- gsub('.*SUMMARY.*', 'SUMMARY', stormDataTidy$EVTYPE)
# TORNADO
stormDataTidy$EVTYPE <- gsub('.*TORNADO.*', 'TORNADO', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*TORNDAO.*', 'TORNADO', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*LANDSPOUT.*', 'TORNADO', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*WATERSPOUT.*', 'TORNADO', stormDataTidy$EVTYPE)
# SURF
stormDataTidy$EVTYPE <- gsub('.*SURF.*', 'SURF', stormDataTidy$EVTYPE)
# VOLCANIC
stormDataTidy$EVTYPE <- gsub('.*VOLCANIC.*', 'VOLCANIC', stormDataTidy$EVTYPE)
# WET
stormDataTidy$EVTYPE <- gsub('.*WET.*', 'WET', stormDataTidy$EVTYPE)
# WIND
stormDataTidy$EVTYPE <- gsub('.*WIND.*', 'WIND', stormDataTidy$EVTYPE)
# WINTER
stormDataTidy$EVTYPE <- gsub('.*WINTER.*', 'WINTER', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*WINTRY.*', 'WINTER', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*SNOW.*', 'WINTER', stormDataTidy$EVTYPE)length(unique(stormDataTidy$EVTYPE))## [1] 81After tidying the dataset, the number of unique Event Type values were reduced to 81.
Clean Date Data
Format date variables for any type of optional reporting or further analysis.
In the raw dataset, the BNG_START and
END_DATE variables are stored as factors which should be
made available as actual date types that can be manipulated and
reported on. In relation ot this, time variables will be ignored.
Creating four new variables based on date variables in the tidy dataset, gives us:
| Variable | Characteristics |
|---|---|
| DATE_START | Begin date of the event stored as a date type |
| DATE_END | End date of the event stored as a date type |
| YEAR | Year the event started |
| DURATION | Duration (in hours) of the event |
stormDataTidy$DATE_START <- as.Date(stormDataTidy$BGN_DATE, format = "%m/%d/%Y")
stormDataTidy$DATE_END <- as.Date(stormDataTidy$END_DATE, format = "%m/%d/%Y")
stormDataTidy$YEAR <- as.integer(format(stormDataTidy$DATE_START, "%Y"))
stormDataTidy$DURATION <- as.numeric(stormDataTidy$DATE_END - stormDataTidy$DATE_START)/3600Clean Economic Data
According to the source “National Weather Service [Storm Data
Documentation] in the page 12, information about Property Damage is
logged using two variables: PROPDMG and
PROPDMGEXP. PROPDMG is the mantissa (the
significant) rounded to three significant digits and
PROPDMGEXP is the exponent (the multiplier). The same
approach is used for Crop Damage where the CROPDMG variable
is encoded by the CROPDMGEXP variable.
The documentation also provide the detail that the
PROPDMGEXP and CROPDMGEXP are supposed to
contain an alphabetical character used to signify magnitude and logs “K”
for thousands, “M” for millions, and “B” for billions. A quick review of
the data, however, shows that there are several other characters being
logged.
table(toupper(stormDataTidy$PROPDMGEXP))##
## - + 0 2 3 4 5 6 7 B
## 11585 1 5 210 1 1 4 18 3 3 40
## H K M
## 7 231427 11327table(toupper(stormDataTidy$CROPDMGEXP))##
## ? 0 B K M
## 152663 6 17 7 99953 1986In order to calculate costs, the PROPDMGEXP and
CROPDMGEXP variables will be mapped to a multiplier factor
which will then be used to calculate the actual costs for both property
and crop damage. Two new variables will be created to store damage
costs:
- PROP_COST
- CROP_COST
# function to get multiplier factor
getMultiplier <- function(exp) {
exp <- toupper(exp);
if (exp == "") return (10^0);
if (exp == "-") return (10^0);
if (exp == "?") return (10^0);
if (exp == "+") return (10^0);
if (exp == "0") return (10^0);
if (exp == "1") return (10^1);
if (exp == "2") return (10^2);
if (exp == "3") return (10^3);
if (exp == "4") return (10^4);
if (exp == "5") return (10^5);
if (exp == "6") return (10^6);
if (exp == "7") return (10^7);
if (exp == "8") return (10^8);
if (exp == "9") return (10^9);
if (exp == "H") return (10^2);
if (exp == "K") return (10^3);
if (exp == "M") return (10^6);
if (exp == "B") return (10^9);
return (NA);
}
# Calculation of the property damage and crop damage costs (in billions)
stormDataTidy$PROP_COST <- with(stormDataTidy, as.numeric(PROPDMG) * sapply(PROPDMGEXP, getMultiplier))/10^9
stormDataTidy$CROP_COST <- with(stormDataTidy, as.numeric(CROPDMG) * sapply(CROPDMGEXP, getMultiplier))/10^9Summarize Data
Create a summarized dataset of health impact data (fatalities + injuries). Sort the results in descending order by health impact.
