Ujian Tengah Semester Spasial
nadine
2024-10-16
Mata Kuliah Spasial
Dosen Pengampu : Dr. I Gede Nyoman Mindra Jaya, M.Si
Mempersiapkan file
setwd("C:/Users/nadin/OneDrive/Documents/Semester 5/Spasial/UTS")
Indonesia<-readRDS("C:/Users/nadin/OneDrive/Documents/Semester 5/Spasial/UTS/gadm36_IDN_2_sp.rds")
data <- read_excel("C:/Users/nadin/OneDrive/Documents/Semester 5/Spasial/UTS/Data Ujian Tengah Semester.xlsx")
data## # A tibble: 35 × 8
## id Kota BGK PUS AirLayak Posyandu Imunisasi Sanitasi
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 Cilacap 5176 348057 88.8 2205 24770 80.4
## 2 2 Banyumas 4127 303267 93.6 2548 24953 83
## 3 3 Purbalingga 3267 184376 82.8 1238 14164 77.2
## 4 4 Banjarnegara 2120 190610 87.6 1578 13256 46.1
## 5 5 Kebumen 3227 193660 87.3 2123 16267 93.7
## 6 6 Purworejo 760 108734 91.3 1646 8716 81.4
## 7 7 Wonosobo 1825 148878 94.6 1291 11199 58.1
## 8 8 Magelang 3425 204578 97.5 2479 17862 85.1
## 9 9 Boyolali 2192 172389 94.4 1860 13461 89.3
## 10 10 Klaten 3699 197321 99.5 2297 15343 97.2
## # ℹ 25 more rowsMembuat plot sebagai eksplorasi data
Jawa_Tengah<-Indonesia[Indonesia$NAME_1 == "Jawa Tengah",]
Jawa_Tengah <- Jawa_Tengah[Jawa_Tengah$NAME_2 != 'Waduk Kedungombo',]
plot(Jawa_Tengah)
Jawa_Tengah$id<-c(1:35)
Jawa_Tengah_sf<-st_as_sf(Jawa_Tengah)
Jawa_Tengah_merged <- Jawa_Tengah_sf %>%left_join(data, by = "id")
Jawa_Tengah_merged <- st_as_sf(Jawa_Tengah_merged)
ggplot() +
geom_sf(data=Jawa_Tengah_merged, aes(fill =
BGK),color=NA) +
theme_bw() +
scale_fill_gradient(low = "#ffcf01", high =
"blue") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())+
theme(legend.position = "right",
axis.text.x = element_blank(),
axis.text.y = element_blank())+
labs(title = "",
fill = "Bayi Gizi Kurang")
summary(data)## id Kota BGK PUS
## Min. : 1.0 Length:35 Min. : 225 Min. : 13937
## 1st Qu.: 9.5 Class :character 1st Qu.:1411 1st Qu.:143723
## Median :18.0 Mode :character Median :2163 Median :179799
## Mean :18.0 Mean :2316 Mean :183086
## 3rd Qu.:26.5 3rd Qu.:3208 3rd Qu.:213785
## Max. :35.0 Max. :5330 Max. :382016
## AirLayak Posyandu Imunisasi Sanitasi
## Min. : 82.18 Min. : 198 Min. : 1536 Min. :46.09
## 1st Qu.: 92.90 1st Qu.:1232 1st Qu.:10548 1st Qu.:81.08
## Median : 95.92 Median :1442 Median :13461 Median :89.31
## Mean : 94.63 Mean :1428 Mean :13931 Mean :85.31
## 3rd Qu.: 98.33 3rd Qu.:1672 3rd Qu.:17779 3rd Qu.:94.71
## Max. :100.00 Max. :2548 Max. :27302 Max. :98.15hist(data$BGK)
breaks <- c(-Inf, 1411, 2163, 3208,Inf)
labels <- c("Very Low", "Low", "High",
"Very High")
Jawa_Tengah_merged$BGK_Discrete <-
cut(Jawa_Tengah_merged$BGK, breaks =
breaks, labels = labels, right = TRUE)
ggplot() +
geom_sf(data=Jawa_Tengah_merged, aes(fill =
BGK_Discrete),color=NA) +
theme_bw() +
scale_fill_manual(values = c("Very Low" = "#ffcf01",
"Low" = "#ff8a01",
"High" = "#003780",
"Very High" = "#011f47"))+
labs(fill = "Bayi Kurang Gizi")+theme(legend.position = "right",
axis.text.x = element_blank(),
axis.text.y = element_blank())+
labs(title = "",
fill = "Bayi Kurang Gizi")
Membuat Koordinat
ID <- c(1:35)
Jawa_Tengah$ID <- c(1:35)
plot(Jawa_Tengah, axes = T, col ="#ffcf01")
Membuat koordinat centroid dari peta
Coordk <- coordinates(Jawa_Tengah)Membuat Matriks Bobot
##Berdasarkan Contiguity (Persinggungan) #ROOK
WRook <- poly2nb(Jawa_Tengah, row.names=ID,queen=FALSE);WRook## Neighbour list object:
## Number of regions: 35
## Number of nonzero links: 146
## Percentage nonzero weights: 11.91837
## Average number of links: 4.171429WBRook <- nb2mat(WRook, style='B', zero.policy=TRUE);WBRook## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
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## [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35]
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## 34 0 0 0 1 0 0 0 0 0
## 35 0 0 0 0 0 0 1 0 0
## attr(,"call")
## nb2mat(neighbours = WRook, style = "B", zero.policy = TRUE)WBRook[c(1:35),c(1:35)]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0
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## 35 1 0 1 0 0 0 0 0 0 0 0 1 1 0
## [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
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## 34 0 0 0 1 0 0 0 0 0
## 35 0 0 0 0 0 0 1 0 0WLR<-nb2listw(WRook);WLR #List neighbours / Moran's I## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 35
## Number of nonzero links: 146
## Percentage nonzero weights: 11.91837
## Average number of links: 4.171429
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 35 1225 35 18.9401 150.9739plot(WLR, coordinates(Jawa_Tengah), col="#ffffff")
plot(Jawa_Tengah, axes=T, col="#ffcf01")
text(Coordk[,1], Coordk[,2], row.names(Jawa_Tengah), col="black", cex=0.8, pos=1.5)
points(Coordk[,1], Coordk[,2], pch=19, cex=0.7,col="#011f47")
plot(WLR, coordinates(Jawa_Tengah), col="#003780", add=T)
#QUEEN
WQueen <- poly2nb(Jawa_Tengah, row.names=ID, queen=TRUE) #Mendapatkan W
WBQueen <- nb2mat(WQueen, style='B', zero.policy = TRUE) #menyajikan dalam bentuk matrix biner "B"
WBQueen[c(1:35),c(1:35)]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0
## 2 1 0 0 0 0 1 1 0 0 0 0 1 0 0
## 3 1 0 0 0 0 0 0 0 0 0 0 0 1 0
## 4 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 1 0 1 0 0 1
## 6 0 1 0 0 0 0 1 0 0 0 0 0 0 0
## 7 0 1 0 0 0 1 0 0 0 0 0 1 0 0
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## 35 1 0 1 0 0 0 0 0 0 0 0 1 1 0
## [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
## 1 0 0 0 0 0 0 0 1 0 1 0 0
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## 35 0 0 0 0 0 1 0 0 0 0 1 0
## [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35]
## 1 0 0 0 0 0 0 0 0 1
## 2 0 0 0 0 0 1 0 0 0
## 3 0 0 0 0 0 0 0 0 1
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## 6 0 0 0 0 0 1 0 0 0
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## 20 0 1 0 0 0 0 1 0 1
## 21 0 0 0 0 0 0 0 0 0
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## 32 0 0 0 0 0 0 0 0 0
## 33 0 1 0 0 0 0 0 0 1
## 34 0 0 0 1 0 0 0 0 0
## 35 0 0 0 0 0 0 1 0 0WLQ<-nb2listw(WQueen);WLQ## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 35
## Number of nonzero links: 146
## Percentage nonzero weights: 11.91837
## Average number of links: 4.171429
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 35 1225 35 18.9401 150.9739plot(WLQ, coordinates(Jawa_Tengah), col="#ffffff")
plot(Jawa_Tengah, axes=T, col="#ffcf01")
text(Coordk[,1], Coordk[,2], row.names(Jawa_Tengah), col="black", cex=0.8, pos=1.5)
points(Coordk[,1], Coordk[,2], pch=19, cex=0.7,col="#011f47")
plot(WLQ, coordinates(Jawa_Tengah), col="#003780", add=T)
##PENDEKATAN JARAK
#k=2
WJ2 <- knn2nb(knearneigh(Coordk, k =
2), row.names = ID);WJ2## Neighbour list object:
## Number of regions: 35
## Number of nonzero links: 70
## Percentage nonzero weights: 5.714286
## Average number of links: 2
## Non-symmetric neighbours listWBJ2 <- nb2mat(WJ2, style='B', zero.policy
= TRUE);WJ2 #menyajikan dalam bentuk matrix biner "B## Neighbour list object:
## Number of regions: 35
## Number of nonzero links: 70
## Percentage nonzero weights: 5.714286
## Average number of links: 2
## Non-symmetric neighbours listWLJ2<-nb2listw(WJ2);WLJ2## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 35
## Number of nonzero links: 70
## Percentage nonzero weights: 5.714286
## Average number of links: 2
## Non-symmetric neighbours list
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 35 1225 35 29.5 153.5plot(Jawa_Tengah, axes=T, col="#ffcf01")
text(Coordk[,1], Coordk[,2], row.names(Jawa_Tengah), col="black", cex=0.8, pos=1.5)
points(Coordk[,1], Coordk[,2], pch=19, cex=0.7,col="#011f47")
plot(WJ2, coordinates(Jawa_Tengah), col="#003780", add=T)
#K=3
WJ3 <- knn2nb(knearneigh(Coordk, k =
3), row.names = ID);WJ3## Neighbour list object:
## Number of regions: 35
## Number of nonzero links: 105
## Percentage nonzero weights: 8.571429
## Average number of links: 3
## Non-symmetric neighbours listWBJ3 <- nb2mat(WJ3, style='B', zero.policy
= TRUE);WBJ3 #menyajikan dalam bentuk matrix biner "B## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 4 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 7 0 1 0 0 0 1 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 9 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 12 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 13 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 16 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0 1 0 0 0 0 1 0
## 18 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 19 0 0 0 0 0 0 0 1 0 1 0 0 0 0
## 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 1 1 0 0 0 0
## 22 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 24 1 1 0 0 0 0 0 0 0 0 0 0 0 0
## 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 26 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 27 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 29 0 0 0 0 0 0 0 0 1 0 1 0 0 0
## 30 0 0 0 0 0 0 0 0 0 0 1 0 0 1
## 31 0 0 0 0 1 0 0 0 0 0 1 0 0 0
## 32 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 33 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 34 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 35 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
## 1 0 0 0 0 0 0 0 1 0 1 0 0
## 2 0 0 0 0 0 0 0 0 0 1 0 0
## 3 0 1 0 0 0 0 0 1 0 0 0 0
## 4 0 0 0 0 0 0 1 0 0 0 0 1
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 1 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 1 0 1 0 0 0 0 0 0 0
## 9 0 0 0 0 1 0 0 0 0 0 0 0
## 10 0 0 0 0 1 0 1 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0 1 0
## 13 0 0 1 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0 0 0
## 15 0 0 0 0 0 1 0 0 0 0 0 0
## 16 0 0 0 0 0 0 0 1 1 0 0 0
## 17 0 0 0 0 0 0 0 0 0 0 0 0
## 18 0 0 0 0 0 0 0 0 1 0 0 0
## 19 0 0 0 0 0 0 1 0 0 0 0 0
## 20 1 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 1 0 0 0 0 0 0 0
## 22 0 1 0 0 0 0 0 0 1 0 0 0
## 23 0 0 0 0 0 0 0 1 0 1 0 0
## 24 0 0 0 0 0 0 0 0 1 0 0 0
## 25 1 0 0 0 0 1 0 0 0 0 0 0
## 26 0 0 0 0 1 0 1 0 0 0 0 0
## 27 0 0 0 0 0 1 0 0 0 0 0 0
## 28 0 0 1 0 0 0 0 0 0 0 0 0
## 29 0 0 0 0 0 0 0 0 0 0 0 0
## 30 0 0 0 0 0 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0 0 0 0 0 0
## 32 0 0 0 1 0 0 0 0 1 0 0 0
## 33 1 0 0 0 0 1 0 0 0 0 0 0
## 34 0 0 0 0 0 0 0 0 0 0 0 0
## 35 0 0 0 0 0 0 0 0 0 0 1 0
## [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35]
## 1 0 0 0 0 0 0 0 0 1
## 2 0 0 0 0 0 1 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 1 1 0 0 1 0 0 0 0
## 6 0 0 0 0 0 1 0 0 0
## 7 0 0 0 0 0 1 0 0 0
## 8 0 0 0 0 0 0 0 0 0
## 9 0 0 1 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0
## 11 0 0 1 1 1 0 0 0 0
## 12 0 0 0 0 0 0 0 0 1
## 13 0 0 0 0 0 0 1 0 0
## 14 0 0 0 1 1 0 0 0 0
## 15 1 0 0 0 0 0 1 0 0
## 16 0 0 0 0 0 0 0 0 0
## 17 0 1 0 0 0 0 0 0 0
## 18 0 0 0 0 0 1 0 0 0
## 19 0 0 0 0 0 0 0 0 0
## 20 1 0 0 0 0 0 1 0 0
## 21 0 0 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 1 0 0 0
## 24 0 0 0 0 0 0 0 0 0
## 25 0 0 0 0 0 0 0 0 1
## 26 0 0 0 0 0 0 0 0 0
## 27 0 1 0 0 0 0 0 0 0
## 28 1 0 0 0 0 0 0 0 0
## 29 0 0 0 0 1 0 0 0 0
## 30 0 0 0 0 1 0 0 0 0
## 31 0 0 0 1 0 0 0 0 0
## 32 0 0 0 0 0 0 0 0 0
## 33 0 0 0 0 0 0 0 0 0
## 34 0 0 0 1 1 0 0 0 0
## 35 0 0 0 0 0 0 1 0 0
## attr(,"call")
## nb2mat(neighbours = WJ3, style = "B", zero.policy = TRUE)WLJ3<-nb2listw(WJ3);WLJ3## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 35
## Number of nonzero links: 105
## Percentage nonzero weights: 8.571429
## Average number of links: 3
## Non-symmetric neighbours list
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 35 1225 35 20.55556 147.5556plot(Jawa_Tengah, axes=T, col="#ffcf01")
