NOAA Storm Data Analysis
David Carnahan
September 13, 2016
======================================================================
Synopsis
[1] In this analysis we will explore the NOAA Storm Database and answer basic questions about the economic and health impacts of severe weather events in the United States from 1950 to the present day. [2] Our overall hypothesis is that weather events that occur rapidly, and for which we have little to no warning system, will be more lethal, and weather events that last longer or happen more frequently would lead to greater economic damage. [3] We obtained data from the NOAA Storm Events Database. [4] From these data, we found that the greatest health impact over the total time period was strong wind events (such as Tornados), but this changed to heat events (fire) after the expansion of weather types in 1996. [5] The greatest economic damage was from water events (floods, etc) for property damage, and heat related events (fire) for crop damage indepdendent of the timeframe used.[6] Additionally, the regional variation was what would be expected – Texas, Louisianna, Florida – were most impacted by hurricane and flooding, while the mid-west – Texas, Oklahoma, Iowa, etc – were most impacted by Tornadoes. [7] Finally, there were several states that were in the top 10 highest impact for all three categories: Texas, Mississippi, Illinois, and Florida. [8] Our final analysis suggests some of our hypotheses are supported to some degree in that fires (since 1996) lead to the greatest health impact with loss of life and certainly tend to be unpredictable, rapidly changing, and spontaneous in nature, and weather event such as flooding and hurricanes, though predictable and able to warn large populations of their impending arrival, tend to cause the greatest economic damage due to the power and longevity of their presence.
Data Processing
Step 1: Download the data, and then read the csv file into the workspace.
url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
destfile <- "~/stormsData.csv.bz2"
download.file(url, destfile)
strm1 <- read.csv(destfile)Step 2: Take an initial look at the dataset.
str(strm1)## '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(strm1, 3)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 0
## 2 TORNADO 0 0
## 3 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14.0 100 3 0 0
## 2 NA 0 2.0 150 2 0 0
## 3 NA 0 0.1 123 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## 3 2 25.0 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2
## 3 3340 8742 0 0 3The dataset is a data.frame with over 900k observations and 39 variables. The variables are a mixture of factor, and numeric variables with one logical, date, and character variable.
Step 3: Transform date variables into date formats, and create a year variable.
strm1$BGN_DATE <- mdy_hms(strm1$BGN_DATE)
strm1$byear <- year(strm1$BGN_DATE)Step 4: Take a look at the distinct values of EVTYPE to determine the different types of weather events in the data.
