Índice de Moran (BD Modelado)

Se van a comprarar dos softwares para la autocorrelación espacial al calcular Indice de Moran

library(readxl)
df <- read_excel("./BD_MODELADO.xlsx")
library(DT)

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datatable(df, class='cell-border stripe', filter='top', options = list(pageLength=10,autoWidth=T))

library(ggplot2)

ggplot(df, aes(x = Avg_X_MCB, y=Avg_Y_MCE, colour=Avg_CEa_07))+
   geom_point(size = 4)+
   scale_color_continuous(type = 'viridis')

ggplot(df, aes(x = Avg_X_MCB, y=Avg_Y_MCE, colour=Avg_CEa_15))+
   geom_point(size = 4)+
   scale_color_continuous(type = 'viridis')

ggplot(df, aes(x = Avg_X_MCB, y=Avg_Y_MCE, colour=NDVI))+
   geom_point(size = 4, shape = 15)+
   scale_color_continuous(type = 'viridis')

Indice de Moran

library(ape)
t.dists<-as.matrix(dist(cbind(df$Avg_X_MCB, df$Avg_Y_MCE)))
dim(t.dists)

[1] 313 313

t.dists.inv<-1/t.dists
t.dists.inv[is.infinite(t.dists.inv)] <- 0
diag(t.dists.inv)<-0
t.dists.inv[1:5, 1:5]

           1          2          3          4          5
1 0.00000000 0.19320482 0.02207833 0.05403989 0.04558763
2 0.19320482 0.00000000 0.02476496 0.04650837 0.05738726
3 0.02207833 0.02476496 0.00000000 0.01665161 0.03039597
4 0.05403989 0.04650837 0.01665161 0.00000000 0.03392139
5 0.04558763 0.05738726 0.03039597 0.03392139 0.00000000

I. MORAN de conductividad electrica a 70 cm de profundidad

I.Moran_70<-Moran.I(df$Avg_CEa_07, t.dists.inv); I.Moran_70

$observed
[1] 0.2687468

$expected
[1] -0.003205128

$sd
[1] 0.004665906

$p.value
[1] 0

I. MORAN de conductividad electrica a 150 cm de profundidad

I.Moran_150<-Moran.I(df$Avg_CEa_15, t.dists.inv); I.Moran_150

$observed
[1] 0.160951

$expected
[1] -0.003205128

$sd
[1] 0.00465455

$p.value
[1] 0

I. MORAN NVDI

I.Moran_NDVI<-Moran.I(df$NDVI, t.dists.inv); I.Moran_NDVI

$observed
[1] 0.09750403

$expected
[1] -0.003205128

$sd
[1] 0.004644979

$p.value
[1] 0

GeoDa

I. MORAN de conductividad electrica a 70 cm de profundidad

I. MORAN de conductividad electrica a 150 cm de profundidad

I. MORAN NVDI