Indice de Moran datos de Materia Organica

set.seed(12345)
MO=rnorm(n = 150,mean = 3,sd = 0.5)
xy=expand.grid(x=seq(1,10),y=seq(1,15))
plot(xy,col=MO,pch=19)

dfMO<-data.frame(MO,xy)
#Para entregar :Calcular el indice de moran con los anteriores datos
library(ape)
MO.dists <- as.matrix(dist(cbind(xy$x, xy$y)))
MO.dists.inv <- 1/MO.dists
diag(MO.dists.inv) <- 0 
 
MO.dists.inv[1:5, 1:5]
##           1         2   3         4         5
## 1 0.0000000 1.0000000 0.5 0.3333333 0.2500000
## 2 1.0000000 0.0000000 1.0 0.5000000 0.3333333
## 3 0.5000000 1.0000000 0.0 1.0000000 0.5000000
## 4 0.3333333 0.5000000 1.0 0.0000000 1.0000000
## 5 0.2500000 0.3333333 0.5 1.0000000 0.0000000
Moran.I(MO, MO.dists.inv) #indice de moran
## $observed
## [1] -0.009650003
## 
## $expected
## [1] -0.006711409
## 
## $sd
## [1] 0.007694112
## 
## $p.value
## [1] 0.7025151

Como el p valor fue mayor a 0.05 nos indica que no hay una correlacion espacial entre los datos, es decir, los datos no se encuentran agrupados en bloques segun sus caracteristicas, podemos encontrar valores de MO diferentes en cualquier coordenada.

Indice de Moran para datos de Conductividad Electrica del Suelo

library(readxl)
library(ape)
DatosCE<- read_excel("d:/Users/CRISTIAN GARCIA/Downloads/BD_MORAN (1).xlsx", 
    sheet = "Hoja1")

Datos<-data.frame('X'=DatosCE$X_WGS84,'Y'=DatosCE$Y_WGS84,'P75'=DatosCE$CEa_075,
                  'P150'=DatosCE$CEa_150)

plot(Datos$X,Datos$Y,col=Datos$P75, main = 'Conductividad Electrica del suelo a 75 cm de profundidad', ylab = 'Latitud', xlab = 'Longitud')

plot(Datos$X,Datos$Y,col=Datos$P150, main = 'Conductividad Electrica del suelo a 150 cm de profundidad',ylab = 'Latitud', xlab = 'Longitud')

