hurricaneexposure
package## Note: Examples in this vignette require that the
## `hurricaneexposuredata` package be installed. The system currently
## running this vignette does not have that package installed, so code
## examples will not be evaluated.This package allows users to explore and map data of county-level exposures to Atlantic-basin tropical storms between 1988 and 2018 for a number of storm hazards (e.g., wind, rain, flood events, distance from the storm track) for counties in the eastern half of the United States. Users can map exposures by county for a selected storm and can also identify all storms to which selected counties were exposed, based on user-specified thresholds (for example, the package allows the user to identify all storms that brought wind of 34 knots or higher to Miami-Dade County in Florida).
If you use this package and the data in the associated data package
(hurricaneexposuredata) for research, please cite both
packages. In particular, be sure to include the version of the packages
that you used, as this will make your research project more
reproducible, and the data will likely be updated as we get further
years of data and find improved ways to measure tropical storm exposure.
The two packages can be cited as:
Anderson B, Yan M, Ferreri J, Crosson W, Al-Hamdan M, Schumacher A and Eddelbuettel D (2020). hurricaneexposure: Explore and Map County-Level Hurricane Exposure in the United States. R package version 0.1.1, <URL: https://github.com/geanders/hurricaneexposure>.
Anderson B, Schumacher A, Crosson W, Al-Hamdan M, Yan M, Ferreri J, Chen Z, Quiring S and Guikema S (2020). hurricaneexposuredata: Data Characterizing Exposure to Hurricanes in United States Counties. R package version 0.1.0, <URL: https://github.com/geanders/hurricaneexposuredata>.
To generate BibTex entries for the packages, you can use the
citation function in R.
This package depends on data in a data package
(hurricaneexposuredata) that is available through a
drat repository on GitHub. To use the
hurricaneexposure package, you will need to install
hurricaneexposuredata on your computer. You can do that by
adding the drat archive to the list of repositories your
system will query when adding and updating R packages. Once you do this,
you can install the hurricaneexposuredata using the
install.packages function (and later update it using the
update.packages function):
You will want to have the latest version of the
hurricaneexposuredata package. If you have previously
installed hurricaneexposuredata, you may need to re-run the
above code if you update the hurricaneexposure package, to
update to the latest data.
The hurricaneexposuredata data package includes data
that characterizes county-level exposure to tropical storms in counties
in the eastern half of the United States between 1988 and 2015 (for some
hazards, exposure data is only included for a subset of these years).
Tropical storms that did not pass within at least 250 km of at least one
US county were excluded from these datasets. The following datasets are
included with the hurricaneexposuredata data package:
county_centers: Location of United States county
centers of populationhurr_tracks: Storm tracks for Atlantic-basin storms,
1988-2018closest_dist: Closest distances between counties and a
storm track, for Atlantic-basin storms, 1988-2018rain: Rainfall for US counties during Atlantic basin
tropical storms, 1988-2011; daily rainfall is given from five days
before to three days after the storm’s closest approach to the
countystorm_winds: Modeled county wind speeds for
Atlantic-basin storms, 1988-2018storm_events: Listings from the NOAA Storm Events
database that occurred near in time and location to tropical storms,
1988-2018. This database changed the types of events it reported in
1996, which should be considered when using the data.ext_tracks_wind: Estimated county wind speeds for
Atlantic-basin storms, 1988-2018, based on the wind radii listed in the
HURDAT2Once you’ve installed and loaded hurricaneexposuredata,
you can load the included data using the data function. For
example, you can access the data on hurricane tracks
(hurr_tracks) using:
For each dataset included in hurricaneexposuredata, you
can see the helpfiles for the data for more information (e.g.,
?hurr_tracks to read documentation on the hurricane tracks
data shown above). This data documentation includes both explanations of
how the dataset was created, definitions of the variables included in
each dataset, and the sources of data included.
The following table shows the storms covered by the
hurricaneexposuredata data package, as well as which hazard
metrics are available for each year. All storms passed within 250 km of
at least one U.S. county.
In functions throughout the hurricaneexposure package,
storms are identified based on their name and year (e.g., Hurricane
Floyd in 1999 is identified as “Floyd-1999”). Note that a few storms
satisfied the criteria to be included in the data but were unnamed
(e.g., “AL13” in 1988). These storms are identified in functions in the
hurricaneexposure package based on the identifying name
listed in the above table, which is based on the first four characters
of the storm’s US ATCF code for the storm. For some research projects,
however, you may want to consider excluding these unnamed storms from
the analysis.
The hurricaneexposure package has functions to interact
with the data in the hurricaneexposuredata package. First,
there are functions that can be used to create some different maps of
hurricane exposures based on distance to the storm track and
rainfall.
