Local Indicators for Categorical Data

Local indicators for categorical data combine a measure of local composition in a window given by the per-observation set of neighbouring observations, with a local multi-category joincount test simplified to neighbours with the same or different categories compared to the focal observation

Usage

licd_multi(fx, listw, zero.policy = attr(listw, "zero.policy"), adjust.n = TRUE,
 nsim = 0L, iseed = NULL, no_repeat_in_row = FALSE, control = list())

Arguments

fx: a factor with two or more categories, of the same length as the neighbours and weights objects in listw
listw: a listw object created for example by nb2listw
zero.policy: default attr(listw, "zero.policy") as set when listw was created, if attribute not set, use global option value; if TRUE assign zero to the lagged value of zones without neighbours, if FALSE assign NA
adjust.n: default TRUE, if FALSE the number of observations is not adjusted for no-neighbour observations, if TRUE, the number of observations is adjusted
nsim: default 0, number of conditonal permutation simulations
iseed: default NULL, used to set the seed; the output will only be reproducible if the count of CPU cores across which computation is distributed is the same
no_repeat_in_row: default FALSE, if TRUE, sample conditionally in each row without replacements to avoid duplicate values, https://github.com/r-spatial/spdep/issues/124
control: comp_binary=TRUE, binomial_punif_alternative="greater", jcm_same_punif_alternative="less", jcm_diff_punif_alternative="greater"

Details

The original code may be found at doi:10.5281/zenodo.4283766

Value

local_comp: data.frame object with LICD local composition columns: ID, category_i, count_like_i, prop_i, count_nbs_i, pbinom_like_BW, pbinom_unlike_BW, pbinom_unlike_BW_alt, chi_BW_i, chi_K_i, anscombe_BW
local_config: data.frame object with LICD local configuration columns: ID, jcm_chi_obs, jcm_count_BB_obs, jcm_count_BW_obs, jcm_count_WW_obs, pval_jcm_obs_BB, pval_jcm_obs_WW, pval_jcm_obs_BW
local_comp_sim: data.frame object with permutation-based LICD local composition columns: ID, pbinom_like_BW, pbinom_unlike_BW, pbinom_unlike_BW_alt, rank_sim_chi_BW, rank_sim_chi_K, rank_sim_anscombe
local_config_sim: data.frame object with permutation-based LICD local configuration columns: ID, jcm_chi_sim_rank, pval_jcm_obs_BB_sim, pval_jcm_obs_BW_sim, pval_jcm_obs_WW_sim

References

Cliff, A. D., Ord, J. K. 1981 Spatial processes, Pion, p. 20;

Upton, G., Fingleton, B. 1985 Spatial data analysis by example: point pattern and qualitative data, Wiley, pp. 158–170;

Boots, B., 2003. Developing local measures of spatial association for categorical data. Journal of Geographical Systems 5, 139–160;

Boots, B., 2006. Local configuration measures for categorical spatial data: binary regular lattices. Journal of Geographical Systems 8 (1), 1–24;

Pietrzak, M.B., Wilk, J., Kossowski, T., Bivand, R.S., 2014. The application of local indicators for categorical data (LICD) in the spatial analysis of economic development. Comparative Economic Research 17 (4), 203–220 doi:10.2478/cer-2014-0041 ;

Bivand, R.S., Wilk, J., Kossowski, T., 2017. Spatial association of population pyramids across Europe: The application of symbolic data, cluster analysis and join-count tests. Spatial Statistics 21 (B), 339–361 doi:10.1016/j.spasta.2017.03.003 ;

Francesco Carrer, Tomasz M. Kossowski, Justyna Wilk, Michał B. Pietrzak, Roger S. Bivand, The application of Local Indicators for Categorical Data (LICD) to explore spatial dependence in archaeological spaces, Journal of Archaeological Science, 126, 2021, doi:10.1016/j.jas.2020.105306

Author

Roger Bivand Roger.Bivand@nhh.no based on earlier code by Tomasz M. Kossowski, Justyna Wilk and Michał B. Pietrzak

Note

In order to increase the numbers of neighbours using nblag and nblag_cumul is advisable; use of binary weights is advised and are in any case used for the composition measure