text(Coordk[,1], Coordk[,2], row.names(Jawa_Tengah), col="black", cex=0.8, pos=1.5)
points(Coordk[,1], Coordk[,2], pch=19, cex=0.7,col="#011f47")
plot(WJ3, coordinates(Jawa_Tengah), col="#003780", add=T)
#K=4
WJ4 <- knn2nb(knearneigh(Coordk, k =
4), row.names = ID);WJ4## Neighbour list object:
## Number of regions: 35
## Number of nonzero links: 140
## Percentage nonzero weights: 11.42857
## Average number of links: 4
## Non-symmetric neighbours listWBJ4 <- nb2mat(WJ4, style='B', zero.policy
= TRUE);WBJ4 #menyajikan dalam bentuk matrix biner "B## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 2 0 0 0 0 0 1 1 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 4 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 6 0 1 0 0 0 0 1 0 0 0 0 0 0 0
## 7 0 1 0 0 0 1 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 1 1 0 0 0 0
## 9 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 1 1 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 12 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 13 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 16 1 0 1 0 0 0 0 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0 1 0 0 0 0 1 0
## 18 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 19 0 0 0 0 0 0 0 1 1 1 0 0 0 0
## 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 1 1 0 0 0 0
## 22 1 0 1 0 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 24 1 1 0 0 0 0 0 0 0 0 0 0 0 0
## 25 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 26 0 0 0 1 0 0 0 0 1 0 0 0 0 0
## 27 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 29 0 0 0 0 0 0 0 0 1 0 1 0 0 0
## 30 0 0 0 0 0 0 0 0 0 0 1 0 0 1
## 31 0 0 0 0 1 0 0 0 0 0 1 0 0 0
## 32 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 33 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 34 0 0 0 0 0 0 0 0 0 0 1 0 0 1
## 35 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
## 1 0 0 0 0 0 0 0 1 0 1 0 0
## 2 0 0 0 0 0 0 0 0 0 1 0 0
## 3 0 1 0 0 0 0 0 1 0 0 0 0
## 4 0 0 0 0 0 0 1 0 0 0 0 1
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 1 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 1 0 0
## 8 0 0 1 0 1 0 0 0 0 0 0 0
## 9 0 0 0 0 1 0 1 0 0 0 0 0
## 10 0 0 0 0 1 0 1 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 1 1 0
## 13 0 0 1 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0 0 0
## 15 0 0 0 0 0 1 0 0 0 0 0 0
## 16 0 0 0 0 0 0 0 1 1 0 0 0
## 17 0 0 0 0 0 0 0 0 0 0 0 0
## 18 0 0 0 0 0 0 0 1 1 0 0 0
## 19 0 0 0 0 0 0 1 0 0 0 0 0
## 20 1 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 1 0 0 0 0 0 0 1
## 22 0 1 0 0 0 0 0 0 1 0 0 0
## 23 0 1 0 0 0 0 0 1 0 1 0 0
## 24 0 0 0 0 0 0 0 1 1 0 0 0
## 25 1 0 0 0 0 1 0 0 0 0 0 0
## 26 0 0 0 0 1 0 1 0 0 0 0 0
## 27 1 0 0 0 0 1 0 0 0 0 0 0
## 28 0 0 1 0 0 1 0 0 0 0 0 0
## 29 0 0 0 0 0 0 0 0 0 0 0 0
## 30 0 0 0 0 0 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0 0 0 0 0 0
## 32 0 0 0 1 0 0 0 0 1 1 0 0
## 33 1 0 0 0 0 1 0 0 0 0 0 0
## 34 0 0 0 0 0 0 0 0 0 0 0 0
## 35 1 0 0 0 0 0 0 0 0 0 1 0
## [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35]
## 1 0 0 0 0 0 0 0 0 1
## 2 0 0 0 0 0 1 0 0 0
## 3 0 0 0 0 0 0 1 0 0
## 4 0 0 1 0 0 0 0 0 0
## 5 1 1 0 0 1 0 0 0 0
## 6 0 0 0 0 0 1 0 0 0
## 7 0 0 0 0 0 1 0 0 0
## 8 0 0 0 0 0 0 0 0 0
## 9 0 0 1 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0
## 11 0 0 1 1 1 0 0 1 0
## 12 0 0 0 0 0 0 0 0 1
## 13 0 1 0 0 0 0 1 0 0
## 14 1 0 0 1 1 0 0 0 0
## 15 1 0 0 0 0 0 1 0 1
## 16 0 0 0 0 0 0 0 0 0
## 17 1 1 0 0 0 0 0 0 0
## 18 0 0 0 0 0 1 0 0 0
## 19 0 0 0 0 0 0 0 0 0
## 20 1 1 0 0 0 0 1 0 0
## 21 0 0 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 1 0 0 0
## 24 0 0 0 0 0 0 0 0 0
## 25 0 0 0 0 0 0 0 0 1
## 26 0 0 0 0 0 0 0 0 0
## 27 0 1 0 0 0 0 0 0 0
## 28 1 0 0 0 0 0 0 0 0
## 29 0 0 0 1 1 0 0 0 0
## 30 0 0 0 0 1 0 0 1 0
## 31 0 0 1 1 0 0 0 0 0
## 32 0 0 0 0 0 0 0 0 0
## 33 0 0 0 0 0 0 0 0 1
## 34 0 0 0 1 1 0 0 0 0
## 35 0 0 0 0 0 0 1 0 0
## attr(,"call")
## nb2mat(neighbours = WJ4, style = "B", zero.policy = TRUE)WLJ4<-nb2listw(WJ4);WLJ4## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 35
## Number of nonzero links: 140
## Percentage nonzero weights: 11.42857
## Average number of links: 4
## Non-symmetric neighbours list
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 35 1225 35 15.875 144.75plot(Jawa_Tengah, axes=T, col="#ffcf01")
text(Coordk[,1], Coordk[,2], row.names(Jawa_Tengah), col="black", cex=0.8, pos=1.5)
points(Coordk[,1], Coordk[,2], pch=19, cex=0.7,col="#011f47")
plot(WJ4, coordinates(Jawa_Tengah), col="#003780", add=T)
Menghitung Inverse Distance
#Inverse Distance
Dist.mat<-as.matrix(dist(Coordk,
method="euclidean"))
Dist.mat[1:35,1:35]## 24 35 46 54 55 56 57
## 24 0.0000000 0.4927800 0.3884859 1.7520798 0.9954655 0.7863504 0.7795139
## 35 0.4927800 0.0000000 0.8115827 2.2443202 1.4755221 0.4671286 0.2874077
## 46 0.3884859 0.8115827 0.0000000 1.5271455 0.8835287 0.9347265 1.0786723
## 54 1.7520798 2.2443202 1.5271455 0.0000000 0.8127282 2.4601759 2.5314720
## 55 0.9954655 1.4755221 0.8835287 0.8127282 0.0000000 1.7601808 1.7617240
## 56 0.7863504 0.4671286 0.9347265 2.4601759 1.7601808 0.0000000 0.4332291
## 57 0.7795139 0.2874077 1.0786723 2.5314720 1.7617240 0.4332291 0.0000000
## 58 1.0696316 1.5547253 0.7783837 0.7733933 0.5077895 1.7109299 1.8355925
## 59 1.2912497 1.7838889 1.0699120 0.4623467 0.4091963 2.0004151 2.0707603
## 25 1.3635125 1.8292787 1.0210086 0.7789438 0.8454335 1.9163919 2.0996659
## 26 1.3929783 1.8565062 1.3068823 0.6506399 0.4234072 2.1702380 2.1383440
## 27 0.3060114 0.4850381 0.6793227 1.8626047 1.0600051 0.9116461 0.7453323
## 28 0.5892990 1.0655876 0.2950220 1.2321973 0.6242139 1.2287029 1.3443074
## 29 1.0189528 1.4627136 1.0087447 0.9808416 0.2695901 1.8048189 1.7404922
## 30 0.5764861 1.0447836 0.5800277 1.2345090 0.4344837 1.3584646 1.3297910
## 31 0.4593294 0.7538619 0.2239684 1.7195121 1.1056573 0.7685789 0.9887672
## 32 0.8036558 1.2894477 0.5280667 0.9996452 0.4761307 1.4625363 1.5712353
## 33 0.7257612 0.5893651 0.7611282 2.2812894 1.6303579 0.2678255 0.6616974
## 34 1.3365778 1.8203788 1.0346526 0.5920676 0.6665289 1.9609187 2.1000770
## 36 0.6075356 1.0717295 0.6151532 1.2179617 0.4126870 1.3908609 1.3559454
## 37 1.5118399 1.9969675 1.2122215 0.4800549 0.7800714 2.1373698 2.2771943
## 38 0.2974169 0.5971425 0.2436480 1.7673225 1.0919094 0.6929059 0.8493677
## 39 0.4102621 0.4726161 0.4667392 1.9930471 1.3124969 0.4680174 0.6790334
## 40 0.2517724 0.2668092 0.5451843 1.9956567 1.2470039 0.5474395 0.5435354
## 41 0.4649154 0.8273914 0.6863034 1.5523705 0.7400456 1.2200267 1.0956707
## 42 1.8938070 2.3847061 1.6186958 0.3093250 1.0347829 2.5497263 2.6686684
## 43 0.8411182 1.3280000 0.7114679 0.9270919 0.1720996 1.5954846 1.6152475
## 44 0.8206606 1.3113915 0.6626950 0.9344201 0.2283667 1.5615550 1.5987759
## 45 1.3216443 1.8042534 1.1756865 0.5143590 0.3293681 2.0771927 2.0907101
## 47 1.2223123 1.6743322 1.1756085 0.8195827 0.3203846 2.0058361 1.9534675
## 48 1.1840639 1.6510224 1.1015565 0.7421675 0.2220467 1.9603813 1.9341425
## 49 0.5932360 0.4244009 0.7031407 2.2296909 1.5417359 0.2326000 0.5322554
## 50 0.4873686 0.9801133 0.3621124 1.2651486 0.5395074 1.2243196 1.2668498
## 52 1.4582851 1.8831513 1.4509297 0.9288114 0.6115301 2.2445422 2.1532087
## 53 0.2577862 0.7327144 0.3968815 1.5188592 0.7434318 1.0424538 1.0196039
## 58 59 25 26 27 28 29
## 24 1.0696316 1.2912497 1.3635125 1.3929783 0.3060114 0.5892990 1.0189528
## 35 1.5547253 1.7838889 1.8292787 1.8565062 0.4850381 1.0655876 1.4627136
## 46 0.7783837 1.0699120 1.0210086 1.3068823 0.6793227 0.2950220 1.0087447
## 54 0.7733933 0.4623467 0.7789438 0.6506399 1.8626047 1.2321973 0.9808416
## 55 0.5077895 0.4091963 0.8454335 0.4234072 1.0600051 0.6242139 0.2695901
## 56 1.7109299 2.0004151 1.9163919 2.1702380 0.9116461 1.2287029 1.8048189
## 57 1.8355925 2.0707603 2.0996659 2.1383440 0.7453323 1.3443074 1.7404922
## 58 0.0000000 0.3597432 0.3609460 0.8072713 1.2582462 0.4925495 0.7753728
## 59 0.3597432 0.0000000 0.5520564 0.5088724 1.4160367 0.7751193 0.6470888
## 25 0.3609460 0.5520564 0.0000000 1.0603067 1.5840008 0.7748977 1.1148256
## 26 0.8072713 0.5088724 1.0603067 0.0000000 1.4082161 1.0439623 0.4112169
## 27 1.2582462 1.4160367 1.5840008 1.4082161 0.0000000 0.8193910 1.0029716
## 28 0.4925495 0.7751193 0.7748977 1.0439623 0.8193910 0.0000000 0.7974167
## 29 0.7753728 0.6470888 1.1148256 0.4112169 1.0029716 0.7974167 0.0000000
## 30 0.7000240 0.7934730 1.0564661 0.8168804 0.6284920 0.4440139 0.4512197
## 31 0.9543022 1.2691200 1.1484983 1.5290503 0.7646530 0.4995822 1.2316048
## 32 0.2659761 0.5461541 0.5884560 0.8711564 0.9996053 0.2341493 0.7046928
## 33 1.5168346 1.8282396 1.6923696 2.0503894 0.9328106 1.0538920 1.7119650
## 34 0.2672094 0.3327965 0.2225531 0.8416267 1.5226915 0.7557872 0.9314560
## 36 0.7049530 0.7817732 1.0634100 0.7872145 0.6475063 0.4721324 0.4169631
## 37 0.4424106 0.3905576 0.3029798 0.8729583 1.6909293 0.9330311 1.0328937
## 38 1.0220302 1.3080673 1.2560262 1.5119777 0.5984985 0.5359495 1.1812624
## 39 1.2433783 1.5342294 1.4609241 1.7301106 0.6574932 0.7610526 1.3866280
## 40 1.2920764 1.5336881 1.5625450 1.6441411 0.3931857 0.8011696 1.2658855
## 41 1.0320930 1.1239218 1.3835381 1.0623226 0.3514343 0.6885411 0.6539935
## 42 0.8403928 0.6340138 0.7040966 0.9471514 2.0430844 1.3314491 1.2400598
## 43 0.4475569 0.4824712 0.8067794 0.5955068 0.9363106 0.4555549 0.3687071
## 44 0.3951543 0.4790857 0.7560461 0.6487232 0.9381306 0.3960379 0.4378608
## 45 0.5893370 0.2757542 0.8272997 0.2331290 1.3871980 0.8939150 0.4667082
## 47 0.7959603 0.5715099 1.0983213 0.2011225 1.2174880 0.9349026 0.2148020
## 48 0.6746760 0.4532287 0.9751734 0.2099088 1.2098903 0.8462162 0.2405690
## 49 1.4784942 1.7707859 1.6859945 1.9562484 0.7747813 0.9976562 1.6016888
## 50 0.6054966 0.8039295 0.9353064 0.9589343 0.6530602 0.2210214 0.6466504
## 52 1.0742268 0.8066607 1.3539108 0.3049734 1.4081417 1.2213244 0.4465986
## 53 0.8830411 1.0627098 1.2085486 1.1357344 0.3761157 0.4523313 0.7624112
## 30 31 32 33 34 36 37
## 24 0.5764861 0.4593294 0.8036558 0.7257612 1.3365778 0.6075356 1.5118399
## 35 1.0447836 0.7538619 1.2894477 0.5893651 1.8203788 1.0717295 1.9969675
## 46 0.5800277 0.2239684 0.5280667 0.7611282 1.0346526 0.6151532 1.2122215
## 54 1.2345090 1.7195121 0.9996452 2.2812894 0.5920676 1.2179617 0.4800549
## 55 0.4344837 1.1056573 0.4761307 1.6303579 0.6665289 0.4126870 0.7800714
## 56 1.3584646 0.7685789 1.4625363 0.2678255 1.9609187 1.3908609 2.1373698
## 57 1.3297910 0.9887672 1.5712353 0.6616974 2.1000770 1.3559454 2.2771943
## 58 0.7000240 0.9543022 0.2659761 1.5168346 0.2672094 0.7049530 0.4424106
## 59 0.7934730 1.2691200 0.5461541 1.8282396 0.3327965 0.7817732 0.3905576
## 25 1.0564661 1.1484983 0.5884560 1.6923696 0.2225531 1.0634100 0.3029798
## 26 0.8168804 1.5290503 