CE_dist1<-as.matrix(dist(cbind(Datos$X[1:6500], Datos$Y[1:6500])))
CE_dist_inv1<-1/CE_dist1
diag(CE_dist_inv1) <- 0 
CE_dist_inv1[is.infinite(CE_dist_inv1)] <- 0
CE_dist_inv1[1:10, 1:10]
##            1         2         3         4         5         6         7
## 1       0.00 406473.84 183582.68 136552.66  99512.00  80767.51  66276.45
## 2  406473.84      0.00 334028.09 205286.88 131603.44 100690.59  79119.77
## 3  183582.68 334028.09      0.00 532631.20 217163.07 144140.71 103677.22
## 4  136552.66 205286.88 532631.20      0.00 366652.29 197620.35 128735.53
## 5   99512.00 131603.44 217163.07 366652.29      0.00 428663.73 198393.56
## 6   80767.51 100690.59 144140.71 197620.35 428663.73      0.00 369323.16
## 7   66276.45  79119.77 103677.22 128735.53 198393.56 369323.16      0.00
## 8   57404.68  66798.00  83495.12  99016.99 135648.88 198443.56 428837.13
## 9   49363.96  56156.93  67505.83  77303.00  97952.59 126960.00 193437.71
## 10  44785.68  50307.49  59227.30  66636.81  81434.82 100529.27 138109.95
##            8         9        10
## 1   57404.68  49363.96  44785.68
## 2   66798.00  56156.93  50307.49
## 3   83495.12  67505.83  59227.30
## 4   99016.99  77303.00  66636.81
## 5  135648.88  97952.59  81434.82
## 6  198443.56 126960.00 100529.27
## 7  428837.13 193437.71 138109.95
## 8       0.00 352381.47 203715.00
## 9  352381.47      0.00 482860.83
## 10 203715.00 482860.83      0.00
CE_dist2<-as.matrix(dist(cbind(Datos$X[6501:13000], Datos$Y[6501:13000])))
CE_dist_inv2<-1/CE_dist2
diag(CE_dist_inv2) <- 0 
CE_dist_inv2[is.infinite(CE_dist_inv2)] <- 0
CE_dist_inv2[1:10, 1:10]
##            1         2         3         4         5         6         7
## 1       0.00 186622.71  88742.74  59592.84  43892.46  35369.17  29862.41
## 2  186622.71      0.00 169197.23  87548.02  57389.65  43639.55  35550.87
## 3   88742.74 169197.23      0.00 181420.99  86847.15  58807.15  45007.65
## 4   59592.84  87548.02 181420.99      0.00 166598.88  87011.81  59857.28
## 5   43892.46  57389.65  86847.15 166598.88      0.00 182140.90  93423.31
## 6   35369.17  43639.55  58807.15  87011.81 182140.90      0.00 191801.90
## 7   29862.41  35550.87  45007.65  59857.28  93423.31 191801.90      0.00
## 8   25078.62  28971.80  34957.60  43301.19  58508.10  86196.33 156548.02
## 9   22222.28  25226.03  29646.00  35436.68  45010.66  59784.49  86857.28
## 10  19807.38  22159.23  25498.68  29668.56  36096.72  45018.38  58825.03
##            8         9        10
## 1   25078.62  22222.28  19807.38
## 2   28971.80  25226.03  22159.23
## 3   34957.60  29646.00  25498.68
## 4   43301.19  35436.68  29668.56
## 5   58508.10  45010.66  36096.72
## 6   86196.33  59784.49  45018.38
## 7  156548.02  86857.28  58825.03
## 8       0.00 195109.42  94235.14
## 9  195109.42      0.00 182267.93
## 10  94235.14 182267.93      0.00
CE_dist3<-as.matrix(dist(cbind(Datos$X[13001:18526], Datos$Y[13001:18526])))
CE_dist_inv3<-1/CE_dist3
diag(CE_dist_inv3) <- 0 
CE_dist_inv3[is.infinite(CE_dist_inv3)] <- 0
CE_dist_inv3[1:10, 1:10]
##            1         2         3         4         5         6         7
## 1       0.00 198632.95  92659.27  56698.73  46252.81  36997.79  31005.16
## 2  198632.95      0.00 173676.82  79348.26  60292.18  45466.45  36739.98
## 3   92659.27 173676.82      0.00 146095.19  92352.48  61589.78  46597.08
## 4   56698.73  79348.26 146095.19      0.00 251052.71 106477.38  68418.90
## 5   46252.81  60292.18  92352.48 251052.71      0.00 184892.16  94048.79
## 6   36997.79  45466.45  61589.78 106477.38 184892.16      0.00 191416.05
## 7   31005.16  36739.98  46597.08  68418.90  94048.79 191416.05      0.00
## 8   26172.63  30144.56  36475.34  48611.91  60284.45  89449.70 167919.07
## 9   22672.27  25593.52  30016.77  37778.55  44470.15  58553.38  84358.18
## 10  19926.81  22148.74  25386.09  30724.78  35009.14  43186.44  55768.74
##            8         9        10
## 1   26172.63  22672.27  19926.81
## 2   30144.56  25593.52  22148.74
## 3   36475.34  30016.77  25386.09
## 4   48611.91  37778.55  30724.78
## 5   60284.45  44470.15  35009.14
## 6   89449.70  58553.38  43186.44
## 7  167919.07  84358.18  55768.74
## 8       0.00 169521.23  83500.71
## 9  169521.23      0.00 164555.39
## 10  83500.71 164555.39      0.00

Indice de Moran para CE a 75 cm de profundidad

Moran.I(Datos$P75[1:6500], CE_dist_inv1)
## $observed
## [1] 0.4678792
## 
## $expected
## [1] -0.0001538698
## 
## $sd
## [1] 0.001434575
## 
## $p.value
## [1] 0
Moran.I(Datos$P75[6501:13000], CE_dist_inv2)
## $observed
## [1] 0.6212211
## 
## $expected
## [1] -0.0001538698
## 
## $sd
## [1] 0.001281639
## 
## $p.value
## [1] 0
Moran.I(Datos$P75[13001:18526], CE_dist_inv3)
## $observed
## [1] 0.371161
## 
## $expected
## [1] -0.0001809955
## 
## $sd
## [1] 0.001493688
## 
## $p.value
## [1] 0

Indice de Moran para CE a 150 cm de profundidad

Moran.I(Datos$P150[1:6500], CE_dist_inv1)
## $observed
## [1] 0.3130099
## 
## $expected
## [1] -0.0001538698
## 
## $sd
## [1] 0.001434396
## 
## $p.value
## [1] 0
Moran.I(Datos$P150[6501:13000], CE_dist_inv2)
## $observed
## [1] 0.2968086
## 
## $expected
## [1] -0.0001538698
## 
## $sd
## [1] 0.001281582
## 
## $p.value
## [1] 0
Moran.I(Datos$P150[13001:18526], CE_dist_inv3)
## $observed
## [1] 0.5218761
## 
## $expected
## [1] -0.0001809955
## 
## $sd
## [1] 0.001493801
## 
## $p.value
## [1] 0

Los p valores obtenidos en cada uno de los 3 grupos de cada profundidad para la correlacion espacial de la CE nos indican que los datos de CE si estan relacionados entre si y que valores similares de CE se pueden encontrar formando bloques o conjuntos en las coordenadas.