Examples

columbus <- st_read(system.file("shapes/columbus.gpkg", package="spData")[1], quiet=TRUE)
HICRIME <- cut(columbus$CRIME, breaks=c(0,35,80), labels=c("low","high"))
(nb <- poly2nb(columbus))
#> Neighbour list object:
#> Number of regions: 49 
#> Number of nonzero links: 236 
#> Percentage nonzero weights: 9.829238 
#> Average number of links: 4.816327 
lw <- nb2listw(nblag_cumul(nblag(nb, 2)), style="B")
obj <- licd_multi(HICRIME, lw)
str(obj)
#> List of 4
#>  $ local_comp      :'data.frame':	49 obs. of  11 variables:
#>   ..$ ID                  : int [1:49] 1 2 3 4 5 6 7 8 9 10 ...
#>   ..$ category_i          : num [1:49] 1 1 1 1 2 1 1 2 1 1 ...
#>   ..$ count_like_i        : num [1:49] 4 4 6 7 12 7 2 8 10 8 ...
#>   ..$ prop_i              : num [1:49] 0.51 0.51 0.51 0.51 0.49 ...
#>   ..$ count_nbs_i         : num [1:49] 5 6 11 14 22 14 11 14 25 17 ...
#>   ..$ pbinom_like_BW      : num [1:49] 0.965 0.881 0.702 0.575 0.769 ...
#>   ..$ pbinom_unlike_BW    : num [1:49] 0.0346 0.1192 0.2979 0.4255 0.2313 ...
#>   ..$ pbinom_unlike_BW_alt: num [1:49] 0.201 0.363 0.528 0.634 0.379 ...
#>   ..$ chi_BW_i            : num [1:49] 1.68033 0.58778 0.0547 0.00583 0.27273 ...
#>   ..$ chi_K_i             : num [1:49] 1.68033 0.58778 0.0547 0.00583 0.27273 ...
#>   ..$ anscombe_BW         : num [1:49] 1.06 0.936 0.828 0.785 0.829 ...
#>  $ local_config    :'data.frame':	49 obs. of  8 variables:
#>   ..$ ID              : int [1:49] 1 2 3 4 5 6 7 8 9 10 ...
#>   ..$ jcm_chi_obs     : num [1:49] 0 0.0625 0.625 2.7468 14.1889 ...
#>   ..$ jcm_count_BB_obs: num [1:49] 6 6 11 12 55 15 1 27 30 25 ...
#>   ..$ jcm_count_BW_obs: num [1:49] 4 7 23 31 50 36 15 31 73 53 ...
#>   ..$ jcm_count_WW_obs: num [1:49] 0 1 10 20 21 19 32 9 61 24 ...
#>   ..$ pval_jcm_obs_BB : num [1:49] 1 0.207108 0.70232 0.820405 0.000214 ...
#>   ..$ pval_jcm_obs_WW : num [1:49] 1 0.3946 0.0981 0.0243 0.7839 ...
#>   ..$ pval_jcm_obs_BW : num [1:49] 1.00 1.75e-01 2.08e-01 5.10e-02 3.52e-06 ...
#>  $ local_comp_sim  : NULL
#>  $ local_config_sim: NULL
#>  - attr(*, "out")= num [1:49, 1:29] 1 1 1 1 2 1 1 2 1 1 ...
#>   ..- attr(*, "ncpus")= int 1
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : chr [1:29] "category_i" "count_like_i" "prop_i" "count_nbs_i" ...
#>  - attr(*, "ncpus")= int 1
#>  - attr(*, "nsim")= int 0
#>  - attr(*, "con")=List of 4
#>   ..$ comp_binary               : logi TRUE
#>   ..$ binomial_punif_alternative: chr "greater"
#>   ..$ jcm_same_punif_alternative: chr "less"
#>   ..$ jcm_diff_punif_alternative: chr "greater"
#>  - attr(*, "class")= chr [1:2] "licd" "list"
h_obj <- hotspot(obj)
str(h_obj)
#> List of 9
#>  $ ID              : int [1:49] 1 2 3 4 5 6 7 8 9 10 ...
#>  $ local_comp      : Factor w/ 2 levels "Cluster","Dispersed": 2 2 2 2 2 2 2 2 2 2 ...
#>  $ local_comp_sim  : NULL
#>  $ local_config    : Factor w/ 3 levels "Cluster","Dispersed",..: 3 3 3 3 2 3 3 2 3 3 ...
#>  $ local_config_sim: NULL
#>  $ both            : Factor w/ 6 levels "Cluster.Cluster",..: 6 6 6 6 4 6 6 4 6 6 ...
#>  $ both_sim        : NULL
#>  $ both_recode     : Factor w/ 4 levels "Clump","Cluster",..: 4 4 4 4 3 4 4 3 4 4 ...
#>  $ both_recode_sim : NULL
table(h_obj$both_recode)
#> 
#>      Clump    Cluster  Dispersed No cluster 
#>          8          3         11         27 
columbus$both <- h_obj$both_recode
plot(columbus[, "both"])

