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Given two continuous numeric variables, calculate the bivariate Moran's I. See details for more.
global_moran_bv(x, y, nb, wt, nsim = 99, scale = TRUE)
an object of class boot
a numeric vector of same length as y.
y
a numeric vector of same length as x.
x
a neighbor list object for example as created by st_contiguity().
st_contiguity()
a weights list as created by st_weights().
st_weights()
the number of simulations to run.
default TRUE.
TRUE
The Global Bivariate Moran is defined as
\( I_B = \frac{\Sigma_i(\Sigma_j{w_{ij}y_j\times x_i})}{\Sigma_i{x_i^2}} \)
It is important to note that this is a measure of autocorrelation of X with the spatial lag of Y. As such, the resultant measure may overestimate the amount of spatial autocorrelation which may be a product of the inherent correlation of X and Y.
Global Spatial Autocorrelation (2): Bivariate, Differential and EB Rate Moran Scatter Plot, Luc Anselin
Other global_moran: global_moran(), global_moran_perm(), global_moran_test(), local_moran_bv()
global_moran()
global_moran_perm()
global_moran_test()
local_moran_bv()
x <- guerry_nb$crime_pers y <- guerry_nb$wealth nb <- guerry_nb$nb wt <- guerry_nb$wt global_moran_bv(x, y, nb, wt)
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