Median center iteratively calculates the point that minimizes distance to all features. One can specify the groups to calculate individual centers for and weights for each individual point. It is analagous to the ArcGIS Pro Median Center tool.
It uses the methodology introduced by Kuhn and Kuenne (1962).
Currently, median center is only implemenented for projected data.
Arguments
- x
Input POINT or POLYGON simple features
- group
column name(s) specifying groups to calculate individual mean centers for
- weight
name of numeric weight column specifying an individual point's contribution to the mean center
- tolerance
numeric threshold determining when an estimate improvement is sufficiently small enough to stop iterating (smaller = slower, but more precision)
- ...
expressions passed to
dplyr::summarise()
Examples
df <- data.frame(
lon = c(-88, -90, -92, -89, -90),
lat = c(42, 40, 30, 32, 42),
grp = c("a", "b", "a", "b", "a"),
wt = c(1, 1, 1, 1, 1)
)
x <- sf::st_as_sf(df, coords = c("lon", "lat"), crs = 4326)
x_transformed <- sf::st_transform(x, crs = "ESRI:102003")
median_center(x_transformed, group = "grp", weight = "wt")
#> Simple feature collection with 2 features and 1 field
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: 542640.4 ymin: -147278.5 xmax: 582550.3 ymax: 482723.9
#> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic
#> # A tibble: 2 × 2
#> grp geometry
#> <chr> <POINT [m]>
#> 1 a (542640.4 482723.9)
#> 2 b (582550.3 -147278.5)
x_transformed |>
dplyr::group_by(grp) |>
median_center(weight = "wt")
#> Simple feature collection with 2 features and 1 field
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: 542640.4 ymin: -147278.5 xmax: 582550.3 ymax: 482723.9
#> Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic
#> # A tibble: 2 × 2
#> grp geometry
#> <chr> <POINT [m]>
#> 1 a (542640.4 482723.9)
#> 2 b (582550.3 -147278.5)