For several of the storm hazard measurements (currently rain, wind,
and distance), you can use the map_counties function in
hurricaneexposure to plot a map for a specific storm
showing the continuous value of that metric for all eastern U.S.
counties. For example, you can map county-level rain during Hurricane
Floyd (1999) using the following call:
By default, this map shows the cumulative rain in each county for two
days before to one day after the date that the storm passed closest to
the county (see the help file for the closest_dist dataset
in hurricaneexposuredata to find out more about how this
date of closest approach was determined for each county-storm
combination). The period used to calculate cumulative rainfall for the
map can be customized using the days_included option in the
map_counties function. The two following examples show the
difference in rain maps for Tropical Storm Allison (2001) when using
rain only from the day before and the day the storm was closest to each
county (days_included = -1:0) versus from five days before
to three days after the storm’s closest approach
(days_included = -5:3):
map_counties(storm = "Allison-2001", metric = "rainfall", days_included = -1:0) +
ggplot2::ggtitle("Rain during Allison (2001) for day before and day of closest approach")map_counties(storm = "Allison-2001", metric = "rainfall", days_included = -5:3) +
ggplot2::ggtitle("Rain during Allison (2001) for five days before to three days\nafter the day of closest approach")You can also use the map_counties function to plot the
maximum wind during the storm for each county. For this, you use the
argument metric = "wind". For example, you can plot
county-specific storm winds during Hurricane Katrina (2005) with the
call:
These county winds are determined based on a model of wind speeds,
using as input data from the hurricane tracks data. See the
documentation for the storm_winds dataset
(?storm_winds) to find out more about this data; further
details on the wind model are available in the vignettes for the stormwindmodel
package.
You can map a few other variables of wind with this function. For
example, you can map the duration of winds of 20 m / s or more by using
the argument wind_var = "sust_dur":
You can map estimated gust winds, rather than sustained winds (the
default), using the argument wind_var = "vmax_gust".
Further, we have included a second source of estimated winds in the data
available in hurricaneexposuredata. These wind estimates
are based on the wind radii in the HURDAT2 dataset (you can find out
more about this data in the helpfile for the
ext_tracks_wind dataset). These data provide estimates of
which counties were exposed during a storm to sustained winds in four
categories: 0–34 knots; 34–50 knots; 50–64 knots; and 64 knots or
higher. Therefore, these data provide a categorical rather than
continuous estimate of county wind speeds. However, they may be
preferable to the model wind speeds for some storms, especially storms
in extra-tropical transition. Further, you may find it interesting to
use these data in a sensitivity analysis, to compare if impact study
results are sensitive to whether these wind data or the modeled wind
data are used for exposure classification. You can create a wind map
based on this data using the wind_source = "ext_tracks"
argument in the map_counties function. For example, to map
estimated winds during Hurricane Katrina based on the HURDAT2 wind
radii, you can run:
Finally, you can also use the map_counties function to
plot the closest distance between the storm and each county. For this,
you use the argument metric = "distance". For example, the
following call plots county-level distances from the track of Hurricane
Sandy (2012):
See the documentation for the closest_dist dataset
(?closest_dist) for more details on how distances from the
storm track were calculated for each county.
You can also map binary county-level exposures, where each county is classified as “exposed” based on whether it meets a threshold for one of the hazard-based metrics. These maps can be created for the metrics shown in the continuous exposure maps above (wind, rain, and distance from the storm track). In addition, binary classification maps can also be created based on storm event listings for flood events and tornado events from the NOAA Storm Events database.
For example, you can map a binary variable of distance-based exposure
using map_distance_exposure, with “exposure” defined as
that the county was within the number of kilometers given in
dist_limit of the storm track. For example, you can map the
counties that were within 75 km of Hurricane Sandy’s storm track with
the call:
Similarly, you can map binary county exposure based on rain using
map_rain_exposure. In the case of rain, you must define
exposure thresholds for both rainfall and distance. This is because the
rain data is observed data, so neglecting to limit “exposed” counties to
those close to the storm track may pick up counties far from the storm
that experienced a lot of rain over the same period. For example, you
can use the following call to map rain exposure to Tropical Storm
Allison (2001), where a county is defined as exposed to the storm if
cumulative rainfall from five days before to three days after the
storm’s closest approach was 175 mm or more and the county was within
500 km of the storm’s track:
As with the continuous maps of rain exposure, the number of days
included to calculate cumulative rain can be adjusted with the
days_included option in map_rain_exposure (see
the help file for map_rain_exposure for more details).
