Calculate spatial weights

Calculate polygon spatial weights from a nb list. See spdep::nb2listw() for further details.

Usage

st_weights(nb, style = "W", allow_zero = NULL, ...)

Arguments

nb

A neighbor list object as created by st_neighbors().

style

Default "W" for row standardized weights. This value can also be "B", "C", "U", "minmax", and "S". See spdep::nb2listw() for details.

allow_zero

If TRUE, assigns zero as lagged value to zone without neighbors.

...

additional arguments passed to spdep::nb2listw().

Details

Under the hood, st_weights() creates a listw object and then extracts the weights elements from it as the neighbours element is already--presumably--already existent in the neighbors list you've already created. listw objects are recreated using recreate_listw() when calculating other statistics.

Examples

guerry %>%
 dplyr::mutate(nb = st_contiguity(geometry),
               wt = st_weights(nb),
               .before = 1)
#> Simple feature collection with 85 features and 28 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -5.139026 ymin: 42.33349 xmax: 8.23032 ymax: 51.08939
#> Geodetic CRS:  WGS 84
#> # A tibble: 85 × 29
#>    nb        wt    code_dept count ave_id_geo  dept region department crime_pers
#>  * <nb>      <lis> <fct>     <dbl>      <dbl> <int> <fct>  <fct>           <int>
#>  1 <int [4]> <dbl… 01            1         49     1 E      Ain             28870
#>  2 <int [6]> <dbl… 02            1        812     2 N      Aisne           26226
#>  3 <int [6]> <dbl… 03            1       1418     3 C      Allier          26747
#>  4 <int [4]> <dbl… 04            1       1603     4 E      Basses-Al…      12935
#>  5 <int [3]> <dbl… 05            1       1802     5 E      Hautes-Al…      17488
#>  6 <int [7]> <dbl… 07            1       2249     7 S      Ardeche          9474
#>  7 <int [3]> <dbl… 08            1      35395     8 N      Ardennes        35203
#>  8 <int [3]> <dbl… 09            1       2526     9 S      Ariege           6173
#>  9 <int [5]> <dbl… 10            1      34410    10 E      Aube            19602
#> 10 <int [5]> <dbl… 11            1       2807    11 S      Aude            15647
#> # … with 75 more rows, and 20 more variables: crime_prop <int>, literacy <int>,
#> #   donations <int>, infants <int>, suicides <int>, main_city <ord>,
#> #   wealth <int>, commerce <int>, clergy <int>, crime_parents <int>,
#> #   infanticide <int>, donation_clergy <int>, lottery <int>, desertion <int>,
#> #   instruction <int>, prostitutes <int>, distance <dbl>, area <int>,
#> #   pop1831 <dbl>, geometry <MULTIPOLYGON [°]>

# using geometry column directly
nb <- st_contiguity(guerry$geometry)
wt <- st_weights(nb)
wt[1:3]
#> [[1]]
#> [1] 0.25 0.25 0.25 0.25
#> 
#> [[2]]
#> [1] 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667
#> 
#> [[3]]
#> [1] 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667
#>