Effects of severe weather effects in public health and economic damages in the US, 1950-2011
César Carrera
15/7/2020
Synopsis
In this brief report we analize the effects of severe weather events in the US for the period of 1950 to 2011, using the National Oceanic and Atmospheric Administration’s (NOAA) storm database. The focus is on the effects of the severe weather events in the population health, approximated by the number of fatalitis and injures, and the economic effects, measure by property and crop damages. We selected the top ten most harmful events by health and economic effects.
Data Processing
Loading of libraries used for the analysis.
library(tidyverse)## Warning: package 'tidyverse' was built under R version 3.6.3## -- Attaching packages -------------------------------------------------------------------------------------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.0 v purrr 0.3.3
## v tibble 3.0.1 v dplyr 0.8.3
## v tidyr 1.0.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## Warning: package 'ggplot2' was built under R version 3.6.3## Warning: package 'tibble' was built under R version 3.6.3## Warning: package 'tidyr' was built under R version 3.6.3## Warning: package 'forcats' was built under R version 3.6.3## -- Conflicts ----------------------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggplot2)Reading the data to the working environment.
data_noaa <- read.csv(bzfile("repdata_data_StormData.csv.bz2"),header = TRUE)Exploring of the data.
str(data_noaa)## '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 "","- 1 N Albion",..: 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 "","- .5 NNW",..: 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","$AC",..: 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 "","-2 at Deer Park\n",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...summary(data_noaa)## STATE__ BGN_DATE BGN_TIME
## Min. : 1.0 5/25/2011 0:00:00: 1202 12:00:00 AM: 10163
## 1st Qu.:19.0 4/27/2011 0:00:00: 1193 06:00:00 PM: 7350
## Median :30.0 6/9/2011 0:00:00 : 1030 04:00:00 PM: 7261
## Mean :31.2 5/30/2004 0:00:00: 1016 05:00:00 PM: 6891
## 3rd Qu.:45.0 4/4/2011 0:00:00 : 1009 12:00:00 PM: 6703
## Max. :95.0 4/2/2006 0:00:00 : 981 03:00:00 PM: 6700
## (Other) :895866 (Other) :857229
## TIME_ZONE COUNTY COUNTYNAME STATE
## CST :547493 Min. : 0.0 JEFFERSON : 7840 TX : 83728
## EST :245558 1st Qu.: 31.0 WASHINGTON: 7603 KS : 53440
## MST : 68390 Median : 75.0 JACKSON : 6660 OK : 46802
## PST : 28302 Mean :100.6 FRANKLIN : 6256 MO : 35648
## AST : 6360 3rd Qu.:131.0 LINCOLN : 5937 IA : 31069
## HST : 2563 Max. :873.0 MADISON : 5632 NE : 30271
## (Other): 3631 (Other) :862369 (Other):621339
## EVTYPE BGN_RANGE BGN_AZI
## HAIL :288661 Min. : 0.000 :547332
## TSTM WIND :219940 1st Qu.: 0.000 N : 86752
## THUNDERSTORM WIND: 82563 Median : 0.000 W : 38446
## TORNADO : 60652 Mean : 1.484 S : 37558
## FLASH FLOOD : 54277 3rd Qu.: 1.000 E : 33178
## FLOOD : 25326 Max. :3749.000 NW : 24041
## (Other) :170878 (Other):134990
## BGN_LOCATI END_DATE END_TIME
## :287743 :243411 :238978
## COUNTYWIDE : 19680 4/27/2011 0:00:00: 1214 06:00:00 PM: 9802
## Countywide : 993 5/25/2011 0:00:00: 1196 05:00:00 PM: 8314
## SPRINGFIELD : 843 6/9/2011 0:00:00 : 1021 04:00:00 PM: 8104
## SOUTH PORTION: 810 4/4/2011 0:00:00 : 1007 12:00:00 PM: 7483
## NORTH PORTION: 784 5/30/2004 0:00:00: 998 11:59:00 PM: 7184
## (Other) :591444 (Other) :653450 (Other) :622432
## COUNTY_END COUNTYENDN END_RANGE END_AZI
## Min. :0 Mode:logical Min. : 0.0000 :724837
## 1st Qu.:0 NA's:902297 1st Qu.: 