0.8711564 2.0503894 0.8416267 0.7872145 0.8729583
## 27 0.6284920 0.7646530 0.9996053 0.9328106 1.5226915 0.6475063 1.6909293
## 28 0.4440139 0.4995822 0.2341493 1.0538920 0.7557872 0.4721324 0.9330311
## 29 0.4512197 1.2316048 0.7046928 1.7119650 0.9314560 0.4169631 1.0328937
## 30 0.0000000 0.7970225 0.4871295 1.2608204 0.9462647 0.0355598 1.1012929
## 31 0.7970225 0.0000000 0.7229659 0.5626346 1.1963613 0.8324746 1.3717083
## 32 0.4871295 0.7229659 0.0000000 1.2827543 0.5330131 0.5016587 0.7082865
## 33 1.2608204 0.5626346 1.2827543 0.0000000 1.7557643 1.2953924 1.9297209
## 34 0.9462647 1.1963613 0.5330131 1.7557643 0.0000000 0.9464482 0.1776537
## 36 0.0355598 0.8324746 0.5016587 1.2953924 0.9464482 0.0000000 1.0983611
## 37 1.1012929 1.3717083 0.7082865 1.9297209 0.1776537 1.0983611 0.0000000
## 38 0.7323699 0.1735571 0.7699763 0.5385122 1.2776656 0.7676362 1.4551341
## 39 0.9354185 0.3172334 0.9946869 0.3257096 1.4953630 0.9698910 1.6723219
## 40 0.8273161 0.5079635 1.0277430 0.5396306 1.5567983 0.8577342 1.7338039
## 41 0.3377077 0.8565076 0.8003903 1.1887508 1.2828478 0.3433900 1.4389875
## 42 1.4258230 1.7873815 1.0994798 2.3474864 0.5918354 1.4152664 0.4212188
## 43 0.3110200 0.9335678 0.3359886 1.4601788 0.6629377 0.3001727 0.8054052
## 44 0.3257402 0.8826338 0.2668522 1.4175261 0.6252688 0.3222709 0.7762160
## 45 0.7636893 1.3916183 0.6942102 1.9337441 0.6085349 0.7409867 0.6474195
## 47 0.6473837 1.3995577 0.7964220 1.9010191 0.8924811 0.6151297 0.9599696
## 48 0.6086639 1.3245771 0.6908777 1.8414786 0.7704094 0.5798674 0.8433274
## 49 1.1516260 0.5375265 1.2311847 0.1677749 1.7283549 1.1851482 1.9048625
## 50 0.2350018 0.5853615 0.3476275 1.0915433 0.8710077 0.2673320 1.0416725
## 52 0.8972877 1.6745692 1.0875529 2.1580641 1.1386596 0.8626738 1.1774929
## 53 0.3189163 0.5706397 0.6234922 0.9619207 1.1484196 0.3497497 1.3183777
## 38 39 40 41 42 43 44
## 24 0.2974169 0.4102621 0.2517724 0.4649154 1.8938070 0.84111825 0.82066057
## 35 0.5971425 0.4726161 0.2668092 0.8273914 2.3847061 1.32800001 1.31139147
## 46 0.2436480 0.4667392 0.5451843 0.6863034 1.6186958 0.71146786 0.66269501
## 54 1.7673225 1.9930471 1.9956567 1.5523705 0.3093250 0.92709185 0.93442009
## 55 1.0919094 1.3124969 1.2470039 0.7400456 1.0347829 0.17209958 0.22836666
## 56 0.6929059 0.4680174 0.5474395 1.2200267 2.5497263 1.59548456 1.56155497
## 57 0.8493677 0.6790334 0.5435354 1.0956707 2.6686684 1.61524750 1.59877585
## 58 1.0220302 1.2433783 1.2920764 1.0320930 0.8403928 0.44755687 0.39515426
## 59 1.3080673 1.5342294 1.5336881 1.1239218 0.6340138 0.48247119 0.47908573
## 25 1.2560262 1.4609241 1.5625450 1.3835381 0.7040966 0.80677935 0.75604608
## 26 1.5119777 1.7301106 1.6441411 1.0623226 0.9471514 0.59550679 0.64872322
## 27 0.5984985 0.6574932 0.3931857 0.3514343 2.0430844 0.93631061 0.93813062
## 28 0.5359495 0.7610526 0.8011696 0.6885411 1.3314491 0.45555485 0.39603786
## 29 1.1812624 1.3866280 1.2658855 0.6539935 1.2400598 0.36870707 0.43786081
## 30 0.7323699 0.9354185 0.8273161 0.3377077 1.4258230 0.31102000 0.32574024
## 31 0.1735571 0.3172334 0.5079635 0.8565076 1.7873815 0.93356779 0.88263378
## 32 0.7699763 0.9946869 1.0277430 0.8003903 1.0994798 0.33598861 0.26685219
## 33 0.5385122 0.3257096 0.5396306 1.1887508 2.3474864 1.46017876 1.41752613
## 34 1.2776656 1.4953630 1.5567983 1.2828478 0.5918354 0.66293774 0.62526880
## 36 0.7676362 0.9698910 0.8577342 0.3433900 1.4152664 0.30017270 0.32227089
## 37 1.4551341 1.6723219 1.7338039 1.4389875 0.4212188 0.80540517 0.77621597
## 38 0.0000000 0.2263451 0.3409433 0.7298378 1.8623397 0.92203048 0.88096376
## 39 0.2263451 0.0000000 0.2866417 0.8750671 2.0828393 1.14395773 1.10503585
## 40 0.3409433 0.2866417 0.0000000 0.6738325 2.1257618 1.09121687 1.06815038
## 41 0.7298378 0.8750671 0.6738325 0.0000000 1.7578245 0.64342003 0.66329102
## 42 1.8623397 2.0828393 2.1257618 1.7578245 0.0000000 1.11533223 1.10553804
## 43 0.9220305 1.1439577 1.0912169 0.6434200 1.1153322 0.00000000 0.06916569
## 44 0.8809638 1.1050359 1.0681504 0.6632910 1.1055380 0.06916569 0.00000000
## 45 1.3979128 1.6220824 1.5725958 1.0594594 0.7797063 0.48269474 0.51705503
## 47 1.3652053 1.5772555 1.4715082 0.8687929 1.1010935 0.47999619 0.54388200
## 48 1.3032460 1.5206458 1.4354660 0.8689524 1.0096523 0.39232305 0.45040857
## 49 0.4627343 0.2366449 0.3835663 1.0484143 2.3171394 1.37478729 1.33805572
## 50 0.5530642 0.7730127 0.7312137 0.4731688 1.4107729 0.37159992 0.33904853
## 52 1.6278408 1.8325517 1.7013732 1.0576306 1.2339865 0.76852024 0.83370243
## 53 0.4593097 0.6372053 0.5084121 0.2900799 1.6807864 0.59623146 0.58462976
## 45 47 48 49 50 52 53
## 24 1.3216443 1.2223123 1.1840639 0.5932360 0.4873686 1.4582851 0.2577862
## 35 1.8042534 1.6743322 1.6510224 0.4244009 0.9801133 1.8831513 0.7327144
## 46 1.1756865 1.1756085 1.1015565 0.7031407 0.3621124 1.4509297 0.3968815
## 54 0.5143590 0.8195827 0.7421675 2.2296909 1.2651486 0.9288114 1.5188592
## 55 0.3293681 0.3203846 0.2220467 1.5417359 0.5395074 0.6115301 0.7434318
## 56 2.0771927 2.0058361 1.9603813 0.2326000 1.2243196 2.2445422 1.0424538
## 57 2.0907101 1.9534675 1.9341425 0.5322554 1.2668498 2.1532087 1.0196039
## 58 0.5893370 0.7959603 0.6746760 1.4784942 0.6054966 1.0742268 0.8830411
## 59 0.2757542 0.5715099 0.4532287 1.7707859 0.8039295 0.8066607 1.0627098
## 25 0.8272997 1.0983213 0.9751734 1.6859945 0.9353064 1.3539108 1.2085486
## 26 0.2331290 0.2011225 0.2099088 1.9562484 0.9589343 0.3049734 1.1357344
## 27 1.3871980 1.2174880 1.2098903 0.7747813 0.6530602 1.4081417 0.3761157
## 28 0.8939150 0.9349026 0.8462162 0.9976562 0.2210214 1.2213244 0.4523313
## 29 0.4667082 0.2148020 0.2405690 1.6016888 0.6466504 0.4465986 0.7624112
## 30 0.7636893 0.6473837 0.6086639 1.1516260 0.2350018 0.8972877 0.3189163
## 31 1.3916183 1.3995577 1.3245771 0.5375265 0.5853615 1.6745692 0.5706397
## 32 0.6942102 0.7964220 0.6908777 1.2311847 0.3476275 1.0875529 0.6234922
## 33 1.9337441 1.9010191 1.8414786 0.1677749 1.0915433 2.1580641 0.9619207
## 34 0.6085349 0.8924811 0.7704094 1.7283549 0.8710077 1.1386596 1.1484196
## 36 0.7409867 0.6151297 0.5798674 1.1851482 0.2673320 0.8626738 0.3497497
## 37 0.6474195 0.9599696 0.8433274 1.9048625 1.0416725 1.1774929 1.3183777
## 38 1.3979128 1.3652053 1.3032460 0.4627343 0.5530642 1.6278408 0.4593097
## 39 1.6220824 1.5772555 1.5206458 0.2366449 0.7730127 1.8325517 0.6372053
## 40 1.5725958 1.4715082 1.4354660 0.3835663 0.7312137 1.7013732 0.5084121
## 41 1.0594594 0.8687929 0.8689524 1.0484143 0.4731688 1.0576306 0.2900799
## 42 0.7797063 1.1010935 1.0096523 2.3171394 1.4107729 1.2339865 1.6807864
## 43 0.4826947 0.4799962 0.3923230 1.3747873 0.3715999 0.7685202 0.5962315
## 44 0.5170550 0.5438820 0.4504086 1.3380557 0.3390485 0.8337024 0.5846298
## 45 0.0000000 0.3265138 0.2303971 1.8547994 0.8528979 0.5328630 1.0717119
## 47 0.3265138 0.0000000 0.1232892 1.7977617 0.8170214 0.2911482 0.9646124
## 48 0.2303971 0.1232892 0.0000000 1.7464022 0.7504299 0.4030696 0.9271904
## 49 1.8547994 1.7977617 1.7464022 0.0000000 1.0032006 2.0453617 0.8417461
## 50 0.8528979 0.8170214 0.7504299 1.0032006 0.0000000 1.0892789 0.2775474
## 52 0.5328630 0.2911482 0.4030696 2.0453617 1.0892789 0.0000000 1.2040485
## 53 1.0717119 0.9646124 0.9271904 0.8417461 0.2775474 1.2040485 0.0000000Dist.mat.inv<-1/Dist.mat
diag(Dist.mat.inv)<-0
Dist.mat.inv[1:35,1:35]## 24 35 46 54 55 56 57
## 24 0.0000000 2.0293030 2.5740962 0.5707503 1.0045552 1.2716976 1.2828508
## 35 2.0293030 0.0000000 1.2321604 0.4455692 0.6777262 2.1407381 3.4793782
## 46 2.5740962 1.2321604 0.0000000 0.6548165 1.1318251 1.0698317 0.9270656
## 54 0.5707503 0.4455692 0.6548165 0.0000000 1.2304236 0.4064750 0.3950271
## 55 1.0045552 0.6777262 1.1318251 1.2304236 0.0000000 0.5681235 0.5676258
## 56 1.2716976 2.1407381 1.0698317 0.4064750 0.5681235 0.0000000 2.3082476
## 57 1.2828508 3.4793782 0.9270656 0.3950271 0.5676258 2.3082476 0.0000000
## 58 0.9349013 0.6432004 1.2847135 1.2930032 1.9693200 0.5844775 0.5447832
## 59 0.7744436 0.5605730 0.9346563 2.1628791 2.4438150 0.4998962 0.4829144
## 25 0.7333999 0.5466636 0.9794237 1.2837897 1.1828251 0.5218139 0.4762662
## 26 0.7178863 0.5386462 0.7651798 1.5369484 2.3617925 0.4607789 0.4676516
## 27 3.2678522 2.0616937 1.4720545 0.5368826 0.9433917 1.0969168 1.3416834
## 28 1.6969315 0.9384493 3.3895773 0.8115584 1.6020149 0.8138664 0.7438775
## 29 0.9813997 0.6836608 0.9913312 1.0195326 3.7093349 0.5540722 0.5745501
## 30 1.7346473 0.9571360 1.7240557 0.8100386 2.3015826 0.7361252 0.7519979
## 31 2.1770869 1.3265030 4.4649160 0.5815603 0.9044394 1.3011027 1.0113604
## 32 1.2443138 0.7755258 1.8937001 1.0003549 2.1002635 0.6837437 0.6364419
## 33 1.3778636 1.6967412 1.3138390 0.4383486 0.6133623 3.7337743 1.5112648
## 34 0.7481794 0.5493362 0.9665080 1.6889962 1.5003101 0.5099651 0.4761730
## 36 1.6459940 0.9330712 1.6256113 0.8210439 2.4231441 0.7189792 0.7374928
## 37 0.6614457 0.5007593 0.8249318 2.0830951 1.2819339 0.4678648 0.4391369
## 38 3.3622838 1.6746421 4.1042810 0.5658277 0.9158269 1.4431973 1.1773464
## 39 2.4374664 2.1158821 2.1425242 0.5017443 0.7619066 2.1366728 1.4726817
## 40 3.9718405 3.7479972 1.8342421 0.5010882 0.8019221 1.8266857 1.8398067
## 41 2.1509291 1.2086178 1.4570816 0.6441761 1.3512681 0.8196542 0.9126829
## 42 0.5280369 0.4193389 0.6177813 3.2328462 0.9663862 0.3921990 0.3747187
## 43 1.1888935 0.7530120 1.4055449 1.0786418 5.8105896 0.6267688 0.6191002
## 44 1.2185306 0.7625488 1.5089898 1.0701825 4.3789229 0.6403873 0.6254785
## 45 0.7566332 0.5542459 0.8505669 1.9441675 3.0361165 0.4814190 0.4783064
## 47 0.8181216 0.5972530 0.8506233 1.2201331 3.1212483 0.4985452 0.5119102
## 48 0.8445490 0.6056853 0.9078064 1.3474048 4.5035570 0.5101048 0.5170250
## 49 1.6856697 2.3562626 1.4221905 0.4484927 0.6486195 4.2992256 1.8787973
## 50 2.0518351 1.0202902 2.7615735 0.7904210 1.8535426 0.8167802 0.7893596
## 52 0.6857370 0.5310248 0.6892133 1.0766449 1.6352424 0.4455252 0.4644232
## 53 3.8791835 1.3647882 2.5196437 0.6583889 1.3451133 0.9592751 0.9807730
## 58 59 25 26 27 28 29
## 24 0.9349013 0.7744436 0.7333999 0.7178863 3.2678522 1.6969315 0.9813997
## 35 0.6432004 0.5605730 0.5466636 0.5386462 2.0616937 0.9384493 0.6836608
## 46 1.2847135 0.9346563 0.9794237 0.7651798 1.4720545 3.3895773 0.9913312
## 54 1.2930032 2.1628791 1.2837897 1.5369484 0.5368826 0.8115584 1.0195326
## 55 1.9693200 2.4438150 1.1828251 2.3617925 0.9433917 1.6020149 3.7093349
## 56 0.5844775 0.4998962 0.5218139 0.4607789 1.0969168 0.8138664 0.5540722
## 57 0.5447832 0.4829144 0.4762662 0.4676516 1.3416834 0.7438775 0.5745501
## 58 0.0000000 2.7797608 2.7704972 1.2387409 0.7947570 2.0302529 1.2897022
## 59 2.7797608 0.0000000 1.8114091 1.9651292 0.7061964 1.2901239 1.5453829
## 25 2.7704972 1.8114091 0.0000000 0.9431234 0.6313128 1.2904930 0.8970013
## 26 1.2387409 1.9651292 0.9431234 0.0000000 0.7101183 0.9578890 2.4318068
## 27 0.7947570 0.7061964 0.6313128 0.7101183 0.0000000 1.2204186 0.9970372
## 28 2.0302529 1.2901239 1.2904930 0.9578890 1.2204186 0.0000000 1.2540495
## 29 1.2897022 1.5453829 0.8970013 2.4318068 0.9970372 1.2540495 0.0000000
## 30 1.4285225 1.2602823 0.9465519 1.2241694 1.5911102 