unique(strm1$EVTYPE)## [1] TORNADO TSTM WIND
## [3] HAIL FREEZING RAIN
## [5] SNOW ICE STORM/FLASH FLOOD
## [7] SNOW/ICE WINTER STORM
## [9] HURRICANE OPAL/HIGH WINDS THUNDERSTORM WINDS
## [11] RECORD COLD HURRICANE ERIN
## [13] HURRICANE OPAL HEAVY RAIN
## [15] LIGHTNING THUNDERSTORM WIND
## [17] DENSE FOG RIP CURRENT
## [19] THUNDERSTORM WINS FLASH FLOOD
## [21] FLASH FLOODING HIGH WINDS
## [23] FUNNEL CLOUD TORNADO F0
## [25] THUNDERSTORM WINDS LIGHTNING THUNDERSTORM WINDS/HAIL
## [27] HEAT WIND
## [29] LIGHTING HEAVY RAINS
## [31] LIGHTNING AND HEAVY RAIN FUNNEL
## [33] WALL CLOUD FLOODING
## [35] THUNDERSTORM WINDS HAIL FLOOD
## [37] COLD HEAVY RAIN/LIGHTNING
## [39] FLASH FLOODING/THUNDERSTORM WI WALL CLOUD/FUNNEL CLOUD
## [41] THUNDERSTORM WATERSPOUT
## [43] EXTREME COLD HAIL 1.75)
## [45] LIGHTNING/HEAVY RAIN HIGH WIND
## [47] BLIZZARD BLIZZARD WEATHER
## [49] WIND CHILL BREAKUP FLOODING
## [51] HIGH WIND/BLIZZARD RIVER FLOOD
## [53] HEAVY SNOW FREEZE
## [55] COASTAL FLOOD HIGH WIND AND HIGH TIDES
## [57] HIGH WIND/BLIZZARD/FREEZING RA HIGH TIDES
## [59] HIGH WIND AND HEAVY SNOW RECORD COLD AND HIGH WIND
## [61] RECORD HIGH TEMPERATURE RECORD HIGH
## [63] HIGH WINDS HEAVY RAINS HIGH WIND/ BLIZZARD
## [65] ICE STORM BLIZZARD/HIGH WIND
## [67] HIGH WIND/LOW WIND CHILL HEAVY SNOW/HIGH
## [69] RECORD LOW HIGH WINDS AND WIND CHILL
## [71] HEAVY SNOW/HIGH WINDS/FREEZING LOW TEMPERATURE RECORD
## [73] AVALANCHE MARINE MISHAP
## [75] WIND CHILL/HIGH WIND HIGH WIND/WIND CHILL/BLIZZARD
## [77] HIGH WIND/WIND CHILL HIGH WIND/HEAVY SNOW
## [79] HIGH TEMPERATURE RECORD FLOOD WATCH/
## [81] RECORD HIGH TEMPERATURES HIGH WIND/SEAS
## [83] HIGH WINDS/HEAVY RAIN HIGH SEAS
## [85] SEVERE TURBULENCE RECORD RAINFALL
## [87] RECORD SNOWFALL RECORD WARMTH
## [89] HEAVY SNOW/WIND EXTREME HEAT
## [91] WIND DAMAGE DUST STORM
## [93] APACHE COUNTY SLEET
## [95] HAIL STORM FUNNEL CLOUDS
## [97] FLASH FLOODS DUST DEVIL
## [99] EXCESSIVE HEAT THUNDERSTORM WINDS/FUNNEL CLOU
## [101] WINTER STORM/HIGH WIND WINTER STORM/HIGH WINDS
## [103] GUSTY WINDS STRONG WINDS
## [105] FLOODING/HEAVY RAIN SNOW AND WIND
## [107] HEAVY SURF COASTAL FLOODING HEAVY SURF
## [109] HEAVY PRECIPATATION URBAN FLOODING
## [111] HIGH SURF BLOWING DUST
## [113] URBAN/SMALL WILD FIRES
## [115] HIGH URBAN/SMALL FLOODING
## [117] WATER SPOUT HIGH WINDS DUST STORM
## [119] WINTER STORM HIGH WINDS LOCAL FLOOD
## [121] WINTER STORMS MUDSLIDES
## [123] RAINSTORM SEVERE THUNDERSTORM
## [125] SEVERE THUNDERSTORMS SEVERE THUNDERSTORM WINDS
## [127] THUNDERSTORMS WINDS DRY MICROBURST
## [129] FLOOD/FLASH FLOOD FLOOD/RAIN/WINDS
## [131] WINDS DRY MICROBURST 61
## [133] THUNDERSTORMS FLASH FLOOD WINDS
## [135] URBAN/SMALL STREAM FLOODING MICROBURST
## [137] STRONG WIND HIGH WIND DAMAGE
## [139] STREAM FLOODING URBAN AND SMALL
## [141] HEAVY SNOWPACK ICE
## [143] FLASH FLOOD/ DOWNBURST
## [145] GUSTNADO AND FLOOD/RAIN/WIND
## [147] WET MICROBURST DOWNBURST WINDS
## [149] DRY MICROBURST WINDS DRY MIRCOBURST WINDS
## [151] DRY MICROBURST 53 SMALL STREAM URBAN FLOOD
## [153] MICROBURST WINDS HIGH WINDS 57
## [155] DRY MICROBURST 50 HIGH WINDS 66
## [157] HIGH WINDS 76 HIGH WINDS 63
## [159] HIGH WINDS 67 BLIZZARD/HEAVY SNOW
## [161] HEAVY SNOW/HIGH WINDS BLOWING SNOW
## [163] HIGH WINDS 82 HIGH WINDS 80
## [165] HIGH WINDS 58 FREEZING DRIZZLE
## [167] LIGHTNING THUNDERSTORM WINDSS DRY MICROBURST 58
## [169] HAIL 75 HIGH WINDS 73
## [171] HIGH WINDS 55 LIGHT SNOW AND SLEET
## [173] URBAN FLOOD DRY MICROBURST 84
## [175] THUNDERSTORM WINDS 60 HEAVY RAIN/FLOODING
## [177] THUNDERSTORM WINDSS TORNADOS
## [179] GLAZE RECORD HEAT
## [181] COASTAL FLOODING HEAT WAVE
## [183] FIRST SNOW FREEZING RAIN AND SLEET
## [185] UNSEASONABLY DRY UNSEASONABLY WET
## [187] WINTRY MIX WINTER WEATHER
## [189] UNSEASONABLY COLD EXTREME/RECORD COLD
## [191] RIP CURRENTS HEAVY SURF SLEET/RAIN/SNOW
## [193] UNSEASONABLY WARM DROUGHT
## [195] NORMAL PRECIPITATION HIGH WINDS/FLOODING
## [197] DRY RAIN/SNOW
## [199] SNOW/RAIN/SLEET WATERSPOUT/TORNADO
## [201] WATERSPOUTS WATERSPOUT TORNADO
## [203] URBAN/SMALL STREAM FLOOD STORM SURGE
## [205] WATERSPOUT-TORNADO WATERSPOUT-
## [207] TORNADOES, TSTM