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Calculate the Local Moran's I Statistic

Source: R/lisa.R
local_moran.Rd

Moran's I is calculated for each polygon based on the neighbor and weight lists.

Usage

local_moran(x, nb, wt, alternative = "two.sided", nsim = 499, ...)

Arguments

x

A numeric vector.

nb

a neighbor list object for example as created by st_contiguity().

wt

a weights list as created by st_weights().

alternative

default "two.sided". Should be one of "greater", "less", or "two.sided" to specify the alternative hypothesis.

nsim

The number of simulations to run.

...

See ?spdep::localmoran_perm() for more options.

Details

local_moran() calls spdep::localmoran_perm() and calculates the Moran I for each polygon. As well as provide simulated p-values.

See also

Other stats: st_lag(), st_nb_dists()

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union

lisa <- guerry %>%
  mutate(nb = st_contiguity(geometry),
         wt = st_weights(nb),
         moran = local_moran(crime_pers, nb, wt))

# unnest the dataframe column
tidyr::unnest(lisa, moran)
#> Simple feature collection with 85 features and 40 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 × 41
#>    code_dept count ave_id_geo  dept region department   crime_pers crime_prop
#>    <fct>     <dbl>      <dbl> <int> <fct>  <fct>             <int>      <int>
#>  1 01            1         49     1 E      Ain               28870      15890
#>  2 02            1        812     2 N      Aisne             26226       5521
#>  3 03            1       1418     3 C      Allier            26747       7925
#>  4 04            1       1603     4 E      Basses-Alpes      12935       7289
#>  5 05            1       1802     5 E      Hautes-Alpes      17488       8174
#>  6 07            1       2249     7 S      Ardeche            9474      10263
#>  7 08            1      35395     8 N      Ardennes          35203       8847
#>  8 09            1       2526     9 S      Ariege             6173       9597
#>  9 10            1      34410    10 E      Aube              19602       4086
#> 10 11            1       2807    11 S      Aude              15647      10431
#> # … with 75 more rows, and 33 more variables: 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 [°]>, nb <nb>, wt <list>, ii <dbl>, eii <dbl>,
#> #   var_ii <dbl>, z_ii <dbl>, p_ii <dbl>, p_ii_sim <dbl>, p_folded_sim <dbl>, …