multi_county_rain {hurricaneexposure} | R Documentation |
Hurricane exposure by rain for communities
Description
This function takes a dataframe with multi-county communities (see example for the proper format) and returns a community-level dataframe of storms to which the community was exposed, based on the average distance between the storm's track and the population-based centers of each county in the community and the given threshold of rainfall, summed over the days included in the rainfall measurement.
Usage
multi_county_rain(
communities,
start_year,
end_year,
rain_limit,
dist_limit,
days_included = c(-2, -1, 0, 1)
)
Arguments
communities |
A dataframe with the FIPS codes for all counties within
each community. It must include columns with a column identifier
( |
start_year |
Four-digit integer with first year to consider. |
end_year |
Four-digit integer with last year to consider. |
rain_limit |
Minimum of rainfall, in millimeters, summed across the days
selected to be included ( |
dist_limit |
Maximum distance, in kilometers, of how close the storm track must come to the county's population mean center to classify the county as "exposed" to the storm. |
days_included |
A numeric vector listing the days to include when
calculating total precipitation. Negative numbers are days before the
closest date of the storm to a county. For example,
|
Value
Returns a dataframe with a row for each county-storm pair and with columns for:
commun
: Each community's unique idstorm_id
: Unique storm identifier with the storm name and year, separated by a hyphen(e.g., "Alberto-1988", "Katrina-2005")closest_date
: Date (based on local time) of the closest approach of the storm to the county's population mean center.local_time
: Local time of the closest approach of the storm to the county's population mean center, based on storm tracks linearly interpolated to 15-minute increments.closest_time_utc
: Time, in UTC, of the closest approach of the storm to the county's population mean center, based on storm tracks linearly interpolated to 15-minute increments.mean_dist
: Average of the minimum distance (in kilometers) between the storm's track and the population mean centers of all the counties in the community.mean_rain
: Average of cumulative rainfall, in millimeters, in the counties in the community for the days selected using thedays_included
option.min_dist
: The smallest minimum distance (in kilometers) between the storm's track and the population mean centers of any of the counties in the community.max_rain
: The maximum cumulative rainfall, in millimeters, in any of the counties in the community for the days selected using thedays_included
option.
References
Al-Hamdan MZ, Crosson WL, Economou SA, Estes MG, Estes SM, Hemmings SN, Kent ST, Puckette M, Quattrochi DA, Rickman DL, Wade GM, McClure LA, 2014. Environmental public health applications using remotely sensed data. Geocarto International 29(1):85-98.
North America Land Data Assimilation System (NLDAS) Daily Precipitation years 1979-2011 on CDC WONDER Online Database, released 2012. http://wonder.cdc.gov/wonder/help/Precipitation.html
Rui H, Mocko D, 2014. README Document for North America Land Data Assimilation System Phase 2 (NLDAS-2) Products. Goddard Earth Sciences Data and Information Services Center.
Examples
# Ensure that data package is available before running the example.
# If it is not, see the `hurricaneexposure` package vignette for details
# on installing the required data package.
if (requireNamespace("hurricaneexposuredata", quietly = TRUE)) {
communities <- data.frame(community_name = c(rep("ny", 6), "no", "new"),
fips = c("36005", "36047", "36061",
"36085", "36081", "36119",
"22071", "51700"))
rain_storm_df <- multi_county_rain(communities = communities,
start_year = 1995, end_year = 2005,
rain_limit = 100, dist_limit = 100)
}