WIND, HAIL TROPICAL STORM ALBERTO
## [209] TROPICAL STORM TROPICAL STORM GORDON
## [211] TROPICAL STORM JERRY LIGHTNING THUNDERSTORM WINDS
## [213] WAYTERSPOUT MINOR FLOODING
## [215] LIGHTNING INJURY URBAN/SMALL STREAM FLOOD
## [217] LIGHTNING AND THUNDERSTORM WIN THUNDERSTORM WINDS53
## [219] URBAN AND SMALL STREAM FLOOD URBAN AND SMALL STREAM
## [221] WILDFIRE DAMAGING FREEZE
## [223] THUNDERSTORM WINDS 13 SMALL HAIL
## [225] HEAVY SNOW/HIGH WIND HURRICANE
## [227] WILD/FOREST FIRE SMALL STREAM FLOODING
## [229] MUD SLIDE LIGNTNING
## [231] FROST FREEZING RAIN/SNOW
## [233] HIGH WINDS/ THUNDERSNOW
## [235] FLOODS EXTREME WIND CHILLS
## [237] COOL AND WET HEAVY RAIN/SNOW
## [239] SMALL STREAM AND URBAN FLOODIN SMALL STREAM/URBAN FLOOD
## [241] SNOW/SLEET/FREEZING RAIN SEVERE COLD
## [243] GLAZE ICE COLD WAVE
## [245] EARLY SNOW SMALL STREAM AND URBAN FLOOD
## [247] HIGH WINDS RURAL FLOOD
## [249] SMALL STREAM AND MUD SLIDES
## [251] HAIL 80 EXTREME WIND CHILL
## [253] COLD AND WET CONDITIONS EXCESSIVE WETNESS
## [255] GRADIENT WINDS HEAVY SNOW/BLOWING SNOW
## [257] SLEET/ICE STORM THUNDERSTORM WINDS URBAN FLOOD
## [259] THUNDERSTORM WINDS SMALL STREA ROTATING WALL CLOUD
## [261] LARGE WALL CLOUD COLD AIR FUNNEL
## [263] GUSTNADO COLD AIR FUNNELS
## [265] BLOWING SNOW- EXTREME WIND CHI SNOW AND HEAVY SNOW
## [267] GROUND BLIZZARD MAJOR FLOOD
## [269] SNOW/HEAVY SNOW FREEZING RAIN/SLEET
## [271] ICE JAM FLOODING SNOW- HIGH WIND- WIND CHILL
## [273] STREET FLOOD COLD AIR TORNADO
## [275] SMALL STREAM FLOOD FOG
## [277] THUNDERSTORM WINDS 2 FUNNEL CLOUD/HAIL
## [279] ICE/SNOW TSTM WIND 51
## [281] TSTM WIND 50 TSTM WIND 52
## [283] TSTM WIND 55 HEAVY SNOW/BLIZZARD
## [285] THUNDERSTORM WINDS 61 HAIL 0.75
## [287] THUNDERSTORM DAMAGE THUNDERTORM WINDS
## [289] HAIL 1.00 HAIL/WINDS
## [291] SNOW AND ICE WIND STORM
## [293] SNOWSTORM GRASS FIRES
## [295] LAKE FLOOD PROLONG COLD
## [297] HAIL/WIND HAIL 1.75
## [299] THUNDERSTORMW 50 WIND/HAIL
## [301] SNOW AND ICE STORM URBAN AND SMALL STREAM FLOODIN
## [303] THUNDERSTORMS WIND THUNDERSTORM WINDS
## [305] HEAVY SNOW/SLEET AGRICULTURAL FREEZE
## [307] DROUGHT/EXCESSIVE HEAT TUNDERSTORM WIND
## [309] TROPICAL STORM DEAN THUNDERTSORM WIND
## [311] THUNDERSTORM WINDS/ HAIL THUNDERSTORM WIND/LIGHTNING
## [313] HEAVY RAIN/SEVERE WEATHER THUNDESTORM WINDS
## [315] WATERSPOUT/ TORNADO LIGHTNING.
## [317] WARM DRY CONDITIONS HURRICANE-GENERATED SWELLS
## [319] HEAVY SNOW/ICE STORM RIVER AND STREAM FLOOD
## [321] HIGH WIND 63 COASTAL SURGE
## [323] HEAVY SNOW AND ICE STORM MINOR FLOOD
## [325] HIGH WINDS/COASTAL FLOOD RAIN
## [327] RIVER FLOODING SNOW/RAIN
## [329] ICE FLOES HIGH WAVES
## [331] SNOW SQUALLS SNOW SQUALL
## [333] THUNDERSTORM WIND G50 LIGHTNING FIRE
## [335] BLIZZARD/FREEZING RAIN HEAVY LAKE SNOW
## [337] HEAVY SNOW/FREEZING RAIN LAKE EFFECT SNOW
## [339] HEAVY WET SNOW DUST DEVIL WATERSPOUT
## [341] THUNDERSTORM WINDS/HEAVY RAIN THUNDERSTROM WINDS
## [343] THUNDERSTORM WINDS LE CEN HAIL 225
## [345] BLIZZARD AND HEAVY SNOW HEAVY SNOW AND ICE
## [347] ICE STORM AND SNOW HEAVY SNOW ANDBLOWING SNOW
## [349] HEAVY SNOW/ICE BLIZZARD AND EXTREME WIND CHIL
## [351] LOW WIND CHILL BLOWING SNOW & EXTREME WIND CH
## [353] WATERSPOUT/ URBAN/SMALL STREAM
## [355] TORNADO F3 FUNNEL CLOUD.
## [357] TORNDAO HAIL 0.88
## [359] FLOOD/RIVER FLOOD MUD SLIDES URBAN FLOODING
## [361] TORNADO F1 THUNDERSTORM WINDS G
## [363] DEEP HAIL GLAZE/ICE STORM
## [365] HEAVY SNOW/WINTER STORM AVALANCE
## [367] BLIZZARD/WINTER STORM DUST STORM/HIGH WINDS
## [369] ICE JAM FOREST FIRES
## [371] THUNDERSTORM WIND G60 FROST\\FREEZE
## [373] THUNDERSTORM WINDS. HAIL 88
## [375] HAIL 175 HVY RAIN
## [377] HAIL 100 HAIL 150
## [379] HAIL 075 THUNDERSTORM WIND G55
## [381] HAIL 125 THUNDERSTORM WINDS G60
## [383] HARD FREEZE HAIL 200
## [385] THUNDERSTORM WINDS FUNNEL CLOU THUNDERSTORM WINDS 62
## [387] WILDFIRES RECORD HEAT WAVE
## [389] HEAVY SNOW AND HIGH WINDS HEAVY SNOW/HIGH WINDS & FLOOD
## [391] HAIL FLOODING THUNDERSTORM WINDS/FLASH FLOOD
## [393] HIGH WIND 70 WET SNOW
## [395] HEAVY RAIN AND FLOOD LOCAL FLASH FLOOD
## [397] THUNDERSTORM WINDS 53 FLOOD/FLASH FLOODING