You can use the map_wind_exposure function to map
counties exposed to a certain winds during a storm. For example, to
identify all counties exposed to sustained winds of 20 m / s or higher
during Hurricane Katrina, you can run:
If you would like to base a wind threshold on knots rather than m /
s, you can use the convert_wind_speed function from the
weathermetrics package to do that. For example, to map
counties in which winds were 34 knots or higher during the storm, you
can run:
library(weathermetrics)
map_wind_exposure(storm = "Katrina-2005",
wind_limit = convert_wind_speed(34, "knots", "mps"))Finally, you can map which counties were exposed to specific types of
events, as listed in the NOAA Storm Events Database, using the
map_event_exposure function. For example, you can map
counties for which a flood event was listed for Hurricane Floyd (1999)
with the call:
Similarly, you can map which counties were exposed to tornado events during Hurricane Ivan (2004) with:
When using this function, note that the types of events reported in the NOAA Storm Events Database changed in 1996, so for many event types there will be no listings before 1996. Tornado listings do extend earlier, but flood listings do not. Further, when using this data, the user should be aware of and take into account the limitations of the database, especially for comparing storms across years. Reporting standards likely have changed over time; for example, it is possible that earlier years have more false negatives than more recent years.
Event listings from the NOAA Storm Events database were linked to
storms based on the start date of the event being within a five-day
window of the date the storm was closest to the county and the storm
coming within 500 km of the county. The noaastormevents
package was used to create this dataset. For more information on the
data, see the help file for the storm_events dataset
(?storm_events) or the vignette for the
noaastormevents package.
If you would like to add a storm’s track to one of the maps of
county-level exposure, you can do so using the map_tracks
function. This function can be used either to map a storm track on a
blank U.S. map or to add a track to an existing map, if the map is a
ggplot object (as are all maps generated in the previous
section).
First, the map_tracks function can be used to create a
map of the hurricane tracks for one or more storms. For example, to plot
the tracks of Hurricane Floyd in 1999, you can run:
There are some different options you can use for the tracks’ appearance. For example, if you wanted to plot the tracks of several storms, and plot each point when the track locations were measured (typically every six hours), use some transparency so you can see all the tracks, and show the tracks in blue, you can run:
map_tracks(storms = c("Andrew-1992", "Katrina-2005", "Rita-2005"),
alpha = 0.5, plot_points = TRUE, color = "blue")As another example, to map all tracks for storms in 2018, you can run:
library(dplyr)
library(tidyr)
storms_2018 <- hurr_tracks %>%
select(storm_id) %>%
distinct() %>%
mutate(year = stringr::str_extract(storm_id, "-[0-9].+")) %>%
filter(year == "-2018")
map_tracks(storms = storms_2018$storm_id) The map_tracks function is used in all the mapping
functions described in the previous section, to add the storm’s track to
the exposure maps. However, if you would like to customize the
appearance of the storm’s track on the map, you can do so by plotting
the exposure map without the storm track
(add_track = FALSE) and then adding the track yourself with
the map_tracks function. For example, to change the color
of the storm track in a map of flood event exposure during Hurricane
Floyd (1999) and to add points showing the available observations for
the storm in the Best Tracks hurricane tracking data, you can run:
The hurricaneexposure package also has several functions
that can input a list of counties and output a list of all of the storms
to which each county was exposed over a certain period, based on one of
the hazard metrics. These exposure listings include the storm dates,
making them easy to integrate into time series datasets of health or
other impact data.
The county_rain function takes a list of county FIPS
codes, bounds on the starting and ending years of the analysis, and
thresholds to define rain-based exposure (cumulative rainfall and
distance from the storm’s track, as explained for the binary rain maps
shown in an earlier section) and creates a list of all storms that met
these thresholds for the counties. For example, to get a dataset of all
the storms to which Orleans Parish (FIPS 22071), and Newport News,
Virginia (FIPS 51700), were exposed between 1995 and 2005, where
“exposed” means that the storm passed within 100 kilometers of the
county center and the rainfall over a three-day window of the date of
closest approach was 100 millimeters or more, you can run:
county_rain(counties = c("22071", "51700"), start_year = 1995, end_year = 2005,
rain_limit = 100, dist_limit = 100, days_included = c(-1, 0, 1))This function draws on the same dataset (rain) from the
hurricaneexposuredata package as the functions for mapping
rain exposure shown above. For this and other exposures, see the table
earlier in this vignette to see which years of data are available (for
rain, data is currently available for storms through 2011).
In addition to giving the names and closest dates of each storm for
each county (closest_date– note, this is given based on
local time for the county; see documentation for the countytimezones
package to find out more about how local time was calculated from
UTC for each county), this function also gives you the distance between
the county and the storm’s track at the time when the storm was closest
to the county’s population weighted center (storm_dist, in
kilometers) and the total precipitation over the included days
(tot_precip). The returned dataframe also gives the time of
closest approach in both local time (local_time) and UTC
(closest_time_utc), based on 15-minute intervals along the
storm’s track.