0.0000 N : 28082
## Median :0 Median : 0.0000 S : 22510
## Mean :0 Mean : 0.9862 W : 20119
## 3rd Qu.:0 3rd Qu.: 0.0000 E : 20047
## Max. :0 Max. :925.0000 NE : 14606
## (Other): 72096
## END_LOCATI LENGTH WIDTH
## :499225 Min. : 0.0000 Min. : 0.000
## COUNTYWIDE : 19731 1st Qu.: 0.0000 1st Qu.: 0.000
## SOUTH PORTION : 833 Median : 0.0000 Median : 0.000
## NORTH PORTION : 780 Mean : 0.2301 Mean : 7.503
## CENTRAL PORTION: 617 3rd Qu.: 0.0000 3rd Qu.: 0.000
## SPRINGFIELD : 575 Max. :2315.0000 Max. :4400.000
## (Other) :380536
## 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 :465934 Min. : 0.000 :618413
## 1st Qu.: 0.00 K :424665 1st Qu.: 0.000 K :281832
## Median : 0.00 M : 11330 Median : 0.000 M : 1994
## Mean : 12.06 0 : 216 Mean : 1.527 k : 21
## 3rd Qu.: 0.50 B : 40 3rd Qu.: 0.000 0 : 19
## Max. :5000.00 5 : 28 Max. :990.000 B : 9
## (Other): 84 (Other): 9
## WFO STATEOFFIC
## :142069 :248769
## OUN : 17393 TEXAS, North : 12193
## JAN : 13889 ARKANSAS, Central and North Central: 11738
## LWX : 13174 IOWA, Central : 11345
## PHI : 12551 KANSAS, Southwest : 11212
## TSA : 12483 GEORGIA, North and Central : 11120
## (Other):690738 (Other) :595920
## ZONENAMES
## :594029
## :205988
## GREATER RENO / CARSON CITY / M - GREATER RENO / CARSON CITY / M : 639
## GREATER LAKE TAHOE AREA - GREATER LAKE TAHOE AREA : 592
## JEFFERSON - JEFFERSON : 303
## MADISON - MADISON : 302
## (Other) :100444
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_
## Min. : 0 Min. :-14451 Min. : 0 Min. :-14455
## 1st Qu.:2802 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0
## Median :3540 Median : 8707 Median : 0 Median : 0
## Mean :2875 Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.:4019 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. :9706 Max. : 17124 Max. :9706 Max. :106220
## NA's :47 NA's :40
## REMARKS REFNUM
## :287433 Min. : 1
## : 24013 1st Qu.:225575
## Trees down.\n : 1110 Median :451149
## Several trees were blown down.\n : 568 Mean :451149
## Trees were downed.\n : 446 3rd Qu.:676723
## Large trees and power lines were blown down.\n: 432 Max. :902297
## (Other) :588295The variables needed to answer the main questions are the following: EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG *CROPDMGEXP
Also, to compare the economic consequences, we need to standarized the measurement of the monetary ammounts. Acording to the documentation, the variables PROPDMGEXP y CROPDMGEXP identify the magnitude acording to the letters “K” for thousands, “M” for millions, and “B” for billions. We assume the other cases do not correspond to valid measurements and will not be considered.
#Variables of interest
data_noaa_subset <- subset(data_noaa, select = c(EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP))
#Damaga by type in dollars
data_noaa_subset <- data_noaa_subset %>%
mutate(PROPDMG_MOD = case_when(PROPDMGEXP=="K" ~ PROPDMG*1000, PROPDMGEXP=="M" ~ PROPDMG*1000000, PROPDMGEXP=="B" ~ PROPDMG*1000000000, TRUE ~ NA_real_),
CROPDMG_MOD = case_when(CROPDMGEXP=="K" ~ CROPDMG*1000, CROPDMGEXP=="M" ~ CROPDMG*1000000, CROPDMGEXP=="B" ~ CROPDMG*1000000000, TRUE ~ NA_real_))
#Total damage: property damage + crop damage
data_noaa_subset <- data_noaa_subset %>%
mutate(TOTAL_DMG = rowSums(data_noaa_subset[,8:9], na.rm=TRUE),
FATAL_INJUR = rowSums(data_noaa_subset[,2:3], na.rm=TRUE))Results
Effects on the population health
The first question is wich types of events are most harmful with respect to the population health. In this case, we approximate the population health with the number of fatalities and injuries.