2.2521820 2.2162151
## 31 1.0478861 0.7879476 0.8707022 0.6540007 1.3077827 2.0016725 0.8119487
## 32 3.7597369 1.8309850 1.6993625 1.1478995 1.0003949 4.2707798 1.4190580
## 33 0.6592676 0.5469743 0.5908875 0.4877122 1.0720290 0.9488638 0.5841241
## 34 3.7423830 3.0048395 4.4933107 1.1881752 0.6567318 1.3231238 1.0735880
## 36 1.4185344 1.2791433 0.9403711 1.2703017 1.5443866 2.1180497 2.3982936
## 37 2.2603435 2.5604418 3.3005501 1.1455301 0.5913908 1.0717756 0.9681539
## 38 0.9784447 0.7644867 0.7961617 0.6613854 1.6708481 1.8658474 0.8465520
## 39 0.8042605 0.6517930 0.6844983 0.5779977 1.5209282 1.3139696 0.7211740
## 40 0.7739480 0.6520231 0.6399816 0.6082203 2.5433276 1.2481752 0.7899608
## 41 0.9689049 0.8897416 0.7227846 0.9413337 2.8454819 1.4523461 1.5290672
## 42 1.1899198 1.5772526 1.4202597 1.0557974 0.4894560 0.7510614 0.8064127
## 43 2.2343529 2.0726626 1.2394963 1.6792420 1.0680216 2.1951253 2.7121802
## 44 2.5306573 2.0873091 1.3226707 1.5414894 1.0659496 2.5250111 2.2838308
## 45 1.6968222 3.6264184 1.2087519 4.2894700 0.7208776 1.1186745 2.1426665
## 47 1.2563441 1.7497510 0.9104804 4.9720937 0.8213634 1.0696302 4.6554511
## 48 1.4821930 2.2063915 1.0254586 4.7639735 0.8265212 1.1817312 4.1568109
## 49 0.6763638 0.5647210 0.5931218 0.5111825 1.2906868 1.0023493 0.6243410
## 50 1.6515369 1.2438902 1.0691684 1.0428243 1.5312525 4.5244490 1.5464306
## 52 0.9309021 1.2396786 0.7386011 3.2789749 0.7101558 0.8187833 2.2391473
## 53 1.1324502 0.9409906 0.8274388 0.8804876 2.6587563 2.2107687 1.3116282
## 30 31 32 33 34 36 37
## 24 1.7346473 2.1770869 1.2443138 1.3778636 0.7481794 1.6459940 0.6614457
## 35 0.9571360 1.3265030 0.7755258 1.6967412 0.5493362 0.9330712 0.5007593
## 46 1.7240557 4.4649160 1.8937001 1.3138390 0.9665080 1.6256113 0.8249318
## 54 0.8100386 0.5815603 1.0003549 0.4383486 1.6889962 0.8210439 2.0830951
## 55 2.3015826 0.9044394 2.1002635 0.6133623 1.5003101 2.4231441 1.2819339
## 56 0.7361252 1.3011027 0.6837437 3.7337743 0.5099651 0.7189792 0.4678648
## 57 0.7519979 1.0113604 0.6364419 1.5112648 0.4761730 0.7374928 0.4391369
## 58 1.4285225 1.0478861 3.7597369 0.6592676 3.7423830 1.4185344 2.2603435
## 59 1.2602823 0.7879476 1.8309850 0.5469743 3.0048395 1.2791433 2.5604418
## 25 0.9465519 0.8707022 1.6993625 0.5908875 4.4933107 0.9403711 3.3005501
## 26 1.2241694 0.6540007 1.1478995 0.4877122 1.1881752 1.2703017 1.1455301
## 27 1.5911102 1.3077827 1.0003949 1.0720290 0.6567318 1.5443866 0.5913908
## 28 2.2521820 2.0016725 4.2707798 0.9488638 1.3231238 2.1180497 1.0717756
## 29 2.2162151 0.8119487 1.4190580 0.5841241 1.0735880 2.3982936 0.9681539
## 30 0.0000000 1.2546698 2.0528421 0.7931344 1.0567868 28.1216463 0.9080237
## 31 1.2546698 0.0000000 1.3831911 1.7773526 0.8358679 1.2012379 0.7290180
## 32 2.0528421 1.3831911 0.0000000 0.7795725 1.8761265 1.9933873 1.4118580
## 33 0.7931344 1.7773526 0.7795725 0.0000000 0.5695525 0.7719668 0.5182096
## 34 1.0567868 0.8358679 1.8761265 0.5695525 0.0000000 1.0565819 5.6289273
## 36 28.1216463 1.2012379 1.9933873 0.7719668 1.0565819 0.0000000 0.9104474
## 37 0.9080237 0.7290180 1.4118580 0.5182096 5.6289273 0.9104474 0.0000000
## 38 1.3654303 5.7617935 1.2987412 1.8569680 0.7826774 1.3027005 0.6872219
## 39 1.0690402 3.1522535 1.0053414 3.0702199 0.6687339 1.0310437 0.5979710
## 40 1.2087279 1.9686455 0.9730059 1.8531196 0.6423440 1.1658624 0.5767665
## 41 2.9611405 1.1675320 1.2493905 0.8412192 0.7795157 2.9121404 0.6949331
## 42 0.7013493 0.5594776 0.9095210 0.4259876 1.6896590 0.7065808 2.3740633
## 43 3.2152273 1.0711595 2.9762914 0.6848477 1.5084373 3.3314155 1.2416111
## 44 3.0699308 1.1329727 3.7473930 0.7054544 1.5993122 3.1029796 1.2883012
## 45 1.3094331 0.7185879 1.4404859 0.5171315 1.6432913 1.3495518 1.5445936
## 47 1.5446790 0.7145115 1.2556157 0.5260336 1.1204719 1.6256735 1.0416996
## 48 1.6429429 0.7549579 1.4474343 0.5430419 1.2980112 1.7245321 1.1857792
## 49 0.8683375 1.8603733 0.8122258 5.9603668 0.5785849 0.8437763 0.5249723
## 50 4.2552861 1.7083462 2.8766422 0.9161340 1.1480955 3.7406664 0.9599946
## 52 1.1144698 0.5971685 0.9194955 0.4633783 0.8782256 1.1591868 0.8492620
## 53 3.1356194 1.7524193 1.6038692 1.0395868 0.8707619 2.8591873 0.7585080
## 38 39 40 41 42 43 44
## 24 3.3622838 2.4374664 3.9718405 2.1509291 0.5280369 1.1888935 1.2185306
## 35 1.6746421 2.1158821 3.7479972 1.2086178 0.4193389 0.7530120 0.7625488
## 46 4.1042810 2.1425242 1.8342421 1.4570816 0.6177813 1.4055449 1.5089898
## 54 0.5658277 0.5017443 0.5010882 0.6441761 3.2328462 1.0786418 1.0701825
## 55 0.9158269 0.7619066 0.8019221 1.3512681 0.9663862 5.8105896 4.3789229
## 56 1.4431973 2.1366728 1.8266857 0.8196542 0.3921990 0.6267688 0.6403873
## 57 1.1773464 1.4726817 1.8398067 0.9126829 0.3747187 0.6191002 0.6254785
## 58 0.9784447 0.8042605 0.7739480 0.9689049 1.1899198 2.2343529 2.5306573
## 59 0.7644867 0.6517930 0.6520231 0.8897416 1.5772526 2.0726626 2.0873091
## 25 0.7961617 0.6844983 0.6399816 0.7227846 1.4202597 1.2394963 1.3226707
## 26 0.6613854 0.5779977 0.6082203 0.9413337 1.0557974 1.6792420 1.5414894
## 27 1.6708481 1.5209282 2.5433276 2.8454819 0.4894560 1.0680216 1.0659496
## 28 1.8658474 1.3139696 1.2481752 1.4523461 0.7510614 2.1951253 2.5250111
## 29 0.8465520 0.7211740 0.7899608 1.5290672 0.8064127 2.7121802 2.2838308
## 30 1.3654303 1.0690402 1.2087279 2.9611405 0.7013493 3.2152273 3.0699308
## 31 5.7617935 3.1522535 1.9686455 1.1675320 0.5594776 1.0711595 1.1329727
## 32 1.2987412 1.0053414 0.9730059 1.2493905 0.9095210 2.9762914 3.7473930
## 33 1.8569680 3.0702199 1.8531196 0.8412192 0.4259876 0.6848477 0.7054544
## 34 0.7826774 0.6687339 0.6423440 0.7795157 1.6896590 1.5084373 1.5993122
## 36 1.3027005 1.0310437 1.1658624 2.9121404 0.7065808 3.3314155 3.1029796
## 37 0.6872219 0.5979710 0.5767665 0.6949331 2.3740633 1.2416111 1.2883012
## 38 0.0000000 4.4180317 2.9330388 1.3701675 0.5369590 1.0845628 1.1351205
## 39 4.4180317 0.0000000 3.4886764 1.1427695 0.4801138 0.8741582 0.9049480
## 40 2.9330388 3.4886764 0.0000000 1.4840483 0.4704196 0.9164081 0.9361978
## 41 1.3701675 1.1427695 1.4840483 0.0000000 0.5688850 1.5541947 1.5076339
## 42 0.5369590 0.4801138 0.4704196 0.5688850 0.0000000 0.8965938 0.9045369
## 43 1.0845628 0.8741582 0.9164081 1.5541947 0.8965938 0.0000000 14.4580362
## 44 1.1351205 0.9049480 0.9361978 1.5076339 0.9045369 14.4580362 0.0000000
## 45 0.7153522 0.6164915 0.6358913 0.9438776 1.2825342 2.0717027 1.9340301
## 47 0.7324906 0.6340127 0.6795749 1.1510223 0.9081881 2.0833499 1.8386341
## 48 0.7673149 0.6576153 0.6966379 1.1508111 0.9904400 2.5489198 2.2202064
## 49 2.1610674 4.2257407 2.6071111 0.9538214 0.4315666 0.7273853 0.7473530
## 50 1.8081083 1.2936398 1.3675892 2.1134105 0.7088313 2.6910663 2.9494303
## 52 0.6143107 0.5456872 0.5877605 0.9455097 0.8103817 1.3012019 1.1994687
## 53 2.1771804 1.5693529 1.9669083 3.4473255 0.5949596 1.6772010 1.7104843
## 45 47 48 49 50 52 53
## 24 0.7566332 0.8181216 0.8445490 1.6856697 2.0518351 0.6857370 3.8791835
## 35 0.5542459 0.5972530 0.6056853 2.3562626 1.0202902 0.5310248 1.3647882
## 46 0.8505669 0.8506233 0.9078064 1.4221905 2.7615735 0.6892133 2.5196437
## 54 1.9441675 1.2201331 1.3474048 0.4484927 0.7904210 1.0766449 0.6583889
## 55 3.0361165 3.1212483 4.5035570 0.6486195 1.8535426 1.6352424 1.3451133
## 56 0.4814190 0.4985452 0.5101048 4.2992256 0.8167802 0.4455252 0.9592751
## 57 0.4783064 0.5119102 0.5170250 1.8787973 0.7893596 0.4644232 0.9807730
## 58 1.6968222 1.2563441 1.4821930 0.6763638 1.6515369 0.9309021 1.1324502
## 59 3.6264184 1.7497510 2.2063915 0.5647210 1.2438902 1.2396786 0.9409906
## 25 1.2087519 0.9104804 1.0254586 0.5931218 1.0691684 0.7386011 0.8274388
## 26 4.2894700 4.9720937 4.7639735 0.5111825 1.0428243 3.2789749 0.8804876
## 27 0.7208776 0.8213634 0.8265212 1.2906868 1.5312525 0.7101558 2.6587563
## 28 1.1186745 1.0696302 1.1817312 1.0023493 4.5244490 0.8187833 2.2107687
## 29 2.1426665 4.6554511 4.1568109 0.6243410 1.5464306 2.2391473 1.3116282
## 30 1.3094331 1.5446790 1.6429429 0.8683375 4.2552861 1.1144698 3.1356194
## 31 0.7185879 0.7145115 0.7549579 1.8603733 1.7083462 0.5971685 1.7524193
## 32 1.4404859 1.2556157 1.4474343 0.8122258 2.8766422 0.9194955 1.6038692
## 33 0.5171315 0.5260336 0.5430419 5.9603668 0.9161340 0.4633783 1.0395868
## 34 1.6432913 1.1204719 1.2980112 0.5785849 1.1480955 0.8782256 0.8707619
## 36 1.3495518 1.6256735 1.7245321 0.8437763 3.7406664 1.1591868 2.8591873
## 37 1.5445936 1.0416996 1.1857792 0.5249723 0.9599946 0.8492620 0.7585080
## 38 0.7153522 0.7324906 0.7673149 2.1610674 1.8081083 0.6143107 2.1771804
## 39 0.6164915 0.6340127 0.6576153 4.2257407 1.2936398 0.5456872 1.5693529
## 40 0.6358913 0.6795749 0.6966379 2.6071111 1.3675892 0.5877605 1.9669083
## 41 0.9438776 1.1510223 1.1508111 0.9538214 2.1134105 0.9455097 3.4473255
## 42 1.2825342 0.9081881 0.9904400 0.4315666 0.7088313 0.8103817 0.5949596
## 43 2.0717027 2.0833499 2.5489198 0.7273853 2.6910663 1.3012019 1.6772010
## 44 1.9340301 1.8386341 2.2202064 0.7473530 2.9494303 1.1994687 1.7104843
## 45 0.0000000 3.0626574 4.3403326 0.5391419 1.1724732 1.8766550 0.9330866
## 47 3.0626574 0.0000000 8.1110081 0.5562472 1.2239581 3.4346770 1.0366858
## 48 4.3403326 8.1110081 0.0000000 0.5726058 1.3325694 2.4809609 1.0785271
## 49 0.5391419 0.5562472 0.5726058 0.0000000 0.9968096 0.4889111 1.1880066
## 50 1.1724732 1.2239581 1.3325694 0.9968096 0.0000000 0.9180385 3.6029885
## 52 1.8766550 3.4346770 2.4809609 0.4889111 0.9180385 0.0000000 0.8305313
## 53 0.9330866 1.0366858 1.0785271 1.1880066 3.6029885 0.8305313 0.0000000Dist.mat.invs<-mat2listw(Dist.mat.inv,
style="W")
summary(Dist.mat.invs)## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 35
## Number of nonzero links: 1190
## Percentage nonzero weights: 97.14286
## Average number of links: 34
## Link number distribution:
##
## 34
## 35
## 35 least connected regions:
## 24 35 46 54 55 56 57 58 59 25 26 27 28 29 30 31 32 33 34 36 37 38 39 40 41 42 43 44 45 47 48 49 50 52 53 with 34 links
## 35 most connected regions:
## 24 35 46 54 55 56 57 58 59 25 26 27 28 29 30 31 32 33 34 36 37 38 39 40 41 42 43 44 45 47 48 49 50 52 53 with 34 links
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 35 1225 35 3.50751 141.1293Asumsi Autokorelasi Spasial
#MORAN’S INDEX and LOCAL MORAN’s INDEX
Kasus<-data$BGK#Berdasarkan contiguity #Rook
moran.test(Kasus,WLR)##
## Moran I test under randomisation
##
## data: Kasus
## weights: WLR
##
## Moran I statistic standard deviate = 0.9792, p-value = 0.1637
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.08487630 -0.02941176 0.01362247moran.plot(Kasus,WLR)
locm<-localmoran(Kasus,WLR)
locm## Ii E.Ii Var.Ii Z.Ii Pr(z != E(Ii))