GDAL37 <- as.numeric_version(unname(sf_extSoftVersion()["GDAL"])) >= "3.7.0"
file <- "etc/shapes/GB_2024_southcoast_50m.gpkg.zip"
zipfile <- system.file(file, package="spdep")
if (GDAL37) {
    sc50m <- st_read(zipfile)
} else {
    td <- tempdir()
    bn <- sub(".zip", "", basename(file), fixed=TRUE)
    target <- unzip(zipfile, files=bn, exdir=td)
    sc50m <- st_read(target)
}
#> Reading layer `GB_2024_southcoast_50m' from data source 
#>   `/tmp/RtmpqhccSk/temp_libpath108a2257736002/spdep/etc/shapes/GB_2024_southcoast_50m.gpkg.zip' 
#>   using driver `GPKG'
#> Simple feature collection with 119 features and 19 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 82643.12 ymin: 5342.9 xmax: 640301.6 ymax: 187226.2
#> Projected CRS: OSGB36 / British National Grid
sc50m$Winner <- factor(sc50m$Winner, levels=c("Con", "Green", "Lab", "LD"))
plot(sc50m[,"Winner"], pal=c("#2297E6", "#61D04F", "#DF536B", "#F5C710"))

nb_sc_50m <- poly2nb(sc50m, row.names=as.character(sc50m$Constituency))
#> Warning: neighbour object has 2 sub-graphs;
#> if this sub-graph count seems unexpected, try increasing the snap argument.
sub2 <- attr(nb_sc_50m, "region.id")[attr(nb_sc_50m, "ncomp")$comp.id == 2L]
iowe <- match(sub2[1], attr(nb_sc_50m, "region.id"))
diowe <- c(st_distance(sc50m[iowe,], sc50m))
meet_criterion <- sum(diowe <= units::set_units(5000, "m"))
cands <- attr(nb_sc_50m, "region.id")[order(diowe)[1:meet_criterion]]
nb_sc_50m_iowe <- addlinks1(nb_sc_50m, from = cands[1],
 to = cands[3:meet_criterion])
ioww <- match(sub2[2], attr(nb_sc_50m, "region.id"))
dioww <- c(st_distance(sc50m[ioww,], sc50m))
meet_criterion <- sum(dioww <= units::set_units(5000, "m"))
cands <- attr(nb_sc_50m, "region.id")[order(dioww)[1:meet_criterion]]
nb_sc_50m_iow <- addlinks1(nb_sc_50m_iowe, from = cands[2], to = cands[3:meet_criterion])
nb_sc_1_2 <- nblag_cumul(nblag(nb_sc_50m_iow, 2))
lw <- nb2listw(nb_sc_1_2, style="B")
licd_obj <- licd_multi(sc50m$Winner, lw)
h_obj <- hotspot(licd_obj)
sc50m$both <- h_obj$both_recode
plot(sc50m[, "both"])

ljc <- local_joincount_uni(factor(sc50m$Winner == "LD"), chosen="TRUE", lw)
sc50m$LD_pv <- ljc[, 2]
plot(sc50m[, "LD_pv"], breaks=c(0, 0.025, 0.05, 0.1, 0.5, 1))