R/bg.pts.R

In ejanalysis/proxistat: Create a Proximity Score in Each Census Unit, Based on Distances to Specified Points (as in EPA's EJScreen tool, for example)

#' @name bg.pts
#' @docType data
#' @aliases bg.pts
#' @title Block group internal points from Census Bureau for 2020 geographies
#' @description The population weighted mean of block internal points, 
#'   within each block group.
#' @details This data set provides a data.frame with 242,335 block groups, with 5 variables.
#'   It has PR but lacks "AS" "GU" "MP" "VI" 
#'   The population weighted mean not spatial mean was used
#'   to summarize the lat lon values of all Census 2020 blocks in a given block group.
#'   
#' @seealso \pkg{EJAM::proxistat2}  \pkg{EJAM::bgpts} and  \pkg{acs}  and for older data \pkg{UScensus2010blocks}  
#' @source Derived from Census Bureau, obtained 10/2022. 
#'   bg.pts <- as.data.frame(copy(EJAM::bgpts)) # see EJAM package for details 
#'   names(bg.pts) <- c('bgid', 'lat','lon','FIPS','blockcount')
#'   bg.pts <- bg.pts[ , c('FIPS', 'lat', 'lon', 'bgid', 'blockcount')]
#'   bg.pts <- metadata_add(bg.pts)
#'   usethis::use_data(bg.pts)
#' @keywords datasets
#' @format A data.frame of block groups, with these variables: \cr\cr
#' \preformatted{
#' 'data.frame':	242335 obs. of  5 variables:
#'  $ FIPS      : chr  "010010201001" "010010201002" "010010202001" "010010202002" ...
#'  $ lat       : num  32.5 32.5 32.5 32.5 32.5 ...
#'  $ lon       : num  -86.5 -86.5 -86.5 -86.5 -86.5 ...
#'  $ bgid      : int  1 2 3 4 5 6 7 8 9 10 ...
#'  $ blockcount: int  26 30 14 27 21 21 16 29 29 18 ...
#'  - attr(*, "census_version")= num 2020
#'  - attr(*, "acs_version")= chr "2016-2020"
#'  - attr(*, "acs_releasedate")= chr "3/17/2022"
#'  - attr(*, "ejscreen_version")= chr "2.1"
#'  - attr(*, "ejscreen_releasedate")= chr "October 2022"
#'  - attr(*, "ejscreen_pkg_data")= chr "bg22"
#' }
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