## 1 0.638002835 -0.1476468625 0.622880410 0.99546641 0.31950947
## 2 -0.206074763 -0.0591914090 0.227813670 -0.30773911 0.75828086
## 3 0.157913102 -0.0163158335 0.098729524 0.55449389 0.57924089
## 4 0.056635068 -0.0006964607 0.007627612 0.65644634 0.51153698
## 5 -0.068912058 -0.0149716249 0.050834101 -0.23924169 0.81091818
## 6 -0.191426973 -0.0437377150 0.332696694 -0.25604993 0.79791228
## 7 -0.014057679 -0.0043599745 0.047575224 -0.04446093 0.96453699
## 8 0.596171957 -0.0221903113 0.133474969 1.69255608 0.09054000
## 9 -0.022258077 -0.0002794180 0.001142754 -0.65016676 0.51558451
## 10 -0.203478037 -0.0345149104 0.365213536 -0.27958799 0.77979363
## 11 0.150264857 -0.0010962133 0.006735980 1.84422449 0.06515043
## 12 -0.037038954 -0.0002363011 0.001453266 -0.96539834 0.33434533
## 13 -0.167657865 -0.0128129863 0.077809369 -0.55511236 0.57881781
## 14 0.299159898 -0.0123010611 0.206177490 0.68593504 0.49275407
## 15 -0.378505718 -0.0022295497 0.077860259 -1.34849330 0.17749977
## 16 0.191725030 -0.0056743985 0.095746419 0.63794703 0.52350816
## 17 0.366178660 -0.0053748008 0.058589038 1.53501677 0.12477971
## 18 -1.572495601 -0.0380983112 0.621885599 -1.94572999 0.05168718
## 19 -0.065544933 -0.0034543887 0.027383172 -0.37521793 0.70749839
## 20 0.095254759 -0.0555865725 0.259832178 0.29591971 0.76729140
## 21 -0.124619263 -0.0349054438 0.207226422 -0.19707738 0.84376698
## 22 0.087296248 -0.0004832593 0.002971340 1.61033760 0.10732418
## 23 -0.091664047 -0.0110810466 0.087167953 -0.27293863 0.78490039
## 24 0.471566912 -0.0103905688 0.081793448 1.68519230 0.09195148
## 25 -0.007904099 -0.0004248781 0.004654514 -0.10962742 0.91270486
## 26 -0.051433808 -0.0002013453 0.003416081 -0.87655904 0.38072620
## 27 1.614333944 -0.0137481437 0.474569629 2.36334066 0.01811101
## 28 0.630119346 -0.1639777263 0.472541738 1.15519023 0.24801253
## 29 -0.106969255 -0.0169325828 0.182432014 -0.21079917 0.83304398
## 30 0.764944921 -0.0789746761 0.447446920 1.26162507 0.20708373
## 31 0.437606646 -0.0405386400 0.426276320 0.73234256 0.46395951
## 32 -0.279239493 -0.0701007143 0.518529806 -0.29043417 0.77148411
## 33 -0.584290147 -0.0406469307 0.310185573 -0.97611992 0.32900504
## 34 0.946774171 -0.0314316084 0.516619726 1.36095803 0.17352695
## 35 -0.359707110 -0.0348050976 0.137428784 -0.87642256 0.38080036
## attr(,"call")
## localmoran(x = Kasus, listw = WLR)
## attr(,"class")
## [1] "localmoran" "matrix" "array"
## attr(,"quadr")
## mean median pysal
## 1 High-High High-High High-High
## 2 High-Low High-Low High-Low
## 3 High-High High-High High-High
## 4 Low-Low Low-Low Low-Low
## 5 High-Low High-Low High-Low
## 6 Low-High Low-Low Low-High
## 7 Low-Low Low-Low Low-High
## 8 High-High High-High High-High
## 9 Low-High High-High Low-High
## 10 High-Low High-Low High-Low
## 11 Low-Low Low-Low Low-Low
## 12 Low-High High-High Low-High
## 13 Low-High Low-High Low-High
## 14 Low-Low Low-Low Low-Low
## 15 Low-High Low-High Low-High
## 16 High-High High-High High-High
## 17 High-High High-High High-High
## 18 High-Low High-Low High-Low
## 19 Low-High Low-High Low-High
## 20 High-Low High-Low High-High
## 21 Low-Low Low-Low Low-High
## 22 High-High High-High High-High
## 23 Low-High Low-Low Low-High
## 24 High-High High-High High-High
## 25 Low-Low Low-Low Low-High
## 26 High-Low High-Low High-Low
## 27 High-High High-High High-High
## 28 High-High High-High High-High
## 29 Low-Low Low-Low Low-High
## 30 Low-Low Low-Low Low-Low
## 31 Low-Low Low-Low Low-Low
## 32 Low-High Low-High Low-High
## 33 Low-High Low-High Low-High
## 34 Low-Low Low-Low Low-Low
## 35 Low-High Low-High Low-High#Queen
moran.test(Kasus,WLQ)##
## Moran I test under randomisation
##
## data: Kasus
## weights: WLQ
##
## Moran I statistic standard deviate = 0.9792, p-value = 0.1637
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.08487630 -0.02941176 0.01362247moran.plot(Kasus,WLQ)
locm<-localmoran(Kasus,WLQ)
locm## Ii E.Ii Var.Ii Z.Ii Pr(z != E(Ii))
## 1 0.638002835 -0.1476468625 0.622880410 0.99546641 0.31950947
## 2 -0.206074763 -0.0591914090 0.227813670 -0.30773911 0.75828086
## 3 0.157913102 -0.0163158335 0.098729524 0.55449389 0.57924089
## 4 0.056635068 -0.0006964607 0.007627612 0.65644634 0.51153698
## 5 -0.068912058 -0.0149716249 0.050834101 -0.23924169 0.81091818
## 6 -0.191426973 -0.0437377150 0.332696694 -0.25604993 0.79791228
## 7 -0.014057679 -0.0043599745 0.047575224 -0.04446093 0.96453699
## 8 0.596171957 -0.0221903113 0.133474969 1.69255608 0.09054000
## 9 -0.022258077 -0.0002794180 0.001142754 -0.65016676 0.51558451
## 10 -0.203478037 -0.0345149104 0.365213536 -0.27958799 0.77979363
## 11 0.150264857 -0.0010962133 0.006735980 1.84422449 0.06515043
## 12 -0.037038954 -0.0002363011 0.001453266 -0.96539834 0.33434533
## 13 -0.167657865 -0.0128129863 0.077809369 -0.55511236 0.57881781
## 14 0.299159898 -0.0123010611 0.206177490 0.68593504 0.49275407
## 15 -0.378505718 -0.0022295497 0.077860259 -1.34849330 0.17749977
## 16 0.191725030 -0.0056743985 0.095746419 0.63794703 0.52350816
## 17 0.366178660 -0.0053748008 0.058589038 1.53501677 0.12477971
## 18 -1.572495601 -0.0380983112 0.621885599 -1.94572999 0.05168718
## 19 -0.065544933 -0.0034543887 0.027383172 -0.37521793 0.70749839
## 20 0.095254759 -0.0555865725 0.259832178 0.29591971 0.76729140
## 21 -0.124619263 -0.0349054438 0.207226422 -0.19707738 0.84376698
## 22 0.087296248 -0.0004832593 0.002971340 1.61033760 0.10732418
## 23 -0.091664047 -0.0110810466 0.087167953 -0.27293863 0.78490039
## 24 0.471566912 -0.0103905688 0.081793448 1.68519230 0.09195148
## 25 -0.007904099 -0.0004248781 0.004654514 -0.10962742 0.91270486
## 26 -0.051433808 -0.0002013453 0.003416081 -0.87655904 0.38072620
## 27 1.614333944 -0.0137481437 0.474569629 2.36334066 0.01811101
## 28 0.630119346 -0.1639777263 0.472541738 1.15519023 0.24801253
## 29 -0.106969255 -0.0169325828 0.182432014 -0.21079917 0.83304398
## 30 0.764944921 -0.0789746761 0.447446920 1.26162507 0.20708373
## 31 0.437606646 -0.0405386400 0.426276320 0.73234256 0.46395951
## 32 -0.279239493 -0.0701007143 0.518529806 -0.29043417 0.77148411
## 33 -0.584290147 -0.0406469307 0.310185573 -0.97611992 0.32900504
## 34 0.946774171 -0.0314316084 0.516619726 1.36095803 0.17352695
## 35 -0.359707110 -0.0348050976 0.137428784 -0.87642256 0.38080036
## attr(,"call")
## localmoran(x = Kasus, listw = WLQ)
## attr(,"class")
## [1] "localmoran" "matrix" "array"
## attr(,"quadr")
## mean median pysal
## 1 High-High High-High High-High
## 2 High-Low High-Low High-Low
## 3 High-High High-High High-High
## 4 Low-Low Low-Low Low-Low
## 5 High-Low High-Low High-Low
## 6 Low-High Low-Low Low-High
## 7 Low-Low Low-Low Low-High
## 8 High-High High-High High-High
## 9 Low-High High-High Low-High
## 10 High-Low High-Low High-Low
## 11 Low-Low Low-Low Low-Low
## 12 Low-High High-High Low-High
## 13 Low-High Low-High Low-High
## 14 Low-Low Low-Low Low-Low
## 15 Low-High Low-High Low-High
## 16 High-High High-High High-High
## 17 High-High High-High High-High
## 18 High-Low High-Low High-Low
## 19 Low-High Low-High Low-High
## 20 High-Low High-Low High-High
## 21 Low-Low Low-Low Low-High
## 22 High-High High-High High-High
## 23 Low-High Low-Low Low-High
## 24 High-High High-High High-High
## 25 Low-Low Low-Low Low-High
## 26 High-Low High-Low High-Low
## 27 High-High High-High High-High
## 28 High-High High-High High-High
## 29 Low-Low Low-Low Low-High
## 30 Low-Low Low-Low Low-Low
## 31 Low-Low Low-Low Low-Low
## 32 Low-High Low-High Low-High
## 33 Low-High Low-High Low-High
## 34 Low-Low Low-Low Low-Low
## 35 Low-High Low-High Low-High#Berdasarkan jarak #k=2
moran.test(Kasus,WLJ2)##
## Moran I test under randomisation
##
## data: Kasus
## weights: WLJ2
##
## Moran I statistic standard deviate = 1.2819, p-value = 0.09994
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.16178383 -0.02941176 0.02224650moran.plot(Kasus,WLJ2)
locm<-localmoran(Kasus,WLJ2)
locm## Ii E.Ii Var.Ii Z.Ii Pr(z != E(Ii))
## 1 -0.552803154 -0.1476468625 2.135589977 -0.27724495 0.781592024
## 2 0.148498319 -0.0591914090 0.945004855 0.21364759 0.830821885
## 3 0.211309439 -0.0163158335 0.272357309 0.43616515 0.662716911
## 4 0.001133344 -0.0006964607 0.011810496 0.01683723 0.986566465
## 5 -0.174914484 -0.0149716249 0.250260188 -0.31971939 0.749181062
## 6 0.247369320 -0.0437377150 0.709752948 0.34554061 0.729687984
## 7 -0.038341868 -0.0043599745 0.073664863 -0.12520376 0.900362239
## 8 0.036818382 -0.0221903113 0.368206811 0.09724563 0.922531333
## 9 0.053679256 -0.0002794180 0.004740314 0.78371418 0.433207841
## 10 -0.775689286 -0.0345149104 0.565491927 -0.98561474 0.324322200
## 11 0.271502307 -0.0010962133 0.018582015 1.99975639 0.045526575
## 12 -0.095027320 -0.0002363011 0.004009010 -1.49709245 0.134369205
## 13 0.246884222 -0.0128129863 0.214646535 0.56053836 0.575112281
## 14 0.909488653 -0.0123010611 0.206177490 2.03007103 0.042349320
## 15 -0.027418259 -0.0022295497 0.037750428 -0.12964177 0.896849851
## 16 0.191725030 -0.0056743985 0.095746419 0.63794703 0.523508161
## 17 0.044580505 -0.0053748008 0.090718511 0.16585694 0.868269531
## 18 -1.572495601 -0.0380983112 0.621885599 -1.94572999 0.051687182
## 19 0.001047215 -0.0034543887 0.058417433 0.01862499 0.985140267
## 20 -0.997320559 -0.0555865725 0.890853183 -0.99775790 0.318396768
## 21 -0.403390847 -0.0349054438 0.571659095 -0.48736207 0.626001765
## 22 -0.011188221 -0.0004832593 0.008196801 -0.11823959 0.905877818
## 23 0.434466421 -0.0110810466 0.185958300 1.03320428 0.301508312
## 24 1.087453975 -0.0103905688 0.174492689 2.62816260 0.008584747
## 25 0.081919493 -0.0004248781 0.007206990 0.96996702 0.332062930
## 26 -0.051433808 -0.0002013453 0.003416081 -0.87655904 0.380726196
## 27 1.051063390 -0.0137481437 0.230094366 2.21983020 0.026430296
## 28 1.649484466 -0.1639777263 2.326359324 1.18896784 0.234452322
## 29 0.518636408 -0.0169325828 0.282475377 1.00768577 0.313605339
## 30 1.120070408 -0.0789746761 1.234336330 1.07924180 0.280479951
## 31 1.075220871 -0.0405386400 0.660040753 1.37336262 0.169639645
## 32 0.062778989 -0.0701007143 1.106196919 0.12634045 0.899462436
## 33 0.549795521 -0.0406469307 0.661729222 0.72583418 0.467940428
## 34 0.946774171 -0.0314316084 0.516619726 1.36095803 0.173526954
## 35 -0.579242658 -0.0348050976 0.570074956 -0.72107776 0.470861676
## attr(,"call")
## localmoran(x = Kasus, listw = WLJ2)
## attr(,"class")
## [1] "localmoran" "matrix" "array"
## attr(,"quadr")
## mean median pysal
## 1 High-Low High-Low High-Low
## 2 High-High High-High High-High
## 3 High-High High-High High-High
## 4 Low-High Low-High Low-Low
## 5 High-Low High-Low High-Low
## 6 Low-Low Low-Low Low-Low
## 7 Low-High Low-High Low-High
## 8 High-High High-High High-High
## 9 Low-Low High-Low Low-Low
## 10 High-Low High-Low High-Low
## 11 Low-Low Low-Low Low-Low
## 12 Low-High High-High Low-High
## 13 Low-Low Low-Low Low-Low
## 14 Low-Low Low-Low Low-Low
## 15 Low-High Low-High Low-High
## 16 High-High High-High High-High
## 17 High-High High-High High-High
## 18 High-Low High-Low High-Low
## 19 Low-High Low-High Low-Low
## 20 High-Low High-Low High-Low
## 21 Low-High Low-High Low-High
## 22 High-High High-High High-Low
## 23 Low-Low Low-Low Low-Low
## 24 High-High High-High High-High
## 25 Low-Low Low-Low Low-Low
## 26 High-Low High-Low High-Low
## 27 High-High High-High High-High
## 28 High-High High-High High-High
## 29 Low-Low Low-Low Low-Low
## 30 Low-Low Low-Low Low-Low
## 31 Low-Low Low-Low Low-Low
## 32 Low-High Low-High Low-Low
## 33 Low-Low Low-Low Low-Low
## 34 Low-Low Low-Low Low-Low
## 35 Low-High Low-High Low-High#k=3
moran.test(Kasus,WLJ3)##
