## Note: Examples in this vignette are set to not run on CRAN. If you
## would like to build this vignette locally, you can do so by first
## setting the environmental variable 'NOT_CRAN' to 'true' on your
## computer and then rebuilding the vignette.This package can be used to explore and map data from NOAA’s Storm Events Database. This storm event database is maintained by NOAA’s National Centers for Environmental Information and aims to provide information, including estimates of damage and human health impacts, for severe storm events that affect the U.S. It has aggregated storm event listings for tornados since the 1950s and for a broad range of event types (e.g., snow storms, heat waves, droughts, wildfires, floods) since 1996. This database has been used either alone or in conjunction with other data for a number of scientific studies. It is available for downloading at https://www.ncdc.noaa.gov/stormevents/ftp.jsp, with three files (one with event details, one with fatality details, and with with location details) available per year.
While the online database does not have a structured API, this package uses regular expressions to search the listings of available files to identify the filename for a queried year and download that year’s data to a user’s R session. The package functions then filter the downloaded storm event listings based to the dates, locations, event types, and other search limitations specified by the user. In particular, this package can be used to identify storm event listings that were close in location and time to Atlantic basin tropical storm tracks.
The package has two main functions:
find_events: Create a dataframe with event listings by
county based on user-specified parameters (e.g., date range, specific
tropical storm, type of event) from the NOAA Storm Events databasemap_events: Create a map showing the geographic
distribution by county of certain features of an events dataframe (e.g.,
whether or not a county had an event, number of events per county, total
property damage in a county across event listings).While this package aims to create and map events data by county, some events are listed in the original database by forecast zone rather than county. For these observations, the package functions attempts to match the observation with the appropriate county. However, there are some events listed by forecast zone that cannot be matched to county, and so are excluded from results returned by this package. Further, there may be occasional errors in this matching, so we include a marker returned datasets indicating which events were listed by forecast zone rather than county, to allow further quality control checks by the user for his or her specific applications of the package.
The noaastormevents package includes options that allow
users to find events based on proximity to a tropical storm. To use this
functionality, the user must have the hurricaneexposuredata
package, available from a drat repository, installed locally. This
package can be installed by running:
It is important for users to note that there are limitations to this
storm events database. In particular, listings can be somewhat
subjective. A lack of event listing in the database should not be
considered definitive proof that storm conditions did not exist at a
location at a certain time. Further, the database has changed over time
in terms of which types of events are included. R users should review
the Storm Event Database’s documentation, which is available at the database’s
website, to be sure they understand how to use and interpret event
listings from the database. Further, the noaastormevents
package includes a “Details” vignette with more details on this storm
events data and how it is processed by functions in this package.
The package has two main functions. First, the
find_events function can be used to create a dataframe with
all storm event listings within a specified time frame. For example, the
following code creates a dataframe with all events from the NOAA Storm
Events Database listed as beginning between September 14 and 18, 1999 (a
time window relevant for Hurricane Floyd, which caused extensive damage,
especially from flooding):
The code call returns a data frame with a subset of data from the Storm Events Database for 1999. Each event listing with a start data between September 14 and 18 is included. The data frame has the following columns:
begin_date: The date the event beganend_date: The date the event endedstate: The state in which the event occurredcz_type: Whether the event was listed by county or by
forecast zone (Where possible, events that are listed by forecast zone
are linked to the appropriate county FIPS code and therefore not
excluded from this returned dataframe. However, this column is included
to allow users to perform quality control on events listed by forecast
zone (CZ_TYPE of “Z”).)cz_name: The name of the county (or other area name) in
which the event occurred.event_type: Event type (e.g., “Flood”, “Lightning”,
“Tornado”, “Wildfire”). See the NOAA
Storm Events documentation for definitions of these event typessource: The source of the storm event listing (e.g.,
trained spotter, emergency manager, general public, law
enforcement)injuries_direct, injuries_indirect,
deaths_direct, deaths_indirect,
damage_property, damage_crops: Estimates of
damage from the event to human health, property, and crops. For damages,
initial values in the database (e.g., "5K") have been
converted where possible to numeric values (e.g., 5000).
See the “Details” vignette for more details on this process.fips: Five-digit county (Federal Information Processing
Standard) FIPS code. This code uniquely identifies each U.S. county. If
the event was reported by forecast zone (cz_type of
Z), code within the package has used regular expressions to
try to correctly match the area name to a county FIPS (see the “Details”
vignette for more details on this process).In some cases, a user may wish to identify all storm events listings
that were close in time and place to a tropical storm track, which can
be done using the storm option of the
find_events function. To do this, the package draws on data
and functions in the packages hurricaneexposure (available
on CRAN) and hurricaneexposuredata (available through a
drat repository) to match storm event listings against tropical storm
“best tracks” data through the hurricaneexposuredata and
hurricaneexposure packages.