Similar functions are available in the package to create listings of county exposures to storms based on wind, distance to the storm track, and events listings from the NOAA Storm Events Database (flood and tornado events). For example, to get a listing of all storms between 1988 and 2015 for which Miami-Dade county (FIPS: ) has experienced sustained winds of 34 knots or more (17.5 m / s), you can run:
The returned dataframe includes the estimated maximum values during
the storm for sustained winds (vmax_sust) and gust winds
(vmax_gust), both in m / s, as well as the duration for
which sustained and gust winds were over 20 m / s in the county
(sust_dur and gust_dur, respectively). It also
includes some of the variables on the date and time of closest approach
and the distance at the storm’s closest approach.
The county_wind function allows some further
specifications when identifying the storms to which a county was exposed
based on wind estimates. First, it allows the use of alternative wind
variables. While the default is to use the maximum sustained wind speed
experienced in the county during the storm, it is also possible to base
the exposure metric on the maximum gust winds
(wind_var = "vmax_gust") or on the duration of either
sustained or gust winds at or above 20 m / s
(wind_var = "sust_dur" and
wind_var = "gust_dur", respectively). For example, to
determine which storms brought Orleans Parish sustained winds of 34
knots or higher (17.5 m / s or higher) for an hour or more
(wind_limit = 60; for wind durations, this wind limit
should be specified in minutes), you can run:
county_wind(counties = "12086", start_year = 1988, end_year = 2015,
wind_var = "sust_dur", wind_limit = 60)Further, the county_wind function allows you to pull
wind estimates from an alternate source. By default, this function uses
wind estimates for each county from a wind model (see the vignettes for
the stormwindmodel package for much more detail on this
modeling process). However, you can also pull estimates based on the
wind radii from HURDAT2 by using the option
wind_source = "ext_tracks". See the help file for the
ext_tracks_wind dataset that comes with
hurricaneexposuredata for more details on this data source
and how the county-specific exposures were determined from the data
source for use in the county_wind function. It is important
to note that this data results in wind estimates with breaks at 34
knots, 50 knots, and 64 knots, rather than continuous estimates. This
means that a sustained wind estimate from this data source of 17.4896 m
/ s (34 knots) is estimating that the county had maximum wind speeds of
34 knots or higher during the storm, but not as high as 50 knots.
Further, the dur_sust value when using this data source is
based on number of minutes with winds at or above 34 knots (rather than
the 20 m / s value used for durations for the modeled wind data). Gust
durations can not be determined using the HURDAT2 wind radii.
The HURDAT2 wind radii are more tied to observations during a
specific storm than the modeled data. In certain cases, especially
storms in extratropical transition, this dataset might be preferable to
the modeled wind speeds, even though it provides less continuous
estimates. This source of wind data can be specified using the
wind_source option in the county_wind
function. For example, to generate a list of all storms in Orleans
Parish with maximum sustained winds of 34 knots or more based on the
HURDAT2 wind radii, you can run:
county_wind(counties = "12086", start_year = 1988, end_year = 2015,
wind_var = "vmax_sust", wind_limit = 17.4, wind_source = "ext_tracks")The county_distance function can similarly be used to
generate a listing of all storms that came within a certain distance of
specified counties. For example, to get a list of all storms that came
within 50 km of Orleans Parish (FIPS 22071) between 1988 and 2015, you
can run:
Finally, you can use the county_events function to get a
listing of all storms for which a county had a certain type of NOAA
Storm Events listing. For example, to get a list of all storms for which
Norfolk, Virginia, (FIPS: 51710) had a flood event listing, you can
run:
Some datasets covering storm impacts might have communities composed
of multiple counties rather than county-specific listings. To get a
dataframe listing the relevant storms for multi-county communities, you
can use the multi_county_* family of functions in a similar
way to the county_* family of functions. For example, to
get rain exposure listings in the case where one of the communities (New
York, NY) is comprised of multiple counties, you can run:
communities <- data.frame(community_name = c(rep("ny", 6), "no", "new"),
fips = c("36005", "36047", "36061",
"36085", "36081", "36119",
"22071", "51700"))
multi_county_rain(communities = communities, start_year = 1995, end_year = 2005,
rain_limit = 100, dist_limit = 100)The output from this function includes columns for the average
closest distance for any of the counties in the community
(mean_dist), the average precipitation for all the counties
(mean_precip), the highest precipitation for any of the
counties (max_rain), and the smallest distance between the
storm track and any of the county population-weighted centers
(min_dist).
Similar functions exist for wind exposure
(multi_county_wind) and distance exposure
(multi_county_distance). In some cases, these multi-county
functions may have less functionality than their county_*
counterparts. For example, the multi_county_wind function
currently only allows use of the sustained wind speeds for the exposure
metric, while the county_wind function allows the use of
sustained winds, gust winds, or durations of either.