Global and local spatial autocorrelation on a lattice in spdep
conventions; R arm of the Python modules morie.fn.lac*.
Lacgear: Geary's C with randomisation or normality moments
(spdep::geary.test). Lacgetg: global Getis-Ord G
(spdep::globalG.test). Lacgmc: Geary's C permutation test,
permutations as the order of Philox uniforms, pseudo p-value
(1 + number of permuted C at most C) / (nsim + 1). Laclisa: local Moran's
\(I_i\) with the conditional randomisation moments
(spdep::localmoran defaults). Laclihh, Laclihl:
significant high-high clusters and high-low outliers. Laclimc:
conditional permutation pseudo p-values of \(I_i\), folded
(1 + smaller count of permuted values at least or at most the observed
one) / (nsim + 1).
Lacscat: Moran scatterplot quadrants (1 HH, 2 LH, 3 LL, 4 HL) and
slope. Lacscor: correlation of y with Wy and Lee's
\(L_{yy}\). Lacbivl: Lee's bivariate L (spdep::lee).
Lactest: Moran, Geary and Getis-Ord global tests.
Lacvgm: Matheron semivariogram with gstat default bins.
Usage
Lacgear(y, W, randomisation = TRUE)
Lacgetg(y, W)
Lacgmc(y, W, nsim = 99, seed = 0)
Laclisa(y, W)
Laclihh(y, W, p_thr = 0.05)
Laclihl(y, W, p_thr = 0.05)
Laclimc(y, W, nsim = 99, seed = 0)
Lacscat(y, W)
Lacscor(y, W)
Lacbivl(x, y, W)
Lactest(y, W)
Lacvgm(y, coords, n_lags = 15, cutoff = NULL)Arguments
- y
Variable on the n units.
- W
Spatial weights matrix (zero diagonal).
- randomisation
Randomisation (TRUE) or normality variance.
- nsim
Number of permutations.
- seed
Philox seed.
- p_thr
Significance threshold for the local tests.
- x
Second variable (
Lacbivl).- coords
n x 2 matrix of unit coordinates.
- n_lags
Number of distance bins.
- cutoff
Largest distance considered (default one third of the bounding-box diagonal).
References
Cliff, A. D. and Ord, J. K. (1981). Spatial Processes: Models and Applications. Pion, London.
Geary, R. C. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician 5, 115-145.
Getis, A. and Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis 24, 189-206.
Anselin, L. (1995). Local indicators of spatial association – LISA. Geographical Analysis 27, 93-115.
Sokal, R. R., Oden, N. L. and Thomson, B. A. (1998). Local spatial autocorrelation in a biological model. Geographical Analysis 30, 331-354.
Lee, S.-I. (2001). Developing a bivariate spatial association measure. Journal of Geographical Systems 3, 369-385.
Cressie, N. (1993). Statistics for Spatial Data. Wiley, New York.