moran: Calculate Global Moran's I statistic

In swash: Health Geography Toolbox for Model-Based Analysis of Infections Panel Data

Calculate Global Moran's I statistic

Description

Calculating the Global Moran's I statistic for spatial autocorrelation from an nbmatrix object

Usage

moran(
  object,
  randomization = TRUE,
  alternative = "greater",
  verbose = FALSE
  )

Arguments

object	Object of class nbmatrix
randomization	bool argument that indicates which test is conducted (under randomization or normality); passed to spdep::moran.test
alternative	a character string specifying the alternative hypothesis, must be one of greater (default), less or two.sided; passed to spdep::moran.test
verbose	bool argument which indicates whether progress messages are displayed

Details

Moran's I (Moran 1950) is a statistical measure of spatial autocorrelation. Its calculation requires a spatial weights matrix that defines the spatial relationships between observations and assigns weights to neighboring observations (e.g., binary weights based on contiguity). A typical application is, for example, investigating whether spatial autocorrelation exists in the incidence of infectious diseases, that is, whether areas with high (or low) incidence are surrounded by neighboring areas that also exhibit high (or low) incidence (Wieland 2020, 2022). This function internally uses spdep::moran.test for calculation (see the corresponding documentation). The results are written in the nbmatrix instance and displayed in the summary.

Value

object of class nbmatrix-class

Author(s)

Thomas Wieland

References

Cliff AD, Ord JK (1981) Spatial Processes: Models & Applications. https://archive.org/details/spatialprocesses0000clif

GeoDa Center (2026) GeoDa. An Introduction to Spatial Data Science. https://geodacenter.github.io/

Moran PAP (1950) Notes on Continuous Stochastic Phenomena. Biometrika 37(1/2) 17–23. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.2307/2332142")}

Wieland T (2020) Flatten the Curve! Modeling SARS-CoV-2/COVID-19 Growth in Germany at the County Level. REGION 7(2), 43–83. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.18335/region.v7i2.324")}

Wieland T (2022) Spatial patterns of excess mortality in the first year of the COVID-19 pandemic in Germany. European Journal of Geography 13(4), 18-33. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.48088/ejg.t.wie.13.4.018.033")}

See Also

nbmatrix, nbstat, getisord, gstar

Examples

data(RKI_Corona_counties)
# German counties (Source: Robert Koch Institute)

Corona_nbmat <- 
  nbmatrix (
    RKI_Corona_counties, 
    ID_col="AGS",
    verbose = TRUE
  )
# Creating neighborhood matrix

Corona_nbstat2 <- nbstat(
  Corona_nbmat,
  link_data = RKI_Corona_counties, 
  ID_col = "AGS", 
  data_col = "cases_per_", 
  func = NULL,
  verbose = TRUE
)
# New nbmatrix instance
# Defining "cases_per_" (COVID-19 cases per 100,000 inhabitants) as analysis column

Corona_SpatialStatistics <- moran(
  Corona_nbstat2,
  verbose = TRUE
  )
# Calculating Global Moran's I

summary(Corona_SpatialStatistics)
# Summary of nbmatrix object

swash documentation built on Oct. 18, 2026, 5:06 p.m.