make_EV: Prepare data for spatial filtering

In geostan: Bayesian Spatial Analysis

Prepare data for spatial filtering

Description

Prepare data for spatial filtering

Usage

make_EV(C, nsa = FALSE, threshold = 0.2, values = FALSE)

Arguments

C	A binary spatial weights matrix. See shape2mat.
nsa	Logical. Default of nsa = FALSE excludes eigenvectors capturing negative spatial autocorrelation. Setting nsa = TRUE will result in a candidate set of EVs that contains eigenvectors representing positive and negative SA.
threshold	Defaults to threshold=0.2 to exclude eigenvectors representing spatial autocorrelation levels that are less than threshold times the maximum possible Moran coefficient achievable for the given spatial connectivity matrix. If theshold = 0, all eigenvectors will be returned (however, the eigenvector of constants (with eigenvalue of zero) will be dropped automatically).
values	Should eigenvalues be returned also? Defaults to FALSE.

Details

Returns a set of eigenvectors related to the Moran coefficient (MC), limited to those eigenvectors with |MC| > threshold if nsa = TRUE or MC > threshold if nsa = FALSE, optionally with corresponding eigenvalues.

Value

A data.frame of eigenvectors for spatial filtering. If values=TRUE then a named list is returned with elements eigenvectors and eigenvalues.

Source

Daniel Griffith and Yongwan Chun. 2014. "Spatial Autocorrelation and Spatial Filtering." in M. M. Fischer and P. Nijkamp (eds.), Handbook of Regional Science. Springer.

See Also

stan_esf, mc

Examples

library(ggplot2)
data(georgia)
C <- shape2mat(georgia, style = "B")
EV <- make_EV(C)
head(EV)

ggplot(georgia) +
  geom_sf(aes(fill = EV[,1])) +
  scale_fill_gradient2()

geostan documentation built on April 3, 2025, 10:04 p.m.