## [399] TORNADO/WATERSPOUT RAIN AND WIND
## [401] THUNDERSTORM WIND 59 THUNDERSTORM WIND 52
## [403] COASTAL/TIDAL FLOOD SNOW/ICE STORM
## [405] BELOW NORMAL PRECIPITATION RIP CURRENTS/HEAVY SURF
## [407] FLASH FLOOD/FLOOD EXCESSIVE RAIN
## [409] RECORD/EXCESSIVE HEAT HEAT WAVES
## [411] LIGHT SNOW THUNDERSTORM WIND 69
## [413] HAIL DAMAGE LIGHTNING DAMAGE
## [415] RECORD TEMPERATURES LIGHTNING AND WINDS
## [417] FOG AND COLD TEMPERATURES OTHER
## [419] RECORD SNOW SNOW/COLD
## [421] FLASH FLOOD FROM ICE JAMS TSTM WIND G58
## [423] MUDSLIDE HEAVY SNOW SQUALLS
## [425] HEAVY SNOW/SQUALLS HEAVY SNOW-SQUALLS
## [427] ICY ROADS HEAVY MIX
## [429] SNOW FREEZING RAIN LACK OF SNOW
## [431] SNOW/SLEET SNOW/FREEZING RAIN
## [433] SNOW DROUGHT THUNDERSTORMW WINDS
## [435] THUNDERSTORM WIND 60 MPH THUNDERSTORM WIND 65MPH
## [437] THUNDERSTORM WIND/ TREES THUNDERSTORM WIND/AWNING
## [439] THUNDERSTORM WIND 98 MPH THUNDERSTORM WIND TREES
## [441] TORRENTIAL RAIN TORNADO F2
## [443] RIP CURRENTS HURRICANE EMILY
## [445] HURRICANE GORDON HURRICANE FELIX
## [447] THUNDERSTORM WIND 59 MPH THUNDERSTORM WINDS 63 MPH
## [449] THUNDERSTORM WIND/ TREE THUNDERSTORM DAMAGE TO
## [451] THUNDERSTORM WIND 65 MPH FLASH FLOOD - HEAVY RAIN
## [453] THUNDERSTORM WIND. FLASH FLOOD/ STREET
## [455] THUNDERSTORM WIND 59 MPH. HEAVY SNOW FREEZING RAIN
## [457] DAM FAILURE THUNDERSTORM HAIL
## [459] HAIL 088 THUNDERSTORM WINDSHAIL
## [461] LIGHTNING WAUSEON THUDERSTORM WINDS
## [463] ICE AND SNOW RECORD COLD/FROST
## [465] STORM FORCE WINDS FREEZING RAIN AND SNOW
## [467] FREEZING RAIN SLEET AND SOUTHEAST
## [469] HEAVY SNOW & ICE FREEZING DRIZZLE AND FREEZING
## [471] THUNDERSTORM WINDS AND HAIL/ICY ROADS
## [473] FLASH FLOOD/HEAVY RAIN HEAVY RAIN; URBAN FLOOD WINDS;
## [475] HEAVY PRECIPITATION TSTM WIND DAMAGE
## [477] HIGH WATER FLOOD FLASH
## [479] RAIN/WIND THUNDERSTORM WINDS 50
## [481] THUNDERSTORM WIND G52 FLOOD FLOOD/FLASH
## [483] THUNDERSTORM WINDS 52 SNOW SHOWERS
## [485] THUNDERSTORM WIND G51 HEAT WAVE DROUGHT
## [487] HEAVY SNOW/BLIZZARD/AVALANCHE RECORD SNOW/COLD
## [489] WET WEATHER UNSEASONABLY WARM AND DRY
## [491] FREEZING RAIN SLEET AND LIGHT RECORD/EXCESSIVE RAINFALL
## [493] TIDAL FLOOD BEACH EROSIN
## [495] THUNDERSTORM WIND G61 FLOOD/FLASH
## [497] LOW TEMPERATURE SLEET & FREEZING RAIN
## [499] HEAVY RAINS/FLOODING THUNDERESTORM WINDS
## [501] THUNDERSTORM WINDS/FLOODING THUNDEERSTORM WINDS
## [503] HIGHWAY FLOODING THUNDERSTORM W INDS
## [505] HYPOTHERMIA FLASH FLOOD/ FLOOD
## [507] THUNDERSTORM WIND 50 THUNERSTORM WINDS
## [509] HEAVY RAIN/MUDSLIDES/FLOOD MUD/ROCK SLIDE
## [511] HIGH WINDS/COLD BEACH EROSION/COASTAL FLOOD
## [513] COLD/WINDS SNOW/ BITTER COLD
## [515] THUNDERSTORM WIND 56 SNOW SLEET
## [517] DRY HOT WEATHER COLD WEATHER
## [519] RAPIDLY RISING WATER HAIL ALOFT
## [521] EARLY FREEZE ICE/STRONG WINDS
## [523] EXTREME WIND CHILL/BLOWING SNO SNOW/HIGH WINDS
## [525] HIGH WINDS/SNOW EARLY FROST
## [527] SNOWMELT FLOODING HEAVY SNOW AND STRONG WINDS
## [529] SNOW ACCUMULATION BLOWING SNOW/EXTREME WIND CHIL
## [531] SNOW/ ICE SNOW/BLOWING SNOW
## [533] TORNADOES THUNDERSTORM WIND/HAIL
## [535] FLASH FLOODING/FLOOD HAIL 275
## [537] HAIL 450 FLASH FLOOODING
## [539] EXCESSIVE RAINFALL THUNDERSTORMW
## [541] HAILSTORM TSTM WINDS
## [543] BEACH FLOOD HAILSTORMS
## [545] TSTMW FUNNELS
## [547] TSTM WIND 65) THUNDERSTORM WINDS/ FLOOD
## [549] HEAVY RAINFALL HEAT/DROUGHT
## [551] HEAT DROUGHT NEAR RECORD SNOW
## [553] LANDSLIDE HIGH WIND AND SEAS
## [555] THUNDERSTORMWINDS THUNDERSTORM WINDS HEAVY RAIN
## [557] SLEET/SNOW EXCESSIVE
## [559] SNOW/SLEET/RAIN WILD/FOREST FIRES
## [561] HEAVY SEAS DUSTSTORM
## [563] FLOOD & HEAVY RAIN ?
