# local_joincount_bv: Calculate the local bivariate join count In spdep: Spatial Dependence: Weighting Schemes, Statistics

 local_joincount_bv R Documentation

## Calculate the local bivariate join count

### Description

The bivariate join count (BJC) evaluates event occurrences in predefined regions and tests if the co-occurrence of events deviates from complete spatial randomness.

### Usage

```local_joincount_bv(
x,
z,
listw,
nsim = 199,
alternative = "two.sided"
)
```

### Arguments

 `x` a binary variable either numeric or logical `z` a binary variable either numeric or logical with the same length as `x` `listw` a listw object containing binary weights created, for example, with `nb2listw(nb, style = "B")` `nsim` the number of conditional permutation simulations `alternative` default `"greater"`. One of `"less"` or `"greater"`.

### Details

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.

### Value

a `data.frame` with two columns `join_count` and `p_sim` and number of rows equal to the length of arguments `x`.

### Author(s)

Josiah Parry josiah.parry@gmail.com

### References

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

### Examples

```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)
# no colocation case
z <- 1 - x
set.seed(1)
res <- local_joincount_bv(x, z, listw)
na.omit(res)
```

spdep documentation built on March 7, 2023, 7:27 p.m.