## Moran I test under randomisation
##
## data: Kasus
## weights: WLJ3
##
## Moran I statistic standard deviate = 1.3743, p-value = 0.08467
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.13908523 -0.02941176 0.01503179moran.plot(Kasus,WLJ3)
locm<-localmoran(Kasus,WLJ3)
locm## Ii E.Ii Var.Ii Z.Ii Pr(z != E(Ii))
## 1 -0.2728028265 -0.1476468625 1.379235193 -0.1065693666 0.91513062
## 2 -0.6310437542 -0.0591914090 0.610315635 -0.7319925459 0.46417312
## 3 -0.0229925212 -0.0163158335 0.175897428 -0.0159195692 0.98729856
## 4 0.0566350680 -0.0006964607 0.007627612 0.6564463394 0.51153698
## 5 0.4449353400 -0.0149716249 0.161626371 1.1439679980 0.25263691
## 6 0.3214176875 -0.0437377150 0.458382112 0.5393415759 0.58965119
## 7 0.1725735996 -0.0043599745 0.047575224 0.8111848482 0.41725953
## 8 -0.0036750326 -0.0221903113 0.237800232 0.0379685591 0.96971275
## 9 0.0075655498 -0.0002794180 0.003061453 0.1417840488 0.88725058
## 10 -0.2034780371 -0.0345149104 0.365213536 -0.2795879853 0.77979363
## 11 0.2298292969 -0.0010962133 0.012000884 2.1079741643 0.03503322
## 12 -0.0308494081 -0.0002363011 0.002589152 -0.6016287877 0.54742126
## 13 0.0007240007 -0.0128129863 0.138625887 0.0363579879 0.97099691
## 14 0.4525232601 -0.0123010611 0.133156296 1.2738197873 0.20272729
## 15 -0.0810251612 -0.0022295497 0.024380485 -0.5046390561 0.61381237
## 16 0.0379480883 -0.0056743985 0.061836229 0.1754239643 0.86074651
## 17 0.3661786602 -0.0053748008 0.058589038 1.5350167741 0.12477971
## 18 -1.2811936295 -0.0380983112 0.401634449 -1.9615028916 0.04982039
## 19 -0.0985277370 -0.0034543887 0.037727926 -0.4894714048 0.62450799
## 20 -0.3515773092 -0.0555865725 0.575342681 -0.3902247611 0.69637035
## 21 -0.2335330842 -0.0349054438 0.369196499 -0.3268969712 0.74374581
## 22 0.0243650136 -0.0004832593 0.005293767 0.3415181419 0.73271355
## 23 0.1680347216 -0.0110810466 0.120098069 0.5168515318 0.60525980
## 24 0.6033597576 -0.0103905688 0.112693195 1.8282807653 0.06750743
## 25 -0.0004645777 -0.0004248781 0.004654514 -0.0005819009 0.99953571
## 26 -0.0437409939 -0.0002013453 0.002206219 -0.9269592287 0.35394770
## 27 1.0140119905 -0.0137481437 0.148602611 2.6661129700 0.00767339
## 28 1.4361146343 -0.1639777263 1.502440397 1.3054084684 0.19175380
## 29 0.3704092822 -0.0169325828 0.182432014 0.9068676559 0.36447679
## 30 1.0999560457 -0.0789746761 0.797175547 1.3204175896 0.18669563
## 31 0.4376066465 -0.0405386400 0.426276320 0.7323425622 0.46395951
## 32 0.3577233096 -0.0701007143 0.714418844 0.5061610445 0.61274360
## 33 -0.1721814531 -0.0406469307 0.427366789 -0.2012052773 0.84053807
## 34 1.0357358980 -0.0314316084 0.333650240 1.8475103186 0.06467321
## 35 -0.3425793592 -0.0348050976 0.368173409 -0.5072313510 0.61199250
## attr(,"call")
## localmoran(x = Kasus, listw = WLJ3)
## attr(,"class")
## [1] "localmoran" "matrix" "array"
## attr(,"quadr")
## mean median pysal
## 1 High-Low High-Low High-Low
## 2 High-Low High-Low High-Low
## 3 High-High High-High High-Low
## 4 Low-Low Low-Low Low-Low
## 5 High-High High-High High-High
## 6 Low-Low Low-Low Low-Low
## 7 Low-Low Low-Low Low-Low
## 8 High-High High-High High-Low
## 9 Low-Low High-Low Low-Low
## 10 High-Low High-Low High-Low
## 11 Low-Low Low-Low Low-Low
## 12 Low-High High-High Low-High
## 13 Low-High Low-High Low-Low
## 14 Low-Low Low-Low Low-Low
## 15 Low-High Low-High Low-High
## 16 High-High High-High High-High
## 17 High-High High-High High-High
## 18 High-Low High-Low High-Low
## 19 Low-High Low-High Low-High
## 20 High-Low High-Low High-Low
## 21 Low-High Low-High Low-High
## 22 High-High High-High High-High
## 23 Low-Low Low-Low Low-Low
## 24 High-High High-High High-High
## 25 Low-High Low-High Low-High
## 26 High-Low High-Low High-Low
## 27 High-High High-High High-High
## 28 High-High High-High High-High
## 29 Low-Low Low-Low Low-Low
## 30 Low-Low Low-Low Low-Low
## 31 Low-Low Low-Low Low-Low
## 32 Low-Low Low-Low Low-Low
## 33 Low-High Low-High Low-High
## 34 Low-Low Low-Low Low-Low
## 35 Low-High Low-High Low-High#k=4
moran.test(Kasus,WLJ4)##
## Moran I test under randomisation
##
## data: Kasus
## weights: WLJ4
##
## Moran I statistic standard deviate = 1.7457, p-value = 0.04043
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.15574384 -0.02941176 0.01124917moran.plot(Kasus,WLJ4)
locm<-localmoran(Kasus,WLJ4)
locm## Ii E.Ii Var.Ii Z.Ii Pr(z != E(Ii))
## 1 -0.254809075 -0.1476468625 1.001057802 -0.10710558 0.91470522
## 2 -0.905773054 -0.0591914090 0.442971026 -1.27198265 0.20337928
## 3 -0.236140195 -0.0163158335 0.127667488 -0.61522739 0.53840456
## 4 0.071665947 -0.0006964607 0.005536170 0.97254187 0.33078104
## 5 0.218349624 -0.0149716249 0.117309463 0.68122059 0.49573192
## 6 -0.191426973 -0.0437377150 0.332696694 -0.25604993 0.79791228
## 7 0.072219065 -0.0043599745 0.034530404 0.41210614 0.68026163
## 8 0.232479841 -0.0221903113 0.172596943 0.61300144 0.53987538
## 9 0.032219772 -0.0002794180 0.002222022 0.68944296 0.49054456
## 10 -0.179005220 -0.0345149104 0.265074341 -0.28064343 0.77898390
## 11 0.222266137 -0.0010962133 0.008710319 2.39327680 0.01669864
## 12 -0.036456050 -0.0002363011 0.001879223 -0.83551880 0.40342567
## 13 -0.389072778 -0.0128129863 0.100615563 -1.18619265 0.23554623
## 14 0.228854282 -0.0123010611 0.096645699 0.77572113 0.43791366
## 15 0.014108215 -0.0022295497 0.017695513 0.12281773 0.90225144
## 16 0.274492702 -0.0056743985 0.044881134 1.32246816 0.18601231
## 17 0.347701141 -0.0053748008 0.042524302 1.71218043 0.08686342
## 18 -0.924423026 -0.0380983112 0.291508874 -1.64159859 0.10067322
## 19 -0.065544933 -0.0034543887 0.027383172 -0.37521793 0.70749839
## 20 0.547831555 -0.0555865725 0.417587430 0.93378016 0.35041730
## 21 -0.197683707 -0.0349054438 0.267965201 -0.31445406 0.75317620
## 22 0.090073217 -0.0004832593 0.003842251 1.46092080 0.14403718
## 23 0.058624592 -0.0110810466 0.087167953 0.23609647 0.81335782
## 24 0.471566912 -0.0103905688 0.081793448 1.68519230 0.09195148
## 25 0.002344862 -0.0004248781 0.003378276 0.04765313 0.96199269
## 26 -0.034821868 -0.0002013453 0.001601288 -0.86516495 0.38694826
## 27 0.713449251 -0.0137481437 0.107856734 2.21426034 0.02681087
## 28 1.888600512 -0.1639777263 1.090480933 1.96557981 0.04934717
## 29 0.588638055 -0.0169325828 0.132410333 1.66419369 0.09607373
## 30 1.248459955 -0.0789746761 0.578595155 1.74512181 0.08096367
## 31 0.550902369 -0.0405386400 0.309394103 1.06329903 0.28764636
## 32 0.038889104 -0.0701007143 0.518529806 0.15135580 0.87969505
## 33 0.190572446 -0.0406469307 0.310185573 0.41515802 0.67802623
## 34 0.943939275 -0.0314316084 0.242165496 1.98204566 0.04747413
## 35 -0.182057434 -0.0348050976 0.267222636 -0.28485613 0.77575437
## attr(,"call")
## localmoran(x = Kasus, listw = WLJ4)
## attr(,"class")
## [1] "localmoran" "matrix" "array"
## attr(,"quadr")
## mean median pysal
## 1 High-Low High-Low High-Low
## 2 High-Low High-Low High-Low
## 3 High-Low High-Low High-Low
## 4 Low-Low Low-Low Low-Low
## 5 High-High High-High High-High
## 6 Low-High Low-High Low-High
## 7 Low-Low Low-Low Low-Low
## 8 High-High High-High High-High
## 9 Low-Low High-Low Low-Low
## 10 High-Low High-Low High-Low
## 11 Low-Low Low-Low Low-Low
## 12 Low-High High-High Low-High
## 13 Low-High Low-High Low-High
## 14 Low-Low Low-Low Low-Low
## 15 Low-Low Low-Low Low-Low
## 16 High-High High-High High-High
## 17 High-High High-High High-High
## 18 High-Low High-Low High-Low
## 19 Low-High Low-High Low-High
## 20 High-High High-High High-High
## 21 Low-High Low-High Low-High
## 22 High-High High-High High-High
## 23 Low-Low Low-Low Low-Low
## 24 High-High High-High High-High
## 25 Low-High Low-High Low-Low
## 26 High-Low High-Low High-Low
## 27 High-High High-High High-High
## 28 High-High High-High High-High
## 29 Low-Low Low-Low Low-Low
## 30 Low-Low Low-Low Low-Low
## 31 Low-Low Low-Low Low-Low
## 32 Low-Low Low-High Low-Low
## 33 Low-Low Low-Low Low-Low
## 34 Low-Low Low-Low Low-Low
## 35 Low-High Low-High Low-Highlocm <- localmoran(Kasus, WLJ4)
plot(locm)
Jawa_Tengah_merged$locm <- locm[, 1] # Nilai Local Moran's I
Jawa_Tengah_merged$p_value <- locm[, 5] # P-value dari Local Moran
ggplot(Jawa_Tengah_merged) +
geom_sf(aes(fill = locm)) +
scale_fill_gradient2(low = "#ff8a01", mid = "#78B7D0", high = "#16325B", midpoint = 0) +
labs(title = "Local Moran's I - BGK", fill = "Moran's I") +
theme_minimal()
Variables
y <- data$BGK
x1 <- data$PUS
x2 <- data$AirLayak
x3 <- data$Posyandu
x4 <- data$Imunisasi
x5 <- data$Sanitasi
plot(data)
Model OLS
model_ols <- lm(y ~ x1 + x2 + x3 + x4 + x5)
summary(model_ols)##
## Call:
## lm(formula = y ~ x1 + x2 + x3 + x4 + x5)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2394.9 -492.6 166.0 533.9 2418.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.302e+02 4.118e+03 0.226 0.823
## x1 6.088e-03 5.321e-03 1.144 0.262
## x2 7.174e-01 4.391e+01 0.016 0.987
## x3 6.751e-01 4.248e-01 1.589 0.123
## x4 -6.727e-03 7.079e-02 -0.095 0.925
## x5 -7.812e+00 1.624e+01 -0.481 0.634
##
## Residual standard error: 1066 on 29 degrees of freedom
## Multiple R-squared: 0.422, Adjusted R-squared: 0.3223
## F-statistic: 4.234 on 5 and 29 DF, p-value: 0.005183AICOLS = AIC(model_ols);AICOLS## [1] 594.7626Asumsi OLS
#Heterogenitas Spasial
bptest(model_ols)##
## studentized Breusch-Pagan test
##
## data: model_ols
## BP = 17.361, df = 5, p-value = 0.003863#Multikolinearitas
vif(model_ols)## x1 x2 x3 x4 x5
## 6.766835 1.490332 1.917312 6.276981 1.148656#Normalitas Residual
shapiro.test(model_ols$residuals)##
## Shapiro-Wilk normality test
##
## data: model_ols$residuals
## W = 0.98402, p-value = 0.8795Optimal Bandwidth
#Optimal Bandwidth Gaussian Fixed
bandwGF <- gwr.sel(y ~ x1 + x2 + x3 + x4 + x5, data = data, coord=Coordk, gweight=gwr.Gauss);bandwGF## Bandwidth: 1.108584 CV score: 63624917
## Bandwidth: 1.791936 CV score: 62230306
## Bandwidth: 2.214271 CV score: 62012117
## Bandwidth: 2.190487 CV score: 62020031
## Bandwidth: 2.475288 CV score: 61944805
## Bandwidth: 2.636605 CV score: 61916208
## Bandwidth: 2.736305 CV score: 61901939
## Bandwidth: 2.797923 CV score: 61894165
## Bandwidth: 2.836005 CV score: 61889712
## Bandwidth: 2.859541 CV score: 61887084
## Bandwidth: 2.874087 CV score: 61885505
## Bandwidth: 2.883077 CV score: 61884545
## Bandwidth: 2.888633 CV score: 61883959
## Bandwidth: 2.892066 CV score: 61883599
## Bandwidth: 2.894189 CV score: 61883377
## Bandwidth: 2.8955 CV score: 61883240
## Bandwidth: 2.896311 CV score: 61883156
## Bandwidth: 2.896812 CV score: 61883104
## Bandwidth: 2.897122 CV score: 61883072
## Bandwidth: 2.897313 CV score: 61883052
## Bandwidth: 2.897431 CV score: 61883039
## Bandwidth: 2.897504 CV score: 61883032
## Bandwidth: 2.897549 CV score: 61883027
## Bandwidth: 2.897549 CV score: 61883027## Warning in gwr.sel(y ~ x1 + x2 + x3 + x4 + x5, data = data, coord = Coordk, :
## Bandwidth converged to upper bound:2.89762251611451## [1] 2.897549#Optimal Bandwidth Bisquare Fixed
bandwBF <- gwr.sel(y ~ x1 + x2 + x3 + x4 + x5, data = data, coord=Coordk, gweight=gwr.bisquare);bandwBF## Bandwidth: 1.108584 CV score: 76119623