## [565] THUNDERSTROM WIND FLOOD/FLASHFLOOD
## [567] SNOW AND COLD HOT PATTERN
## [569] PROLONG COLD/SNOW BRUSH FIRES
## [571] SNOW\\COLD WINTER MIX
## [573] EXCESSIVE PRECIPITATION SNOWFALL RECORD
## [575] HOT/DRY PATTERN DRY PATTERN
## [577] MILD/DRY PATTERN MILD PATTERN
## [579] LANDSLIDES HEAVY SHOWERS
## [581] HEAVY SNOW AND HIGH WIND 48
## [583] LAKE-EFFECT SNOW BRUSH FIRE
## [585] WATERSPOUT FUNNEL CLOUD URBAN SMALL STREAM FLOOD
## [587] SAHARAN DUST HEAVY SHOWER
## [589] URBAN FLOOD LANDSLIDE HEAVY SWELLS
## [591] URBAN SMALL URBAN FLOODS
## [593] SMALL STREAM HEAVY RAIN/URBAN FLOOD
## [595] FLASH FLOOD/LANDSLIDE LANDSLIDE/URBAN FLOOD
## [597] HEAVY RAIN/SMALL STREAM URBAN FLASH FLOOD LANDSLIDES
## [599] EXTREME WINDCHILL URBAN/SML STREAM FLD
## [601] TSTM WIND/HAIL Other
## [603] Record dry month Temperature record
## [605] Minor Flooding Ice jam flood (minor
## [607] High Wind Tstm Wind
## [609] ROUGH SURF Wind
## [611] Heavy Surf Dust Devil
## [613] Wind Damage Marine Accident
## [615] Snow Freeze
## [617] Snow Squalls Coastal Flooding
## [619] Heavy Rain Strong Wind
## [621] COASTAL STORM COASTALFLOOD
## [623] Erosion/Cstl Flood Heavy Rain and Wind
## [625] Light Snow/Flurries Wet Month
## [627] Wet Year Tidal Flooding
## [629] River Flooding Damaging Freeze
## [631] Beach Erosion Hot and Dry
## [633] Flood/Flash Flood Icy Roads
## [635] High Surf Heavy Rain/High Surf
## [637] Thunderstorm Wind Rain Damage
## [639] Unseasonable Cold Early Frost
## [641] Wintry Mix blowing snow
## [643] STREET FLOODING Record Cold
## [645] Extreme Cold Ice Fog
## [647] Excessive Cold Torrential Rainfall
## [649] Freezing Rain Landslump
## [651] Late-season Snowfall Hurricane Edouard
## [653] Coastal Storm Flood
## [655] HEAVY RAIN/WIND TIDAL FLOODING
## [657] Winter Weather Snow squalls
## [659] Strong Winds Strong winds
## [661] RECORD WARM TEMPS. Ice/Snow
## [663] Mudslide Glaze
## [665] Extended Cold Snow Accumulation
## [667] Freezing Fog Drifting Snow
## [669] Whirlwind Heavy snow shower
## [671] Heavy rain LATE SNOW
## [673] Record May Snow Record Winter Snow
## [675] Heavy Precipitation COASTAL FLOOD
## [677] Record temperature Light snow
## [679] Late Season Snowfall Gusty Wind
## [681] small hail Light Snow
## [683] MIXED PRECIP Black Ice
## [685] Mudslides Gradient wind
## [687] Snow and Ice Freezing Spray
## [689] Summary Jan 17 Summary of March 14
## [691] Summary of March 23 Summary of March 24
## [693] Summary of April 3rd Summary of April 12
## [695] Summary of April 13 Summary of April 21
## [697] Summary August 11 Summary of April 27
## [699] Summary of May 9-10 Summary of May 10
## [701] Summary of May 13 Summary of May 14
## [703] Summary of May 22 am Summary of May 22 pm
## [705] Heatburst Summary of May 26 am
## [707] Summary of May 26 pm Metro Storm, May 26
## [709] Summary of May 31 am Summary of May 31 pm
## [711] Summary of June 3 Summary of June 4
## [713] Summary June 5-6 Summary June 6
## [715] Summary of June 11 Summary of June 12
## [717] Summary of June 13 Summary of June 15
## [719] Summary of June 16 Summary June 18-19
## [721] Summary of June 23 Summary of June 24
## [723] Summary of June 30 Summary of July 2
## [725] Summary of July 3 Summary of July 11
## [727] Summary of July 22 Summary July 23-24
## [729] Summary of July 26 Summary of July 29
## [731] Summary of August 1 Summary August 2-3
## [733] Summary August 7 Summary August 9
## [735] Summary August 10 Summary August 17
## [737] Summary August 21 Summary August 28
## [739] Summary September 4 Summary September 20
## [741] Summary September 23 Summary Sept. 25-26
## [743] Summary: Oct. 20-21 Summary: October 31
## [745] Summary: Nov. 6-7 Summary: Nov. 16
## [747] Microburst wet micoburst
## [749] Hail(0.75) Funnel Cloud
## [751] Urban Flooding No Severe Weather
## [753] Urban flood Urban Flood
## [755] Cold Summary of May 22
## [757] Summary of June 6 Summary August 4
## [759] Summary of June 10 Summary of June 18
## [761] Summary September 3 Summary: Sept. 18
## [763] Coastal Flood coastal flooding
## [765] Small Hail Record Temperatures
## [767] Light Snowfall Freezing Drizzle
## [769] Gusty wind/rain GUSTY WIND/HVY RAIN
## [771] Blowing Snow Early snowfall
## [773] Monthly Snowfall Record Heat
## [775] Seasonal Snowfall Monthly Rainfall
## [777] Cold Temperature Sml Stream Fld
## [779] Heat Wave MUDSLIDE/LANDSLIDE
## [781] Saharan Dust Volcanic Ash
## [783] Volcanic Ash Plume Thundersnow shower
## [785] NONE COLD AND SNOW
## [787] DAM BREAK TSTM WIND (G45)
## [789] SLEET/FREEZING RAIN BLACK ICE
## [791] BLOW-OUT TIDES UNSEASONABLY COOL
## [793] TSTM HEAVY RAIN Gusty Winds
## [795] GUSTY WIND