Local indicators of spatial association

Share:

Description

Calculate different statistics of local indicator of spatial association (LISA) for each cell in a raster data.

Usage

1
lisa(x, y, d1=0, d2, cell, statistic="I") 

Arguments

x

a raster object (RasterLayer or RasterStack or RasterBrick)

y

a SpatialPoints object (optional)

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"

Details

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.

Value

RasterLayer

if x is a RasterLayer and both y and cell are missed

RasterBrick

if x is a RasterStack or a RasterBrick and both y and cell are missed

numeric vector

if y or cell is specified

Author(s)

Babak Naimi naimi.b@gmail.com

http://r-gis.net

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.

Examples

 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
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
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