moranbi.cluster {bispdep} | R Documentation |

## Maps of BiLISA clusters and statistical significance associated with BiLISA

### Description

Using the `localmoran.bi`

function, create a Bivariate Local Indicators of Spatial Association (BiLISA) cluster map and corresponding significance map. The maps are made by calculating the BiLISAs from `localmoran.bi`

for each of the spatial units and then the statistical significance is evaluated to determine the spatial clusters and outliers.

### Usage

```
moranbi.cluster(varY, varX, listw, zero.policy = NULL, polygons, conditional=TRUE,
significant = TRUE, alternative = "two.sided", pleg, ...)
```

### Arguments

`varY` |
a numeric vector the same length as the neighbours list in listw and |

`varX` |
a numeric vector the same length as the neighbours list in listw and |

`listw` |
a neighbours list with spatial weights. From package spdep:
a listw object. Use |

`zero.policy` |
by default = NULL, if FALSE stop with error for any empty
neighbour sets, if TRUE permit the weights list to be formed with zero-length
weights vectors. Parameter inherited from the |

`polygons` |
SpatialPolygons, SpatialPolygonsDataFrame or sfc_POLYGON object |

`conditional` |
default TRUE: expectation and variance are calculated using the conditional randomization null (Sokal 1998 Eqs. A7 & A8). Elaboration of these changes available in Sauer et al. (2021). If FALSE: expectation and variance are calculated using the total randomization null (Sokal 1998 Eqs. A3 & A4). |

`significant` |
by default is TRUE, if FALSE the significant map is not created |

`alternative` |
by default is "two.sided". Type of alternative hypothesis test. Other values are "less" or "greater". |

`pleg` |
the x and y co-ordinates to be used to position the legend. They can be specified by keyword or in any way which is accepted by |

`...` |
other parameters similar to internal function |

### Details

Using the function `localmoran.bi`

create the Bivariate Local Indicators of Spatial
Association - BiLISA Cluster Map and the corresponding Significance Map.
The significance map is done testing the null hypothesis (Ho) of zero spatial
autocorrelation for each spatial unit and then plotting a choropleth map with this
legend values: (Not Significant, p-value=0.05, p-value= 0.01, p-value=0.001,
p-value=0.0001, and Neighborless). Maps can represent concentrations of similar (cluster)
or dissimilar values (spatial outliers). Most significant clustered spatial units are
those with p-values smaller than 0.0001. Not significant clustered spatial units are
those with p-values greater than 0.05. BiLISA Cluster Map is done based on the
significance map but the choropleth legend is different (Not - Significant, High-High, Low-Low, Low-High, High-Low, and Neighborless).

### Value

one or two maps

### Links

### See Also

Bivariate Moran's Ixy:

`moran.bi`

Plot Bivariate Moran's Ixy:

`moranbi.plot`

Bivariate Moran's Ixy Test:

`moranbi.test`

Create object "nb":

`poly2nb`

Create object "listw"/"nb":

`nb2listw`

### Examples

```
library(spdep)
data(columbus)
columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE)
plot(st_geometry(columbus))
col_nbq <- poly2nb(columbus)
a.lw <- nb2listw(col_nbq, style="W")
moranbi.cluster(columbus$CRIME, columbus$HOVAL, a.lw, zero.policy = FALSE,
conditional=TRUE, st_geometry(columbus), significant=TRUE,
pleg = "topleft")
moranbi.cluster(columbus$CRIME, columbus$HOVAL, a.lw, zero.policy = FALSE,
st_geometry(columbus), significant=TRUE, alternative="greater",
pleg = "topleft")
```

*bispdep*version 1.0-0 Index]