TSTM WIND 40
## [797] TSTM WIND 45 TSTM WIND (41)
## [799] TSTM WIND (G40) TSTM WND
## [801] Wintry mix TSTM WIND
## [803] Frost Frost/Freeze
## [805] RAIN (HEAVY) Record Warmth
## [807] Prolong Cold Cold and Frost
## [809] URBAN/SML STREAM FLDG STRONG WIND GUST
## [811] LATE FREEZE BLOW-OUT TIDE
## [813] Hypothermia/Exposure HYPOTHERMIA/EXPOSURE
## [815] Lake Effect Snow Mixed Precipitation
## [817] Record High COASTALSTORM
## [819] Snow and sleet Freezing rain
## [821] Gusty winds Blizzard Summary
## [823] SUMMARY OF MARCH 24-25 SUMMARY OF MARCH 27
## [825] SUMMARY OF MARCH 29 GRADIENT WIND
## [827] Icestorm/Blizzard Flood/Strong Wind
## [829] TSTM WIND AND LIGHTNING gradient wind
## [831] Freezing drizzle Mountain Snows
## [833] URBAN/SMALL STRM FLDG Heavy surf and wind
## [835] Mild and Dry Pattern COLD AND FROST
## [837] TYPHOON HIGH SWELLS
## [839] HIGH SWELLS VOLCANIC ASH
## [841] DRY SPELL LIGHTNING
## [843] BEACH EROSION UNSEASONAL RAIN
## [845] EARLY RAIN PROLONGED RAIN
## [847] WINTERY MIX COASTAL FLOODING/EROSION
## [849] HOT SPELL UNSEASONABLY HOT
## [851] TSTM WIND (G45) TSTM WIND (G45)
## [853] HIGH WIND (G40) TSTM WIND (G35)
## [855] DRY WEATHER ABNORMAL WARMTH
## [857] UNUSUAL WARMTH WAKE LOW WIND
## [859] MONTHLY RAINFALL COLD TEMPERATURES
## [861] COLD WIND CHILL TEMPERATURES MODERATE SNOW
## [863] MODERATE SNOWFALL URBAN/STREET FLOODING
## [865] COASTAL EROSION UNUSUAL/RECORD WARMTH
## [867] BITTER WIND CHILL BITTER WIND CHILL TEMPERATURES
## [869] SEICHE TSTM
## [871] COASTAL FLOODING/EROSION UNSEASONABLY WARM YEAR
## [873] HYPERTHERMIA/EXPOSURE ROCK SLIDE
## [875] ICE PELLETS PATCHY DENSE FOG
## [877] RECORD COOL RECORD WARM
## [879] HOT WEATHER RECORD TEMPERATURE
## [881] TROPICAL DEPRESSION VOLCANIC ERUPTION
## [883] COOL SPELL WIND ADVISORY
## [885] GUSTY WIND/HAIL RED FLAG FIRE WX
## [887] FIRST FROST EXCESSIVELY DRY
## [889] SNOW AND SLEET LIGHT SNOW/FREEZING PRECIP
## [891] VOG MONTHLY PRECIPITATION
## [893] MONTHLY TEMPERATURE RECORD DRYNESS
## [895] EXTREME WINDCHILL TEMPERATURES MIXED PRECIPITATION
## [897] DRY CONDITIONS REMNANTS OF FLOYD
## [899] EARLY SNOWFALL FREEZING FOG
## [901] LANDSPOUT DRIEST MONTH
## [903] RECORD COLD LATE SEASON HAIL
## [905] EXCESSIVE SNOW DRYNESS
## [907] FLOOD/FLASH/FLOOD WIND AND WAVE
## [909] LIGHT FREEZING RAIN WIND
## [911] MONTHLY SNOWFALL RECORD PRECIPITATION
## [913] ICE ROADS ROUGH SEAS
## [915] UNSEASONABLY WARM/WET UNSEASONABLY COOL & WET
## [917] UNUSUALLY WARM TSTM WIND G45
## [919] NON SEVERE HAIL NON-SEVERE WIND DAMAGE
## [921] UNUSUALLY COLD WARM WEATHER
## [923] LANDSLUMP THUNDERSTORM WIND (G40)
## [925] UNSEASONABLY WARM & WET FLASH FLOOD
## [927] LOCALLY HEAVY RAIN WIND GUSTS
## [929] UNSEASONAL LOW TEMP HIGH SURF ADVISORY
## [931] LATE SEASON SNOW GUSTY LAKE WIND
## [933] ABNORMALLY DRY WINTER WEATHER MIX
## [935] RED FLAG CRITERIA WND
## [937] CSTL FLOODING/EROSION SMOKE
## [939] WATERSPOUT SNOW ADVISORY
## [941] EXTREMELY WET UNUSUALLY LATE SNOW
## [943] VERY DRY RECORD LOW RAINFALL
## [945] ROGUE WAVE PROLONG WARMTH
## [947] ACCUMULATED SNOWFALL FALLING SNOW/ICE
## [949] DUST DEVEL NON-TSTM WIND
## [951] NON TSTM WIND GUSTY THUNDERSTORM WINDS
## [953] PATCHY ICE HEAVY RAIN EFFECTS
## [955] EXCESSIVE HEAT/DROUGHT NORTHERN LIGHTS
## [957] MARINE TSTM WIND HIGH SURF ADVISORY
## [959] HAZARDOUS SURF FROST/FREEZE
## [961] WINTER WEATHER/MIX ASTRONOMICAL HIGH TIDE
## [963] WHIRLWIND VERY WARM
## [965] ABNORMALLY WET TORNADO DEBRIS
## [967] EXTREME COLD/WIND CHILL ICE ON ROAD
## [969] DROWNING GUSTY THUNDERSTORM WIND
## [971] MARINE HAIL HIGH SURF ADVISORIES
## [973] HURRICANE/TYPHOON HEAVY SURF/HIGH SURF
## [975] SLEET STORM STORM SURGE/TIDE
## [977] COLD/WIND CHILL MARINE HIGH WIND
## [979] TSUNAMI DENSE SMOKE
## [981] LAKESHORE FLOOD MARINE THUNDERSTORM WIND
## [983] MARINE STRONG WIND ASTRONOMICAL LOW TIDE
## [985] VOLCANIC ASHFALL
## 985 Levels: HIGH SURF ADVISORY COASTAL FLOOD ... WNDStep 5: Since there are 985 unique weather events, we’ll need to create a categorical variable that collapses similar types of weather events together to better evaluate the economic and health impact of these different types of storms.
