Local statistics

Introduction

This handout accompanies Chapter 8 in O’Sullivan and Unwin (2010).

LISA

Get the data

Read the Auckland data.

if (!require("rspatial")) devtools::install_github('rspatial/rspatial')
library(rspatial)
auck <- sp_data('auctb.rds')

Local Getis Gi

library(spdep)
wr <- poly2nb(auck, row.names=auck$Id, queen=FALSE)
lstw <- nb2listw(wr, style='B')
Gi <- localG(auck$TB, lstw)
head(Gi)
## [1]  0.4241759  0.5623307  0.4047413  0.6819762 -1.3278352 -1.4086435
par(mai=c(0,0,0,0))
Gcuts <- cut(Gi, 5)
Gcutsi <- as.integer(Gcuts)
cols <- rev(gray(seq(0,1,.2)))
plot(auck, col=cols[Gcutsi])
legend('bottomleft', levels(Gcuts), fill=cols)

image0

To get the Gi * we need to include each polygon as its own ‘neighbor’

ws <- include.self(wr)
lstws <- nb2listw(ws, style='B')
Gis <- localG(auck$TB, lstws)
Gscuts <- cut(Gis, 5)
Gscutsi <- as.integer(Gscuts)
cols <- rev(gray(seq(0,1,.2)))
plot(auck, col=cols[Gscutsi])
legend('bottomleft', levels(Gscuts), fill=cols)

image1

This looks very similar to the local average.

m <- sapply(ws, function(i) mean(auck$TB[i]))
cts <- cut(m, 5)
mcts <- as.integer(cts)
plot(auck, col=cols[mcts])
legend('bottomleft', levels(cts), fill=cols)

image2

The local Moran Ii shows where there are locally high levels of autocorrelation.

Ii <- localmoran(auck$TB, lstw)
Icuts <- cut(Ii, 5)
Icutsi <- as.integer(Icuts)
plot(auck, col=cols[Icutsi])
legend('bottomleft', levels(Icuts), fill=cols)

image3

Geographically weighted regression

Here is an example of GWR with California precipitation data (you can download the data with the scripts or links on the top of this page).

cts <- sp_data('counties.rds')
p <- sp_data('precipitation.csv')
head(p)
##      ID                 NAME   LAT    LONG ALT  JAN FEB MAR APR MAY JUN
## 1 ID741         DEATH VALLEY 36.47 -116.87 -59  7.4 9.5 7.5 3.4 1.7 1.0
## 2 ID743  THERMAL/FAA AIRPORT 33.63 -116.17 -34  9.2 6.9 7.9 1.8 1.6 0.4
## 3 ID744          BRAWLEY 2SW 32.96 -115.55 -31 11.3 8.3 7.6 2.0 0.8 0.1
## 4 ID753 IMPERIAL/FAA AIRPORT 32.83 -115.57 -18 10.6 7.0 6.1 2.5 0.2 0.0
## 5 ID754               NILAND 33.28 -115.51 -18  9.0 8.0 9.0 3.0 0.0 1.0
## 6 ID758        EL CENTRO/NAF 32.82 -115.67 -13  9.8 1.6 3.7 3.0 0.4 0.0
##   JUL  AUG SEP OCT NOV DEC
## 1 3.7  2.8 4.3 2.2 4.7 3.9
## 2 1.9  3.4 5.3 2.0 6.3 5.5
## 3 1.9  9.2 6.5 5.0 4.8 9.7
## 4 2.4  2.6 8.3 5.4 7.7 7.3
## 5 8.0  9.0 7.0 8.0 7.0 9.0
## 6 3.0 10.8 0.2 0.0 3.3 1.4
plot(cts)
points(p[,c('LONG', 'LAT')], col='red', pch=20)

image4

Compute annual average precipitation

p$pan <- rowSums(p[,6:17])

Global regression model

m <- lm(pan ~ ALT, data=p)
m
##
## Call:
## lm(formula = pan ~ ALT, data = p)
##
## Coefficients:
## (Intercept)          ALT
##      523.60         0.17

Create Spatial* objects with a planar crs.

alb <- CRS("+proj=aea +lat_1=34 +lat_2=40.5 +lat_0=0 +lon_0=-120 +x_0=0 +y_0=-4000000 +ellps=GRS80 +datum=NAD83 +units=m +no_defs")
sp <- p
coordinates(sp) = ~ LONG + LAT
crs(sp) <- "+proj=longlat +datum=NAD83"
spt <- spTransform(sp, alb)
ctst <- spTransform(cts, alb)

Get the optimal bandwidth

library( spgwr )
## NOTE: This package does not constitute approval of GWR
## as a method of spatial analysis; see example(gwr)
bw <- gwr.sel(pan ~ ALT, data=spt)
## Bandwidth: 526221.1 CV score: 64886883
## Bandwidth: 850593.6 CV score: 74209073
## Bandwidth: 325747.9 CV score: 54001118
## Bandwidth: 201848.6 CV score: 44611213
## Bandwidth: 125274.7 CV score: 35746320
## Bandwidth: 77949.39 CV score: 29181737
## Bandwidth: 48700.74 CV score: 22737197
## Bandwidth: 30624.09 CV score: 17457161
## Bandwidth: 19452.1 CV score: 15163436
## Bandwidth: 12547.43 CV score: 19452191
## Bandwidth: 22792.75 CV score: 15512988
## Bandwidth: 17052.67 CV score: 15709960
## Bandwidth: 20218.99 CV score: 15167438
## Bandwidth: 19767.99 CV score: 15156913
## Bandwidth: 19790.05 CV score: 15156906
## Bandwidth: 19781.39 CV score: 15156902
## Bandwidth: 19781.48 CV score: 15156902
## Bandwidth: 19781.47 CV score: 15156902
## Bandwidth: 19781.47 CV score: 15156902
## Bandwidth: 19781.47 CV score: 15156902
## Bandwidth: 19781.47 CV score: 15156902
bw
## [1] 19781.47

Create a regular set of points to estimate parameters for.

r <- raster(ctst, res=10000)
r <- rasterize(ctst, r)
newpts <- rasterToPoints(r)

Run the gwr function

g <- gwr(pan ~ ALT, data=spt, bandwidth=bw, fit.points=newpts[, 1:2])
g
## Call:
## gwr(formula = pan ~ ALT, data = spt, bandwidth = bw, fit.points = newpts[,
##     1:2])
## Kernel function: gwr.Gauss
## Fixed bandwidth: 19781.47
## Fit points: 4087
## Summary of GWR coefficient estimates at fit points:
##                    Min.    1st Qu.     Median    3rd Qu.      Max.
## X.Intercept. -702.40121   79.54254  330.48807  735.42718 3468.8702
## ALT            -3.91270    0.03058    0.20461    0.41542    4.6133

Link the results back to the raster

slope <- r
intercept <- r
slope[!is.na(slope)] <- g$SDF$ALT
intercept[!is.na(intercept)] <- g$SDF$'(Intercept)'
s <- stack(intercept, slope)
names(s) <- c('intercept', 'slope')
plot(s)

image5

See this page for a more detailed example of Geographically weighted regression.