Event listings are pulled for all events that occurred within a
five-day window of the day the storm was closest to each county and that
were in counties within a user-specified distance of the storm track.
For example, to create a dataset with all event listings for counties
within 300 kilometers (dist_limit option) of the path of
Hurricane Floyd (storm = "Floyd-1999") for a five-day
window of the storm’s closest approach to each county, the user can
run:
Note that the storm ID includes a storm name (“Floyd”) and year
(“1999”). Both must be specified, as storm names are not retired until
they are used for a very severe storm. This functionality will only work
for storms included in the hurricaneexposuredata package.
These currently include:
Once find_events has been used to create a dataset of
storm event listings, the dataset can be explored. The user can do
things like determine the number of events of each type that occurred
near in time and location to a storm’s track. For example, here is a
summary of numbers of different types of events for Hurricane Floyd,
created using dplyr tools:
library(dplyr)
floyd_events %>%
group_by(event_type) %>%
summarize(n = n()) %>%
arrange(desc(n)) %>%
knitr::kable(col.names = c("Event type", "Number of events"),
caption = "NOAA Storm Events within 200 km and within a 5-day window of Hurricane Floyd.")Similarly, you could create a summary with the states in which the most events were listed and give the number and type of events in each of those counties:
floyd_events %>%
group_by(state, event_type) %>%
summarize(n = n()) %>%
ungroup() %>%
arrange(state, desc(n)) %>%
mutate(event_type = paste0(event_type, " (", n, ")")) %>%
group_by(state) %>%
summarize(Total = sum(n),
Events = paste(event_type, collapse = ", ")) %>%
ungroup() %>%
arrange(desc(Total)) %>%
knitr::kable()The package also has a function, map_events, for mapping
several different features from the storm event dataframes returned by
find_events. This function is meant to provide reasonable
defaults for many maps a user might want to create with this data, but
in some cases might not provide the exact map a user would like to
create. In that case, we recommend the user try the
choroplethr package, which can be used to map US county
data as long as the data includes county FIPS codes, as the dataframes
returned by find_events do.
The map_events function allows you to create maps of
several features of the dataframe returned by
find_events:
The maps for property and crop damage use a logarithmic scale, while others use an untransformed scale. Listings for all events within a county in the input dataframe are summed to create county-level values to map. While the function allows mapping indirect injuries and deaths, in practice is seems these are rarely listed in recent Storm Events data, so these may not create informative maps.
The map_events function inputs a dataframe as created by
find_events. For example, which counties had events listed
that started between September 14 and 18, 2009, you can run:
Because the first argument of map_events is a dataframe,
you can also use the pipe operator (%>%) to pipe the
results of a call to find_events directly into
map_events, as shown in the next example.
The map_events function includes a states
option, to allow the user to limit the map to a subset of states (only
states in the continental US can be included). This option also allows
the inputs of “east” (the default, maps only states in the eastern half
of the United States) and “all” (maps all states in the continental US).
To create a map of all events within 300 km of the track of Hurricane
Floyd with start dates in a five-day window of the storm’s closest date
to each county, and to map all continental states, you can run:
floyd_events <- find_events(storm = "Floyd-1999", dist_limit = 300)
floyd_events %>%
map_events(states = "all")If you instead would like to limit the map to states along the East Coast and plot the number of reported events per county, you could instead run:
floyd_events %>%
map_events(plot_type = "number of events",
states = c("florida", "georgia", "south carolina", "north carolina",
"virginia", "maryland", "delaware", "pennsylvania",
"new jersey", "new york", "connecticut", "rhode island",
"massachusetts", "vermont", "new hampshire", "maine",
"west virginia"))The maps for crop and property damage use a logarithmic scale, which typically shows differences in these measurements better than an untransformed scale. For example, here is the call and resulting map to map crop damage associated with Hurricane Floyd in North Carolina, Virginia, and Maryland:
floyd_events %>%
map_events(plot_type = "crop damage",
states = c("north carolina", "virginia", "maryland"))Property damage can be mapped in a similar way:
floyd_events %>%
map_events(plot_type = "property damage",
states = c("florida", "georgia", "south carolina", "north carolina",
"virginia", "maryland", "delaware", "pennsylvania",
"new jersey", "new york", "connecticut", "rhode island",
"massachusetts", "vermont", "new hampshire", "maine",
"west virginia"))The track of a tropical storm can be added to a map by specifying the
name of the storm with the storm argument and setting
add_tracks to TRUE. For example, the track of
Hurricane Floyd can be added to a map of crop damage in North Carolina,
Virginia, and Maryland from the example dataframe with the call:
There are many more details describing how this package works, as well as details on the Storm Events data, in the “Details” vignette that also comes with this package.