I chose to create six types of weather events:
- Those related to water events that occur on dry land,
- Those related to cold weather events,
- Those related to heat related events or fires,
- Those related to strong winds that occur on dry land,
- Those related to strong winds that occur over a body of water, and finally
- Everything else.
strm1$EVTYPE <- tolower(strm1$EVTYPE)
## create search term variables
water <- "flood|rain|thunder|surge|rising water|wet|dam break"
cold <- "winter|cold|ice|icy|wintry|blizzard|snow|sleet|hail|hypothermia|freezing|windchill|frost|avalanche"
heat <- "heat|fire|dry|driest|warm|hyperthermia|hot|drought"
wind_land <- "high wind|strong wind|tornado|funnell|gustnado"
wind_water <- "hurricane|waterspout|tropical|tstm|swells|seas|surf|waves|current"
## create variable and then select variables for final dataset
strm2 <- strm1 %>%
mutate(evtCat = ifelse(grepl(water, strm1$EVTYPE), "water",
ifelse(grepl(cold, strm1$EVTYPE), "cold",
ifelse(grepl(heat, strm1$EVTYPE), "heat",
ifelse(grepl(wind_land, strm1$EVTYPE), "wind_land",
ifelse(grepl(wind_water, strm1$EVTYPE), "wind_water", "other")))))) %>%
select(BGN_DATE, byear, EVTYPE, evtCat, FATALITIES, INJURIES, STATE, CROPDMG, CROPDMGEXP, PROPDMG, PROPDMGEXP, LATITUDE, LONGITUDE)Step 6: Transform the economic variables (crop damage, and property damage) into numerical variables with appropriate scale. In other words, turn k into 1,000, m into 1,000,000 and b into 1,000,000,000.
strm2b <- strm2 %>%
## because integer length will be too large ... I will cut off 1000 from the conversion and denote in graphs
mutate(crop_exp = ifelse(CROPDMGEXP == "K", "",
ifelse(CROPDMGEXP == "M", "000",
ifelse(CROPDMGEXP == "B", "000000", "0"))),
prop_exp = ifelse(PROPDMGEXP == "K", "",
ifelse(PROPDMGEXP == "M", "000",
ifelse(PROPDMGEXP == "B", "000000", "0"))),
CROPDMG = as.integer(CROPDMG),
PROPDMG = as.integer(PROPDMG),
cropDmg = paste0(as.character(CROPDMG), crop_exp),
propDmg = paste0(as.character(PROPDMG), prop_exp),
cropDmg = as.integer(cropDmg),
propDmg = as.integer(propDmg)) %>%
select(byear, evtCat, FATALITIES, INJURIES, STATE, cropDmg, propDmg, LATITUDE, LONGITUDE)Data Analysis / Results
Step 1: Create a summary dataset by the outcomes of interest (mortality, property damage, and crop damage) by weather event types, and then display result via a frequency table.
## create a summary table by year and event type
strm3 <- strm2b %>%
group_by(byear, evtCat) %>%
summarise(sumMort = sum(FATALITIES), sumCropDmg = sum(cropDmg), sumPropDmg = sum(propDmg)) %>%
as.data.frame(select(byear, evtCat, sumMort, sumCropDmg, sumPropDmg))
## frequency table by event type
sumTable <- strm3 %>%
group_by(evtCat) %>%
select(evtCat, sumMort, sumCropDmg, sumPropDmg) %>%
summarise(mortality = sum(sumMort), cropDmg = sum(sumCropDmg), propDmg = sum(sumPropDmg))
## show results
sumTable## # A tibble: 6 × 4
## evtCat mortality cropDmg propDmg
## <chr> <dbl> <int> <int>
## 1 cold 1381 10932889 28688656
## 2 heat 3272 14351603 8980356
## 3 other 1091 631262 2091747
## 4 water 1875 13998993 222896350
## 5 wind_land 6054 1164549 59990676
## 6 wind_water 1472 6200388 91376195Looking at the frequency table for weather events, strong wind events over land account for the greatest loss in life (6054), water events that occur on dry land account for the greatest loss in property (over 222 billion), while heat related weather events account for the greatest loss of crops (14 billion).
Step 2: Plot mortality by weather type over time.
## graph line plot with event type coloring the dots
g <- ggplot(strm3, aes(byear, sumMort))
g <- g + geom_line(aes(color = evtCat))
g <- g + geom_point()
g <- g + labs(x = "Year of Event", y = "Mortality", title = "Storm Mortality Rate Over Time By Type of Storm")
g
This figure suggests that there may have been different data collection methods pre-1960 to mid-1990s. In researching this dataset, it appears there was a significant expansion of weather event types in 1996. Furthermore, it appears that heat related events may have a higher mortality rate than strong wind events after 1996, which is a change from our previous results.
Step 3: Relook at the frequency table filtered by years on or after 1996 to determine if mortality from heat related events are greater than strong winds related events after 1996.
## frequency table by event type
sum1996 <- strm3 %>%
filter(byear >= 1996) %>%
group_by(evtCat) %>%
select(evtCat, sumMort, sumCropDmg, sumPropDmg) %>%
summarise(mortality = sum(sumMort), cropDmg = sum(sumCropDmg), propDmg = sum(sumPropDmg))
## show results
sum1996## # A tibble: 6 × 4
## evtCat mortality cropDmg propDmg
## <chr> <dbl> <int> <int>
## 1 cold 1162 5111124 20107331
## 2 heat 2127 14236300 8224410
## 3 other 873 264380 1892894
## 4 water 1564 7431682 209915071
## 5 wind_land 1871 971121 28122487
## 6 wind_water 1135 6037001 87871565Our hypothesis is confirmed. Additionally, it appears the weather types responsible for the greatest crop and property damage remain the same (heat for crop damage, and water for property damage).
| Greatest Category | All Data | Post 1996 |
|---|---|---|
| Mortality | Wind_Land | Heat |
| Property Damage | Water | Water |
| Crop Damage | Heat | Heat |
Step 4: Look at health, and economic impact of weather event types by states. Specifically, determine the top 10 states for each category (mortality, crop damage, property damage).