healthImpactData <- aggregate(x = list(HEALTH_IMPACT = stormDataTidy$FATALITIES + stormDataTidy$INJURIES),
by = list(EVENT_TYPE = stormDataTidy$EVTYPE),
FUN = sum,
na.rm = TRUE)
healthImpactData <- healthImpactData[order(healthImpactData$HEALTH_IMPACT, decreasing = TRUE),]Create a summarized dataset of damage impact costs (property damage + crop damage). Sort the results in descending order by damage cost.
damageCostImpactData <- aggregate(x = list(DAMAGE_IMPACT = stormDataTidy$PROP_COST + stormDataTidy$CROP_COST),
by = list(EVENT_TYPE = stormDataTidy$EVTYPE),
FUN = sum,
na.rm = TRUE)
damageCostImpactData <- damageCostImpactData[order(damageCostImpactData$DAMAGE_IMPACT, decreasing = TRUE),]Results
Event Types Most Harmful to Population Health
Fatalities and injuries have the most harmful impact on population health. The results below display the ten (10) most harmful weather events in terms of population health in the U.S country.
print(xtable(head(healthImpactData, 10),
caption = "Top 10 Weather Events Most Harmful to Population Health"),
caption.placement = 'top',
type = "html",
include.rownames = FALSE,
html.table.attributes='class="table-bordered", width="100%"')| EVENT_TYPE | HEALTH_IMPACT |
|---|---|
| TORNADO | 97075.00 |
| HEAT | 12392.00 |
| FLOOD | 10127.00 |
| WIND | 9893.00 |
| LIGHTNING | 6049.00 |
| STORM | 4780.00 |
| COLD | 3100.00 |
| WINTER | 1924.00 |
| FIRE | 1698.00 |
| HAIL | 1512.00 |
healthImpactChart <- ggplot(head(healthImpactData, 10),
aes(x = reorder(EVENT_TYPE, HEALTH_IMPACT), y = HEALTH_IMPACT, fill = EVENT_TYPE)) +
coord_flip() +
geom_bar(stat = "identity") +
xlab("Event Type") +
ylab("Total Fatalities and Injures") +
theme(plot.title = element_text(size = 14, hjust = 0.5)) +
ggtitle("Top 10 Weather Events Most Harmful to\nPopulation Health")
print(healthImpactChart)
Event Types with Greatest Economic Consequences
Property and crop damage have the most harmful impact on the economy. The results below display the ten (10) most harmful weather events in terms economic consequences in the U.S country.
print(xtable(head(damageCostImpactData, 10),
caption = "Top 10 Weather Events with Greatest Economic Consequences"),
caption.placement = 'top',
type = "html",
include.rownames = FALSE,
html.table.attributes='class="table-bordered", width="100%"')| EVENT_TYPE | DAMAGE_IMPACT |
|---|---|
| FLOOD | 180.58 |
| HURRICANE/TYPHOON | 71.91 |
| STORM | 70.45 |
| TORNADO | 57.43 |
| HAIL | 20.74 |
| DROUGHT | 15.02 |
| HURRICANE | 14.61 |
| COLD | 12.70 |
| WIND | 12.01 |
| FIRE | 8.90 |
damageCostImpactChart <- ggplot(head(damageCostImpactData, 10),
aes(x = reorder(EVENT_TYPE, DAMAGE_IMPACT), y = DAMAGE_IMPACT, fill = EVENT_TYPE)) +
coord_flip() +
geom_bar(stat = "identity") +
xlab("Event Type") +
ylab("Total Property / Crop Damage Cost\n(in Billions)") +
theme(plot.title = element_text(size = 14, hjust = 0.5)) +
ggtitle("Top 10 Weather Events with\nGreatest Economic Consequences")
print(damageCostImpactChart)
Conclusions
Based on the evidence and results demonstrated in this analysis and supported by the included data and graphs, the following conclusions can be drawn:
Which types of weather events have the greatest economic consequences?
Floods are responsible for causing the most property damage and crop damage costs.
Which types of weather events are most harmful to population health?
Tornadoes are responsible for the greatest number of fatalities and injuries.