Moran's I is calculated for each polygon based on the neighbor and weight lists.
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.
Value
a data.frame containing the columns ii, eii, var_ii, z_ii, p_ii, p_ii_sim, and p_folded_sim. For more details please see spdep::localmoran_perm().
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()
Examples
library(magrittr)
lisa <- guerry %>%
dplyr::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: 47680 ymin: 1703258 xmax: 1031401 ymax: 2677441
#> CRS: NA
#> # A tibble: 85 × 41
#> code_dept count ave_id…¹ dept region depar…² crime…³ crime…⁴ liter…⁵ donat…⁶
#> <fct> <dbl> <dbl> <int> <fct> <fct> <int> <int> <int> <int>
#> 1 01 1 49 1 E Ain 28870 15890 37 5098
#> 2 02 1 812 2 N Aisne 26226 5521 51 8901
#> 3 03 1 1418 3 C Allier 26747 7925 13 10973
#> 4 04 1 1603 4 E Basses… 12935 7289 46 2733
#> 5 05 1 1802 5 E Hautes… 17488 8174 69 6962
#> 6 07 1 2249 7 S Ardeche 9474 10263 27 3188
#> 7 08 1 35395 8 N Ardenn… 35203 8847 67 6400
#> 8 09 1 2526 9 S Ariege 6173 9597 18 3542
#> 9 10 1 34410 10 E Aube 19602 4086 59 3608
#> 10 11 1 2807 11 S Aude 15647 10431 34 2582
#> # … with 75 more rows, 31 more variables: 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>, skewness <dbl>, …
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names