## create a summary table by state and event type
strmState <- strm2b %>%
group_by(STATE, evtCat) %>%
summarise(sumMort = sum(FATALITIES), sumCropDmg = sum(cropDmg), sumPropDmg = sum(propDmg)) %>%
as.data.frame(select(STATE, evtCat, sumMort, sumCropDmg, sumPropDmg))
## list states with the highest mortality, crop damage, and property damage by event type
mortState <- strmState[order(desc(strmState$sumMort)), ]
cropState <- strmState[order(desc(strmState$sumCropDmg)), ]
propState <- strmState[order(desc(strmState$sumPropDmg)), ]
## mortality results
head(mortState, 10)## STATE evtCat sumMort sumCropDmg sumPropDmg
## 108 IL heat 983 284060 2180
## 11 AL wind_land 625 67963 5831725
## 324 TX wind_land 552 82785 3666331
## 273 PA heat 543 539050 1415
## 198 MS wind_land 452 54270 2404341
## 192 MO wind_land 389 32271 3963283
## 25 AR wind_land 384 1387 2534703
## 318 TN wind_land 373 3825 1514357
## 73 FL wind_water 359 1550261 30724072
## 321 TX heat 322 6530194 952213h <- ggplot(mortState, aes(STATE, sumMort))
h <- h + geom_point(aes(color = evtCat, size = sumMort, alpha = 0.5))
h <- h + labs(x = "State", y = "Mortality", title = "Storm Mortality Rate By State")
h <- h + theme(axis.text.x = element_text(angle=90, hjust=1))
h
## crop damage results
head(cropState, 10)## STATE evtCat sumMort sumCropDmg sumPropDmg
## 321 TX heat 322 6530194 952213
## 110 IL water 35 5122425 6409721
## 194 MS cold 7 5010706 118874
## 98 IA water 11 1902604 1871768
## 211 NC wind_water 87 1554908 5626668
## 73 FL wind_water 359 1550261 30724072
## 41 CA water 100 1529220 117406552
## 96 IA heat 5 1505000 651391
## 38 CA cold 78 1098346 147093
## 261 OK heat 92 1095950 60890i <- ggplot(cropState, aes(STATE, sumCropDmg))
i <- i + geom_point(aes(color = evtCat, size = sumCropDmg, alpha = 0.5))
i <- i + labs(x = "State", y = "Crop Damage (Thousands)", title = "Storm Crop Damage By State")
i <- i + theme(axis.text.x = element_text(angle=90, hjust=1))
i
## property damage results
head(propState, 10)## STATE evtCat sumMort sumCropDmg sumPropDmg
## 41 CA water 100 1529220 117406552
## 134 LA water 23 10812 35103293
## 73 FL wind_water 359 1550261 30724072
## 136 LA wind_water 25 546112 21569492
## 199 MS wind_water 31 1005910 13172711
## 197 MS water 23 24955 12550249
## 325 TX wind_water 97 74984 9767545
## 323 TX water 277 127147 8179463
## 110 IL water 35 5122425 6409721
## 11 AL wind_land 625 67963 5831725j <- ggplot(propState, aes(STATE, sumPropDmg))
j <- j + geom_point(aes(color = evtCat, size = sumPropDmg, alpha = 0.5))
j <- j + labs(x = "State", y = "Property Damage (Thousands)", title = "Storm Property Damage By State")
j <- j + theme(axis.text.x = element_text(angle=90, hjust=1))
j
There are some very interesting findings here when you look at all three tables side by side.
| Rank | Mortality | Crop Damage | Property Damage |
|---|---|---|---|
| 1 | Illinois (heat) | Texas (heat) | California (water) |
| 2 | Alabama (wind_land) | Illinois (water) | Louisianna (water) |
| 3 | Texas (wind_land) | Mississippi (cold) | Florida (wind_water) |
| 4 | Pennsylvania (heat) | Iowa (water) | Louisianna (wind_water) |
| 5 | Mississippi (wind_land) | North Carolina (wind_water) | Mississippi (wind_water) |
| 6 | Missouri (wind_land) | Florida (wind_water) | Mississippi (water) |
| 7 | Arkansas (wind_land) | California (water) | Texas (wind_water) |
| 8 | Tennessee (wind_land) | Iowa (heat) | Texas (water) |
| 9 | Florida (wind_water) | California (cold) | Illinois (water) |
| 10 | Texas (heat) | Oklahoma (heat) | Alabama (wind_land) |
Several observations:
- Texas, Mississippi, Illinois, and Florida are in the top 10 of all three categories.
- Texas and Mississippi have a broader range of weather phenomena in the top 10 (four different categories a piece).
- Florida appears to be in the top 10 predominantly because of hurricane type events.
- Alabama is in two of the categories (mortality, property damage) due to tornado type events.
- Illinois is in all three categories primarily due to heat and water related events.
- There are four states (Texas, Mississippi, Louisianna, and Florida) that have both hurricane (wind_water) and flooding (water) events that account for the 80% of the top 10 states with weather related property damage.
Next Steps
As with any meaningful exploratory analysis, there tend to be more questions generated than answers. This analysis is no different. Several questions I would be keen to answer would be:
- Is there a consistent trend of events over time for certain states?
- Are there large events in the top ten states that unduly impact on their results?
- Is there a correlation between weather types as was suggested by the water and wind_water event types in the property damage category?
- Has the development of alerts and warning systems, and the greater accuracy of weather prediction impacted the mortality rates over time?