First, we calculate the total number of fatalities and injures by event.
health_effects <- data_noaa_subset %>%
group_by(EVTYPE) %>%
summarise(TOT_FATALITIES = sum(FATALITIES, na.rm = TRUE),
TOT_INJURIES = sum(INJURIES, na.rm = TRUE),
TOT_FATAL_INJUG = sum(FATAL_INJUR, na.rm = TRUE)) %>%
ungroup()
health_effects <- subset(health_effects, TOT_FATAL_INJUG > 0)
#There are 220 event types with a positive number of fatalities or injuries. For the present análisis, we select the top 10 events.
health_effects <- arrange(health_effects, desc(TOT_FATAL_INJUG))
health_effects <- health_effects[1:10,]
health_effects## # A tibble: 10 x 4
## EVTYPE TOT_FATALITIES TOT_INJURIES TOT_FATAL_INJUG
## <fct> <dbl> <dbl> <dbl>
## 1 TORNADO 5633 91346 96979
## 2 EXCESSIVE HEAT 1903 6525 8428
## 3 TSTM WIND 504 6957 7461
## 4 FLOOD 470 6789 7259
## 5 LIGHTNING 816 5230 6046
## 6 HEAT 937 2100 3037
## 7 FLASH FLOOD 978 1777 2755
## 8 ICE STORM 89 1975 2064
## 9 THUNDERSTORM WIND 133 1488 1621
## 10 WINTER STORM 206 1321 1527According to the data, the event with the highest number of fatalities and injures is “tornado”.
ggplot(health_effects, aes(y=TOT_FATALITIES, x=reorder(EVTYPE, TOT_FATALITIES))) +
geom_bar(position="dodge", stat="identity") +
coord_flip() +
xlab("Event Type") +
ylab("Total Injuries and Fatalities") +
ggtitle("Top most harmful eventes - Heatlh effects")
The second question is wich types of events are most harmful with respect to the economic consequences. In this case, we approximate the economic consecuenses with the estimated damages in dollars. We consider the total of property damages and crop damages.
economic_effects <- data_noaa_subset %>%
group_by(EVTYPE) %>%
summarise(TOTAL_PROP_DAMAGE = sum(PROPDMG_MOD, na.rm = TRUE),
TOTAL_CROP_DAMAGE = sum(CROPDMG_MOD, na.rm = TRUE),
TOTAL_ECONOMIC_DAMAGE = sum(TOTAL_DMG, na.rm = TRUE)) %>%
ungroup()
economic_effects <- subset(economic_effects, TOTAL_ECONOMIC_DAMAGE > 0)
#There are 426 event types with a positive number damages. For the present análisis, we select the top 10 events.
economic_effects <- arrange(economic_effects, desc(TOTAL_ECONOMIC_DAMAGE))
economic_effects <- economic_effects[1:10,]
economic_effects## # A tibble: 10 x 4
## EVTYPE TOTAL_PROP_DAMAGE TOTAL_CROP_DAMAGE TOTAL_ECONOMIC_DAMAGE
## <fct> <dbl> <dbl> <dbl>
## 1 FLOOD 144657709800 5661968450 150319678250
## 2 HURRICANE/TYPHOON 69305840000 2607872800 71913712800
## 3 TORNADO 56925660480 414953110 57340613590
## 4 STORM SURGE 43323536000 5000 43323541000
## 5 HAIL 15727366720 3025537450 18752904170
## 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 8967041310The most harmful event y terms of economic damages are floods, followed by hurracane/typhoon. We observe differences in property damages and crop damages by event, with could be analyse in the future.
options(scipen=10000)
ggplot(economic_effects, aes(y=TOTAL_ECONOMIC_DAMAGE, x=reorder(EVTYPE, TOTAL_ECONOMIC_DAMAGE))) +
geom_bar(position="dodge", stat="identity") +
coord_flip() +
xlab("Event Type") +
ylab("Total economic damages (US Dollars)") +
ggtitle("Top most harmful eventes - Economic effects")