Local indicators of spatial association
Calculate different statistics of local indicator of spatial association (LISA) for each cell in a raster data.
lisa(x, y, d1=0, d2, cell, statistic="I")
x |
a raster object ( |
y |
a |
d1 |
numeric. A number (distance), specifies local neighborhood size. Default is 0, means that the local neighborhood starts from the cell (distance = 0) and ends to a distance = d2 |
d2 |
numeric. A number (distance), specifies local neighborhood size. It specifies the distance to which should be considered as a local neighborhood around a cell |
cell |
numeric (optional). A cell number or a vector of cell numbers in the Raster object, at which LISA should be calculated |
statistic |
a character string specifying the LISA statistic that should be calculated. This can be one of "I", "c", "G", "G*", and "K1" |
This function can calculate different LISA statistics at each grid cell in Raster object. The statistics, implemented in this function, include local Moran's I ("I"), local Geary's c ("c"), local G and G* ("G" and "G*"), and local K1 statistics. This function returns standardized value (Z) for Moran, G and G*, and K1 statistics. If a SpatialPoints or a vector of numbers is defined for y or cell, the LISA is calculated only for the specified locations by points or cells.
RasterLayer |
if |
RasterBrick |
if |
numeric vector |
if |
Babak Naimi naimi.b@gmail.com
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.
Naimi, B., Hamm, N.A.S., Groen, T.A., Skidmore, A.K., and Toxopeus, A.G. 2014. Where is positional uncertainty a problem for species distribution modelling?, Ecography 37 (2): 191-203.
## Not run:
file <- system.file("external/spain.grd", package="usdm")
r <- brick(file) # reading a RasterBrick object including 10 rasters in Spain
r
plot(r) # visualize the raster layers
plot(r[[1]]) # visualize the first raster layer
r.I <- lisa(x=r[[1]],d1=0,d2=25000,statistic="I") # local Moran's I
plot(r.I)
# entering r instead of r[[1]], givees the indicator for each layer:
r.I <- lisa(x=r,d1=0,d2=25000,statistic="I")
plot(r.I)
r.c <- lisa(x=r[[1]],d1=0,d2=25000,statistic="c") # local Geary's c
plot(r.c)
r.g <- lisa(x=r[[1]],d1=0,d2=25000,statistic="G") # G statistic
plot(r.g)
r.g2 <- lisa(x=r[[1]],d1=0,d2=25000,statistic="G*") # G* statistic
plot(r.g2)
r.K1 <- lisa(x=r[[1]],d1=0,d2=30000,statistic="K1") # gives K1 statistic for each layer
plot(r.K1)
lisa(x=r,d1=0,d2=30000,cell=2000,statistic="I") # gives local Moran's I at cell number 2000
#for each raster layer in r
lisa(x=r,d1=0,d2=30000,cell=c(2000,2002,2003),statistic="c") # calculates local Moran's I
# at cell numbers of 2000,2002, and 2003 for each raster layer in r
sp <- sampleRandom(r[[1]],20,sp=TRUE) # draw 20 random points from r,
# and returns a SpatialPointsDataFrame
plot(r[[1]])
points(sp)
lisa(x=r,y=sp,d1=0,d2=30000,statistic="I") # calculates the local Moran's I at
# point locations in sp for each raster layer in r
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