## Bandwidth: 1.791936 CV score: 64514411
## Bandwidth: 2.214271 CV score: 65097002
## Bandwidth: 1.961571 CV score: 65081091
## Bandwidth: 1.530919 CV score: 61976861
## Bandwidth: 1.369601 CV score: 62046918
## Bandwidth: 1.45929 CV score: 61407026
## Bandwidth: 1.452581 CV score: 61389164
## Bandwidth: 1.43623 CV score: 61381862
## Bandwidth: 1.41078 CV score: 61489114
## Bandwidth: 1.442082 CV score: 61378214
## Bandwidth: 1.442215 CV score: 61378215
## Bandwidth: 1.442123 CV score: 61378214
## Bandwidth: 1.442164 CV score: 61378214
## Bandwidth: 1.442123 CV score: 61378214## [1] 1.442123#Optimal Bandwidth Tricube Fixed
bandwTF <- gwr.sel(y ~ x1 + x2 + x3 + x4 + x5, data = data, coord=Coordk, gweight=gwr.tricube);bandwTF## Bandwidth: 1.108584 CV score: 77815498
## Bandwidth: 1.791936 CV score: 62622019
## Bandwidth: 2.214271 CV score: 64398465
## Bandwidth: 1.915153 CV score: 63641170
## Bandwidth: 1.530919 CV score: 58751204
## Bandwidth: 1.369601 CV score: 60993795
## Bandwidth: 1.552433 CV score: 58943381
## Bandwidth: 1.505915 CV score: 58635728
## Bandwidth: 1.453848 CV score: 58869624
## Bandwidth: 1.498882 CV score: 58627068
## Bandwidth: 1.496576 CV score: 58626710
## Bandwidth: 1.497056 CV score: 58626681
## Bandwidth: 1.497096 CV score: 58626681
## Bandwidth: 1.497015 CV score: 58626682
## Bandwidth: 1.497056 CV score: 58626681## [1] 1.497056#Optimal Bandwidth Gaussian Adaptive
bandwGA <- gwr.sel(y ~ x1 + x2 + x3 + x4 + x5, data = data, coord=Coordk, gweight=gwr.Gauss,adapt = TRUE);bandwGA## Adaptive q: 0.381966 CV score: 69601832
## Adaptive q: 0.618034 CV score: 65126523
## Adaptive q: 0.763932 CV score: 64248645
## Adaptive q: 0.7797852 CV score: 64068826
## Adaptive q: 0.8638997 CV score: 63384107
## Adaptive q: 0.9488944 CV score: 62829300
## Adaptive q: 0.9164293 CV score: 62876136
## Adaptive q: 0.9401136 CV score: 62836174
## Adaptive q: 0.9585542 CV score: 62826524
## Adaptive q: 0.959071 CV score: 62826396
## Adaptive q: 0.9747045 CV score: 62791228
## Adaptive q: 0.9843665 CV score: 62701902
## Adaptive q: 0.990338 CV score: 62651439
## Adaptive q: 0.9940285 CV score: 62621884
## Adaptive q: 0.9963094 CV score: 62604203
## Adaptive q: 0.9977191 CV score: 62593491
## Adaptive q: 0.9985903 CV score: 62586951
## Adaptive q: 0.9991288 CV score: 62582940
## Adaptive q: 0.9994616 CV score: 62580472
## Adaptive q: 0.9996672 CV score: 62578951
## Adaptive q: 0.9997943 CV score: 62578013
## Adaptive q: 0.9998729 CV score: 62577434
## Adaptive q: 0.9999214 CV score: 62577076
## Adaptive q: 0.9999214 CV score: 62577076## Warning in gwr.sel(y ~ x1 + x2 + x3 + x4 + x5, data = data, coord = Coordk, :
## Bandwidth converged to upper bound:1## [1] 0.9999214#Optimal Bandwidth Bisquare Adaptive
bandwBA <- gwr.sel(y ~ x1 + x2 + x3 + x4 + x5, data = data, coord=Coordk, gweight=gwr.bisquare, adapt = TRUE);bandwBA## Adaptive q: 0.381966 CV score: 180970336
## Adaptive q: 0.618034 CV score: 104141509
## Adaptive q: 0.763932 CV score: 87229125
## Adaptive q: 0.7966399 CV score: 85575231
## Adaptive q: 0.8493819 CV score: 79834729
## Adaptive q: 0.9069129 CV score: 73083877
## Adaptive q: 0.942469 CV score: 70729865
## Adaptive q: 0.9849492 CV score: 68654202
## Adaptive q: 0.9687232 CV score: 70092473
## Adaptive q: 0.9787514 CV score: 69228449
## Adaptive q: 0.9906981 CV score: 68203707
## Adaptive q: 0.9942511 CV score: 67958741
## Adaptive q: 0.996447 CV score: 67818517
## Adaptive q: 0.9978041 CV score: 67735774
## Adaptive q: 0.9986429 CV score: 67686054
## Adaptive q: 0.9991612 CV score: 67655851
## Adaptive q: 0.9994816 CV score: 67637380
## Adaptive q: 0.9996796 CV score: 67626038
## Adaptive q: 0.999802 CV score: 67619057
## Adaptive q: 0.9998776 CV score: 67614753
## Adaptive q: 0.9999244 CV score: 67612097
## Adaptive q: 0.9999244 CV score: 67612097## Warning in gwr.sel(y ~ x1 + x2 + x3 + x4 + x5, data = data, coord = Coordk, :
## Bandwidth converged to upper bound:1## [1] 0.9999244#Optimal Bandwidth Tricube Adaptive
bandwTA <- gwr.sel(y ~ x1 + x2 + x3 + x4 + x5, data = data, coord=Coordk, gweight=gwr.tricube, adapt = TRUE);bandwTA## Adaptive q: 0.381966 CV score: 192533317
## Adaptive q: 0.618034 CV score: 109301712
## Adaptive q: 0.763932 CV score: 89480937
## Adaptive q: 0.8107024 CV score: 86055271
## Adaptive q: 0.8830077 CV score: 77971492
## Adaptive q: 0.9276948 CV score: 71544057
## Adaptive q: 0.9553129 CV score: 70130831
## Adaptive q: 0.9614682 CV score: 69922617
## Adaptive q: 0.9761861 CV score: 68938771
## Adaptive q: 0.9852822 CV score: 67783754
## Adaptive q: 0.9909039 CV score: 67190272
## Adaptive q: 0.9943783 CV score: 66866035
## Adaptive q: 0.9965256 CV score: 66680812
## Adaptive q: 0.9978527 CV score: 66571848
## Adaptive q: 0.9986729 CV score: 66506539
## Adaptive q: 0.9991798 CV score: 66466936
## Adaptive q: 0.9994931 CV score: 66442747
## Adaptive q: 0.9996867 CV score: 66427906
## Adaptive q: 0.9998064 CV score: 66418774
## Adaptive q: 0.9998803 CV score: 66413147
## Adaptive q: 0.999926 CV score: 66409675
## Adaptive q: 0.999926 CV score: 66409675## Warning in gwr.sel(y ~ x1 + x2 + x3 + x4 + x5, data = data, coord = Coordk, :
## Bandwidth converged to upper bound:1## [1] 0.999926Coord <- coordinates(Jawa_Tengah)
longitude <- Coord[, 1]
latitude <- Coord[, 2]Model GWR Adaptive Gaussian Kernel
GWRAG <- gwr(
y ~ x1 + x2 + x3 + x4 + x5,
data = data,
coords = Coordk,
adapt = bandwGA,
hatmatrix = TRUE,
se.fit = TRUE,
gweight=gwr.Gauss
)
summary(GWRAG)## Length Class Mode
## SDF 35 SpatialPointsDataFrame S4
## lhat 1225 -none- numeric
## lm 11 -none- list
## results 14 -none- list
## bandwidth 35 -none- numeric
## adapt 1 -none- numeric
## hatmatrix 1 -none- logical
## gweight 1 -none- character
## gTSS 1 -none- numeric
## this.call 8 -none- call
## fp.given 1 -none- logical
## timings 12 -none- numericGWRAG## Call:
## gwr(formula = y ~ x1 + x2 + x3 + x4 + x5, data = data, coords = Coordk,
## gweight = gwr.Gauss, adapt = bandwGA, hatmatrix = TRUE, se.fit = TRUE)
## Kernel function: gwr.Gauss
## Adaptive quantile: 0.9999214 (about 34 of 35 data points)
## Summary of GWR coefficient estimates at data points:
## Min. 1st Qu. Median 3rd Qu. Max.
## X.Intercept. 1.0401e+03 1.2697e+03 1.4441e+03 1.6300e+03 2.0350e+03
## x1 4.7988e-03 5.1092e-03 5.5524e-03 6.6472e-03 7.1194e-03
## x2 -6.4907e+00 -4.1174e+00 -3.0754e+00 -1.4797e+00 9.6358e-01
## x3 6.0450e-01 6.2311e-01 6.8695e-01 7.2770e-01 7.4599e-01
## x4 -1.1926e-02 -8.9032e-03 -6.5605e-03 -2.3171e-03 7.8395e-04
## x5 -1.1656e+01 -1.0245e+01 -9.5943e+00 -8.8998e+00 -7.9759e+00
## Global
## X.Intercept. 930.1634
## x1 0.0061
## x2 0.7174
## x3 0.6751
## x4 -0.0067
## x5 -7.8117
## Number of data points: 35
## Effective number of parameters (residual: 2traceS - traceS'S): 7.374169
## Effective degrees of freedom (residual: 2traceS - traceS'S): 27.62583
## Sigma (residual: 2traceS - traceS'S): 1057.08
## Effective number of parameters (model: traceS): 6.732155
## Effective degrees of freedom (model: traceS): 28.26785
## Sigma (model: traceS): 1045.007
## Sigma (ML): 939.1426
## AICc (GWR p. 61, eq 2.33; p. 96, eq. 4.21): 599.0785
## AIC (GWR p. 96, eq. 4.22): 585.2056
## Residual sum of squares: 30869609
## Quasi-global R2: 0.4585534Model GWR Adaptive Bisquare Kernel
GWRAB <- gwr(
y ~ x1 + x2 + x3 + x4 + x5,
data = data,
coords = Coordk,
adapt = bandwBA,
hatmatrix = TRUE,
se.fit = TRUE,
gweight=gwr.bisquare
)
summary(GWRAB)## Length Class Mode
## SDF 35 SpatialPointsDataFrame S4
## lhat 1225 -none- numeric
## lm 11 -none- list
## results 14 -none- list
## bandwidth 35 -none- numeric
## adapt 1 -none- numeric
## hatmatrix 1 -none- logical
## gweight 1 -none- character
## gTSS 1 -none- numeric
## this.call 8 -none- call
## fp.given 1 -none- logical
## timings 12 -none- numericGWRAB## Call:
## gwr(formula = y ~ x1 + x2 + x3 + x4 + x5, data = data, coords = Coordk,
## gweight = gwr.bisquare, adapt = bandwBA, hatmatrix = TRUE,
## se.fit = TRUE)
## Kernel function: gwr.bisquare
## Adaptive quantile: 0.9999244 (about 34 of 35 data points)
## Summary of GWR coefficient estimates at data points:
## Min. 1st Qu. Median 3rd Qu. Max.
## X.Intercept. 6.4715e+02 2.2135e+03 2.8020e+03 3.7885e+03 6.1860e+03
## x1 8.1597e-04 2.4262e-03 3.9481e-03 8.7973e-03 1.1881e-02
## x2 -2.5280e+01 -1.9636e+01 -1.3136e+01 -2.9446e+00 9.1241e+00
## x3 2.5005e-01 4.2461e-01 7.8036e-01 8.5303e-01 9.6496e-01
## x4 -3.0687e-02 -1.8700e-02 -6.9226e-03 1.0769e-02 2.9885e-02
## x5 -3.7919e+01 -2.2360e+01 -1.5849e+01 -1.2635e+01 -7.9186e+00
## Global
## X.Intercept. 930.1634
## x1 0.0061
## x2 0.7174
## x3 0.6751
## x4 -0.0067
## x5 -7.8117
## Number of data points: 35
## Effective number of parameters (residual: 2traceS - traceS'S): 11.51139
## Effective degrees of freedom (residual: 2traceS - traceS'S): 23.48861
## Sigma (residual: 2traceS - traceS'S): 1010.114
## Effective number of parameters (model: traceS): 9.643204
## Effective degrees of freedom (model: traceS): 25.3568
## Sigma (model: traceS): 972.1911
## Sigma (ML): 827.4939
## AICc (GWR p. 61, eq 2.33; p. 96, eq. 4.21): 601.5114
## AIC (GWR p. 96, eq. 4.22): 579.257
## Residual sum of squares: 23966116
## Quasi-global R2: 0.5796392Model GWR Adaptive Tricube Kernel
GWRAT <- gwr(
y ~ x1 + x2 + x3 + x4 + x5,
data = data,
coords = Coordk,
adapt = bandwTA,
hatmatrix = TRUE,
se.fit = TRUE,
gweight=gwr.tricube
)
summary(GWRAT)## Length Class Mode
## SDF 35 SpatialPointsDataFrame S4
## lhat 1225 -none- numeric
## lm 11 -none- list
## results 14 -none- list
## bandwidth 35 -none- numeric
## adapt 1 -none- numeric
## hatmatrix 1 -none- logical
## gweight 1 -none- character
## gTSS 1 -none- numeric
## this.call 8 -none- call
## fp.given 1 -none- logical
## timings 12 -none- numericGWRAT## Call:
## gwr(formula = y ~ x1 + x2 + x3 + x4 + x5, data = data, coords = Coordk,
## gweight = gwr.tricube, adapt = bandwTA, hatmatrix = TRUE,
## se.fit = TRUE)
## Kernel function: gwr.tricube
## Adaptive quantile: 0.999926 (about 34 of 35 data points)
## Summary of GWR coefficient estimates at data points:
## Min. 1st Qu. Median 3rd Qu. Max.
## X.Intercept. 1.0259e+03 2.5145e+03 2.9220e+03 3.9393e+03 6.3527e+03
## x1 9.9890e-04 2.3462e-03 3.4737e-03 8.5698e-03 1.1531e-02
## x2 -2.5506e+01 -1.8027e+01 -1.6148e+01 -4.6792e+00 6.5769e+00
## x3 2.5433e-01 4.0959e-01 7.7707e-01 8.6117e-01 9.5483e-01
## x4 -2.6369e-02 -1.3926e-02 -4.8489e-03 8.0554e-03 2.7157e-02
## x5 -3.7756e+01 -2.1337e+01 -1.6304e+01 -1.3699e+01 -9.1834e+00
## Global
## X.Intercept. 930.1634
## x1 0.0061
## x2 0.7174
## x3 0.6751
## x4 -0.0067
## x5 -7.8117
## Number of data points: 35
## Effective number of parameters (residual: 2traceS - traceS'S): 10.34204
## Effective degrees of freedom (residual: 2traceS - traceS'S): 24.65796
## Sigma (residual: 2traceS - traceS'S): 1009.93
## Effective number of parameters (model: traceS): 9.010387
## Effective degrees of freedom (model: traceS): 25.98961
## Sigma (model: traceS): 983.7165
## Sigma (ML): 847.6876
## AICc (GWR p. 61, eq 2.33; p. 96, eq. 4.21): 600.5112
## AIC (GWR p. 96, eq. 4.22): 580.3119
## Residual sum of squares: 25150100
## Quasi-global R2: 0.5588724Model GWR Fixed Gaussian Kernel
GWRFG <- gwr(
y ~ x1 + x2 + x3 + x4 + x5,
data = data,
coords = Coordk,
bandwidth = bandwGF,
hatmatrix = TRUE,
se.fit = TRUE,
gweight=gwr.Gauss
)
