moran_test: test global spatial autocorrelation

In sdsfun: Spatial Data Science Complementary Features

test global spatial autocorrelation

Description

Spatial autocorrelation test based on global moran index.

Usage

moran_test(sfj, wt = NULL, alternative = "greater", symmetrize = FALSE)

Arguments

sfj	An sf object or can be converted to sf by sf::st_as_sf().
wt	(optional) Spatial weight matrix. Must be a matrix class. If wt is not provided, sdsfun will use a first-order queen adjacency binary matrix.
alternative	(optional) Specification of alternative hypothesis as greater (default), lower, or two.sided.
symmetrize	(optional) Whether or not to symmetrize the asymmetrical spatial weight matrix wt by: 1/2 * (wt + wt'). Default is FALSE.

Value

A list with moran_test class and result stored on the result tibble. Which contains the following information for each variable:

MoranI

observed value of the Moran coefficient

EI

expected value of Moran's I

VarI

variance of Moran's I (under normality)

ZI

standardized Moran coefficient

PI

p-value of the test statistic

Note

This is a ⁠C++⁠ implementation of the MI.vec function in spfilteR package, and embellishes the console output.

The return result of this function is actually a list, please access the result tibble using ⁠$result⁠.

The non-numeric columns of the attribute columns in sfj are ignored.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
moran_test(gzma)

sdsfun documentation built on April 3, 2025, 8:39 p.m.