# localmoran: Local Moran's I statistic In spdep: Spatial Dependence: Weighting Schemes, Statistics and Models

## Description

The local spatial statistic Moran's I is calculated for each zone based on the spatial weights object used. The values returned include a Z-value, and may be used as a diagnostic tool. The statistic is:

I_i = \frac{(x_i-\bar{x})}{{∑_{k=1}^{n}(x_k-\bar{x})^2}/(n-1)}{∑_{j=1}^{n}w_{ij}(x_j-\bar{x})}

, and its expectation and variance are given in Anselin (1995).

## Usage

 1 2 3 localmoran(x, listw, zero.policy=NULL, na.action=na.fail, alternative = "greater", p.adjust.method="none", mlvar=TRUE, spChk=NULL, adjust.x=FALSE) 

## Arguments

 x a numeric vector the same length as the neighbours list in listw listw a listw object created for example by nb2listw zero.policy default NULL, use global option value; if TRUE assign zero to the lagged value of zones without neighbours, if FALSE assign NA na.action a function (default na.fail), can also be na.omit or na.exclude - in these cases the weights list will be subsetted to remove NAs in the data. It may be necessary to set zero.policy to TRUE because this subsetting may create no-neighbour observations. Note that only weights lists created without using the glist argument to nb2listw may be subsetted. If na.pass is used, zero is substituted for NA values in calculating the spatial lag. (Note that na.exclude will only work properly starting from R 1.9.0, na.omit and na.exclude assign the wrong classes in 1.8.*) alternative a character string specifying the alternative hypothesis, must be one of greater (default), less or two.sided. p.adjust.method a character string specifying the probability value adjustment for multiple tests, default "none"; see p.adjustSP. Note that the number of multiple tests for each region is only taken as the number of neighbours + 1 for each region, rather than the total number of regions. mlvar default TRUE: values of local Moran's I are reported using the variance of the variable of interest (sum of squared deviances over n), but can be reported as the sample variance, dividing by (n-1) instead; both are used in other implementations. spChk should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use get.spChkOption() adjust.x default FALSE, if TRUE, x values of observations with no neighbours are omitted in the mean of x

## Details

The values of local Moran's I are divided by the variance (or sample variance) of the variable of interest to accord with Table 1, p. 103, and formula (12), p. 99, in Anselin (1995), rathar than his formula (7), p. 98. The variance of the local Moran statistic is taken from Sokal et al. (1998), equation 5 p. 334 and A4*, p. 351. By default, the implementation divides by n, not (n-1) in calculating the variance and higher moments.

## Value

 Ii local moran statistic E.Ii expectation of local moran statistic Var.Ii variance of local moran statistic Z.Ii standard deviate of local moran statistic Pr() p-value of local moran statistic

## Author(s)

Roger Bivand [email protected]

## References

Anselin, L. 1995. Local indicators of spatial association, Geographical Analysis, 27, 93–115; Getis, A. and Ord, J. K. 1996 Local spatial statistics: an overview. In P. Longley and M. Batty (eds) Spatial analysis: modelling in a GIS environment (Cambridge: Geoinformation International), 261–277; Sokal, R. R, Oden, N. L. and Thomson, B. A. 1998. Local Spatial Autocorrelation in a Biological Model. Geographical Analysis, 30. 331–354; Bivand RS, Wong DWS 2018 Comparing implementations of global and local indicators of spatial association. TEST, 27(3), 716–748 https://doi.org/10.1007/s11749-018-0599-x

localG
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 data(afcon, package="spData") oid <- order(afcon$id) resI <- localmoran(afcon$totcon, nb2listw(paper.nb)) printCoefmat(data.frame(resI[oid,], row.names=afcon$name[oid]), check.names=FALSE) hist(resI[,5]) mean(resI[,1]) sum(resI[,1])/Szero(nb2listw(paper.nb)) moran.test(afcon$totcon, nb2listw(paper.nb)) # note equality for mean() only when the sum of weights equals # the number of observations (thanks to Juergen Symanzik) resI <- localmoran(afcon$totcon, nb2listw(paper.nb), p.adjust.method="bonferroni") printCoefmat(data.frame(resI[oid,], row.names=afcon$name[oid]), check.names=FALSE) hist(resI[,5]) totcon <-afcon$totcon is.na(totcon) <- sample(1:length(totcon), 5) totcon resI.na <- localmoran(totcon, nb2listw(paper.nb), na.action=na.exclude, zero.policy=TRUE) if (class(attr(resI.na, "na.action")) == "exclude") { print(data.frame(resI.na[oid,], row.names=afcon$name[oid]), digits=2) } else print(resI.na, digits=2) resG <- localG(afcon$totcon, nb2listw(include.self(paper.nb))) print(data.frame(resG[oid], row.names=afcon$name[oid]), digits=2)