local_joincount_bv.RdThe bivariate join count (BJC) evaluates event occurrences in predefined regions and tests if the co-occurrence of events deviates from complete spatial randomness.
local_joincount_bv(
x,
z,
listw,
nsim = 199,
alternative = "two.sided"
)a binary variable either numeric or logical
a binary variable either numeric or logical with the same length as x
a listw object containing binary weights created, for example, with nb2listw(nb, style = "B")
the number of conditional permutation simulations
default "greater". One of "less" or "greater".
There are two cases that are evaluated in the bivariate join count. The first being in-situ colocation (CLC) where xi = 1 and zi = 1. The second is the general form of the bivariate join count (BJC) that is used when there is no in-situ colocation.
The BJC case "is useful when x and z cannot occur in the same location, such as when x and z correspond to two different values of a single categorical variable" or "when x and z can co-locate, but do not" (Anselin and Li, 2019). Whereas the CLC case is useful in evaluating simultaneous occurrences of events.
The local bivariate join count statistic requires a binary weights list which can be generated with nb2listw(nb, style = "B").
P-values are only reported for those regions that match the CLC or BJC criteria. Others will not have an associated p-value.
P-values are estimated using a conditional permutation approach. This creates a reference distribution from which the observed statistic is compared.
a data.frame with two columns join_count and p_sim and number of rows equal to the length of arguments x.
Anselin, L., & Li, X. (2019). Operational Local Join Count Statistics for Cluster Detection. Journal of geographical systems, 21(2), 189–210. doi:10.1007/s10109-019-00299-x
data("oldcol")
listw <- nb2listw(COL.nb, style = "B")
# Colocation case
x <- COL.OLD[["CP"]]
z <- COL.OLD[["EW"]]
set.seed(1)
res <- local_joincount_bv(x, z, listw)
na.omit(res)
#> join_count p_sim
#> 9 2 0.480
#> 19 3 0.180
#> 20 4 0.020
#> 21 3 0.040
#> 22 3 0.060
#> 23 4 0.070
#> 24 5 0.005
#> 27 2 0.300
#> 28 3 0.050
#> 29 5 0.010
#> 30 5 0.010
#> 31 9 0.005
#> 32 4 0.090
#> 33 4 0.055
# no colocation case
z <- 1 - x
set.seed(1)
res <- local_joincount_bv(x, z, listw)
na.omit(res)
#> join_count p_sim
#> 9 2 0.065
#> 19 3 0.300
#> 20 1 0.015
#> 21 1 0.040
#> 22 1 0.045
#> 23 3 0.165
#> 24 1 0.000
#> 27 2 0.230
#> 28 1 0.050
#> 29 1 0.000
#> 36 1 0.000
#> 37 2 0.005
#> 38 2 0.185
#> 39 2 0.265
#> 40 1 0.005
#> 43 1 0.070