summary(GWRFG)## Length Class Mode
## SDF 35 SpatialPointsDataFrame S4
## lhat 1225 -none- numeric
## lm 11 -none- list
## results 14 -none- list
## bandwidth 1 -none- numeric
## adapt 0 -none- NULL
## hatmatrix 1 -none- logical
## gweight 1 -none- character
## gTSS 1 -none- numeric
## this.call 8 -none- call
## fp.given 1 -none- logical
## timings 12 -none- numericGWRFG## Call:
## gwr(formula = y ~ x1 + x2 + x3 + x4 + x5, data = data, coords = Coordk,
## bandwidth = bandwGF, gweight = gwr.Gauss, hatmatrix = TRUE,
## se.fit = TRUE)
## Kernel function: gwr.Gauss
## Fixed bandwidth: 2.897549
## Summary of GWR coefficient estimates at data points:
## Min. 1st Qu. Median 3rd Qu. Max.
## X.Intercept. 1.0328e+03 1.1405e+03 1.1678e+03 1.2035e+03 1.2406e+03
## x1 5.2896e-03 5.5970e-03 5.8828e-03 6.3252e-03 6.8280e-03
## x2 -2.0923e+00 -1.4166e+00 -1.0248e+00 -5.6427e-01 8.5212e-01
## x3 6.1522e-01 6.5276e-01 6.7769e-01 7.0159e-01 7.3590e-01
## x4 -9.6754e-03 -7.3713e-03 -6.5758e-03 -5.2452e-03 -2.1755e-03
## x5 -9.0235e+00 -8.6872e+00 -8.5457e+00 -8.3087e+00 -7.9599e+00
## Global
## X.Intercept. 930.1634
## x1 0.0061
## x2 0.7174
## x3 0.6751
## x4 -0.0067
## x5 -7.8117
## Number of data points: 35
## Effective number of parameters (residual: 2traceS - traceS'S): 6.693524
## Effective degrees of freedom (residual: 2traceS - traceS'S): 28.30648
## Sigma (residual: 2traceS - traceS'S): 1063.682
## Effective number of parameters (model: traceS): 6.360341
## Effective degrees of freedom (model: traceS): 28.63966
## Sigma (model: traceS): 1057.477
## Sigma (ML): 956.5789
## AICc (GWR p. 61, eq 2.33; p. 96, eq. 4.21): 599.1016
## AIC (GWR p. 96, eq. 4.22): 586.1215
## Residual sum of squares: 32026511
## Quasi-global R2: 0.4382616Model GWR Fixed Bisquare Kernel
GWRFB <- gwr(
y ~ x1 + x2 + x3 + x4 + x5,
data = data,
coords = Coordk,
bandwidth = bandwBF,
hatmatrix = TRUE,
se.fit = TRUE,
gweight=gwr.bisquare
)
summary(GWRFB)## Length Class Mode
## SDF 35 SpatialPointsDataFrame S4
## lhat 1225 -none- numeric
## lm 11 -none- list
## results 14 -none- list
## bandwidth 1 -none- numeric
## adapt 0 -none- NULL
## hatmatrix 1 -none- logical
## gweight 1 -none- character
## gTSS 1 -none- numeric
## this.call 8 -none- call
## fp.given 1 -none- logical
## timings 12 -none- numericGWRFB## Call:
## gwr(formula = y ~ x1 + x2 + x3 + x4 + x5, data = data, coords = Coordk,
## bandwidth = bandwBF, gweight = gwr.bisquare, hatmatrix = TRUE,
## se.fit = TRUE)
## Kernel function: gwr.bisquare
## Fixed bandwidth: 1.442123
## Summary of GWR coefficient estimates at data points:
## Min. 1st Qu. Median 3rd Qu. Max.
## X.Intercept. -1.0958e+04 3.8050e+02 2.9746e+03 4.4589e+03 6.4651e+03
## x1 -3.2487e-03 1.6266e-03 3.8174e-03 1.0855e-02 2.2453e-02
## x2 -3.3314e+01 -2.3275e+01 -8.8210e+00 9.9479e+00 1.3551e+02
## x3 -4.8599e-01 2.7046e-01 7.4589e-01 8.5237e-01 1.2140e+00
## x4 -9.3078e-02 -3.5775e-02 -6.6957e-03 1.8794e-02 1.0029e-01
## x5 -3.6376e+01 -2.7761e+01 -1.7828e+01 -1.4928e+01 1.9864e+00
## Global
## X.Intercept. 930.1634
## x1 0.0061
## x2 0.7174
## x3 0.6751
## x4 -0.0067
## x5 -7.8117
## Number of data points: 35
## Effective number of parameters (residual: 2traceS - traceS'S): 15.27084
## Effective degrees of freedom (residual: 2traceS - traceS'S): 19.72916
## Sigma (residual: 2traceS - traceS'S): 1010.602
## Effective number of parameters (model: traceS): 12.70298
## Effective degrees of freedom (model: traceS): 22.29702
## Sigma (model: traceS): 950.6284
## Sigma (ML): 758.7526
## AICc (GWR p. 61, eq 2.33; p. 96, eq. 4.21): 610.8016
## AIC (GWR p. 96, eq. 4.22): 576.246
## Residual sum of squares: 20149695
## Quasi-global R2: 0.6465785Model GWR Fixed Tricube Kernel
GWRFT <- gwr(
y ~ x1 + x2 + x3 + x4 + x5,
data = data,
coords = Coordk,
bandwidth = bandwTF,
hatmatrix = TRUE,
se.fit = TRUE,
gweight=gwr.tricube
)
summary(GWRFT)## Length Class Mode
## SDF 35 SpatialPointsDataFrame S4
## lhat 1225 -none- numeric
## lm 11 -none- list
## results 14 -none- list
## bandwidth 1 -none- numeric
## adapt 0 -none- NULL
## hatmatrix 1 -none- logical
## gweight 1 -none- character
## gTSS 1 -none- numeric
## this.call 8 -none- call
## fp.given 1 -none- logical
## timings 12 -none- numericGWRFT## Call:
## gwr(formula = y ~ x1 + x2 + x3 + x4 + x5, data = data, coords = Coordk,
## bandwidth = bandwTF, gweight = gwr.tricube, hatmatrix = TRUE,
## se.fit = TRUE)
## Kernel function: gwr.tricube
## Fixed bandwidth: 1.497056
## Summary of GWR coefficient estimates at data points:
## Min. 1st Qu. Median 3rd Qu. Max.
## X.Intercept. -1.0535e+04 5.3765e+02 3.2107e+03 4.2905e+03 6.2823e+03
## x1 -3.0995e-03 1.7288e-03 3.9394e-03 1.0438e-02 2.2661e-02
## x2 -3.2048e+01 -2.2886e+01 -1.3102e+01 7.8847e+00 1.2982e+02
## x3 -4.5529e-01 2.6490e-01 7.5332e-01 8.6873e-01 1.2209e+00
## x4 -9.9035e-02 -3.1754e-02 -3.0543e-03 1.3884e-02 1.0162e-01
## x5 -3.3864e+01 -2.5136e+01 -1.7941e+01 -1.5111e+01 1.8074e+00
## Global
## X.Intercept. 930.1634
## x1 0.0061
## x2 0.7174
## x3 0.6751
## x4 -0.0067
## x5 -7.8117
## Number of data points: 35
## Effective number of parameters (residual: 2traceS - traceS'S): 13.45519
## Effective degrees of freedom (residual: 2traceS - traceS'S): 21.54481
## Sigma (residual: 2traceS - traceS'S): 1001.162
## Effective number of parameters (model: traceS): 11.57405
## Effective degrees of freedom (model: traceS): 23.42595
## Sigma (model: traceS): 960.1236
## Sigma (ML): 785.4919
## AICc (GWR p. 61, eq 2.33; p. 96, eq. 4.21): 607.0477
## AIC (GWR p. 96, eq. 4.22): 577.5415
## Residual sum of squares: 21594914
## Quasi-global R2: 0.6212296aicc_results = data.frame(Bobot = c("Adaptive Gaussian",
"Adaptive Bisquare",
"Adaptive Tricube",
"Fixed Gaussian",
"Fixed Bisquare",
"Fixed Tricube"),
AICc = c(GWRAG$results$AICc,
GWRAB$results$AICc,
GWRAT$results$AICc,
GWRFG$results$AICc,
GWRFB$results$AICc,
GWRFT$results$AICc),
CV = c(0.9999214,
0.9999244,
0.999926,
2.897549,
1.442123,
1.497056))
aicc_results## Bobot AICc CV
## 1 Adaptive Gaussian 599.4138 0.9999214
## 2 Adaptive Bisquare 603.5932 0.9999244
## 3 Adaptive Tricube 601.8240 0.9999260
## 4 Fixed Gaussian 599.2577 2.8975490
## 5 Fixed Bisquare 616.2404 1.4421230
## 6 Fixed Tricube 610.3318 1.4970560Identifikasi Variabel yang Signifikan
## Ekstrak koefisien lokal untuk variabel xi
coef_x1 = GWRFB$SDF$x1
coef_x2 = GWRFB$SDF$x2
coef_x3 = GWRFB$SDF$x3
coef_x4 = GWRFB$SDF$x4
coef_x5 = GWRFB$SDF$x5
## Ekstrak standar error untuk variabel xi (untuk tiap provinsi)
se_x1 = GWRFB$SDF$x1_se
se_x2 = GWRFB$SDF$x2_se
se_x3 = GWRFB$SDF$x3_se
se_x4 = GWRFB$SDF$x4_se
se_x5 = GWRFB$SDF$x5_se
## t-value untuk variabel xi (untuk tiap provinsi)
t_x1 = coef_x1/se_x1
t_x2 = coef_x2/se_x2
t_x3 = coef_x3/se_x3
t_x4 = coef_x4/se_x4
t_x5 = coef_x5/se_x5
## p-values untuk variabel xi (untuk tiap provinsi)
n = nrow(data)
k = length(coef(GWRFB$lm))
df = n-k-1
# p-values for each variable at each location
p_values1 <- 2 * (1 - pt(abs(t_x1), df))
p_values2 <- 2 * (1 - pt(abs(t_x2), df))
p_values3 <- 2 * (1 - pt(abs(t_x3), df))
p_values4 <- 2 * (1 - pt(abs(t_x4), df))
p_values5 <- 2 * (1 - pt(abs(t_x5), df))
# Display p-values
prov_id = rep(1:35, times=5)
var_id = rep(1:5, each = 35)
p_values = data.frame(ID = prov_id,
Variabel_X_ke = var_id,
p_value = c(p_values1, p_values2, p_values3, p_values4, p_values5))
p_values## ID Variabel_X_ke p_value
## 1 1 1 0.06966540
## 2 2 1 0.02318257
## 3 3 1 0.08981377
## 4 4 1 0.89103230
## 5 5 1 0.79280548
## 6 6 1 0.03606566
## 7 7 1 0.05095055
## 8 8 1 0.50750983
## 9 9 1 0.73557199
## 10 10 1 0.34910300
## 11 11 1 0.84903767
## 12 12 1 0.12635819
## 13 13 1 0.30393330
## 14 14 1 0.94218582
## 15 15 1 0.59823644
## 16 16 1 0.02527693
## 17 17 1 0.46440597
## 18 18 1 0.01460818
## 19 19 1 0.50371411
## 20 20 1 0.63440548
## 21 21 1 0.51951933
## 22 22 1 0.02914747
## 23 23 1 0.01452000
## 24 24 1 0.02609004
## 25 25 1 0.47626583
## 26 26 1 0.96613762
## 27 27 1 0.68258404
## 28 28 1 0.63888932
## 29 29 1 0.94272710
## 30 30 1 0.93798840
## 31 31 1 0.96831683
## 32 32 1 0.01406440
## 33 33 1 0.39272589
## 34 34 1 0.56673359
## 35 35 1 0.24529182
## 36 1 2 0.50778571
## 37 2 2 0.84107878
## 38 3 2 0.53911245
## 39 4 2 0.13983390
## 40 5 2 0.97477703
## 41 6 2 0.78899985
## 42 7 2 0.99088453
## 43 8 2 0.83246592
## 44 9 2 0.57231131
## 45 10 2 0.36438496
## 46 11 2 0.67105412
## 47 12 2 0.49094801
## 48 13 2 0.64714612
## 49 14 2 0.86630678
## 50 15 2 0.57772590
## 51 16 2 0.55459754
## 52 17 2 0.83385207
## 53 18 2 0.70928542
## 54 19 2 0.43946303
## 55 20 2 0.58991542
## 56 21 2 0.22536236
## 57 22 2 0.55055056
## 58 23 2 0.65543213
## 59 24 2 0.64089791
## 60 25 2 0.43821156
## 61 26 2 0.08912888
## 62 27 2 0.84130076
## 63 28 2 0.83558302
## 64 29 2 0.63215485
## 65 30 2 0.91763440
## 66 31 2 0.88658044
## 67 32 2 0.75614936
## 68 33 2 0.58055853
## 69 34 2 0.83817065
## 70 35 2 0.47590072
## 71 1 3 0.58103328
## 72 2 3 0.86386461
## 73 3 3 0.25911808
## 74 4 3 0.03099479
## 75 5 3 0.06859521
## 76 6 3 0.52451289
## 77 7 3 0.59790835
## 78 8 3 0.04856819
## 79 9 3 0.04970052
## 80 10 3 0.05736719
## 81 11 3 0.10623546
## 82 12 3 0.58736388
## 83 13 3 0.07473834
## 84 14 3 0.10572561
## 85 15 3 0.09367652
## 86 16 3 0.55806996
## 87 17 3 0.05204609
## 88 18 3 0.57819526
## 89 19 3 0.04689105
## 90 20 3 0.09142936
## 91 21 3 0.04348020
## 92 22 3 0.66292470
## 93 23 3 0.95593521
## 94 24 3 0.91607665
## 95 25 3 0.24501624
## 96 26 3 0.05245728
## 97 27 3 0.06286372
## 98 28 3 0.05937223
## 99 29 3 0.06731973
## 100 30 3 0.12048465
## 101 31 3 0.09164498
## 102 32 3 0.68201875
## 103 33 3 0.09471136
## 104 34 3 0.24939470
## 105 35 3 0.26136335
## 106 1 4 0.83661531
## 107 2 4 0.55720067
## 108 3 4 0.66657596
## 109 4 4 0.70514192
## 110 5 4 0.79356183
## 111 6 4 0.58828972
## 112 7 4 0.70309745
## 113 8 4 0.74426599
## 114 9 4 0.96040500
## 115 10 4 0.42448427
## 116 11 4 0.44290445
## 117 12 4 0.96003054
## 118 13 4 0.73756974
## 119 14 4 0.59459015
## 120 15 4 0.88800990
## 121 16 4 0.49774765
## 122 17 4 0.79010744
## 123 18 4 0.33847367
## 124 19 4 0.67243695
## 125 20 4 0.86300703
## 126 21 4 0.67118073
## 127 22 4 0.59427319
## 128 23 4 0.43094278
## 129 24 4 0.62948557
## 130 25 4 0.77493270
## 131 26 4 0.91899291
## 132 27 4 0.90913980
## 133 28 4 0.96718584
## 134 29 4 0.67451814
## 135 30 4 0.49171603
## 136 31 4 0.59932181
## 137 32 4 0.35050658
## 138 33 4 0.91983910
## 139 34 4 0.21384632
## 140 35 4 0.99750521
## 141 1 5 0.35485255
## 142 2 5 0.77263047
## 143 3 5 0.25524498
## 144 4 5 0.50250441
## 145 5 5 0.16925305
## 146 6 5 0.96582509
## 147 7 5 0.93620686
## 148 8 5 0.23566199
## 149 9 5 0.43040614
## 150 10 5 0.45232646
## 151 11 5 0.40959688
## 152 12 5 0.34756014
## 153 13 5 0.10446436
## 154 14 5 0.13634745
## 155 15 5 0.09114950
## 156 16 5 0.44685143
## 157 17 5 0.10465778
## 158 18 5 0.81721102
## 159 19 5 0.47086455
## 160 20 5 0.08932978
## 161 21 5 0.56615837
## 162 22 5 0.44319853
## 163 23 5 0.61288817
## 164 24 5 0.56594484
## 165 25 5 0.14911298
## 166 26 5 0.44497782
## 167 27 5 0.11643301
## 168 28 5 0.11318486
## 169 29 5 0.41056552
## 170 30 5 0.25235222
## 171 31 5 0.26116558
## 172 32 5 0.78667205
## 173 33 5 0.10512363
## 174 34 5 0.36549842
## 175 35 5 0.18696422Koefisien pada setiap lokasi
coef_gwr <- GWRFB$SDF@data[, c("x1", "x2","x3","x4","x5")];coef_gwr## x1 x2 x3 x4 x5
## 1 0.0096343905 -28.4968207 0.27042297 -0.0136791667 -17.549296
## 2 0.0168595175 -11.0519435 -0.10901566 -0.0461729899 -6.132143
## 3 0.0087399717 -25.9458255 0.48846301 -0.0285287500 -22.407462
## 4 -0.0010187137 93.3829801 1.18180462 0.0420050176 -15.381128
## 5 0.0013988235 -1.3919626 0.83163713 0.0182234673 -25.436413
## 6 0.0220361738 -19.2858237 -0.48599216 -0.0686713393 -1.085918
## 7 0.0206955772 -0.8570436 -0.38115172 -0.0516422718 1.986378
## 8 0.0035359009 9.2379892 0.94782211 -0.0230070910 -22.477880
## 9 0.0018544629 26.5613588 0.96288553 0.0036504928 -15.801663
## 10 0.0053970477 44.1805424 1.02895092 -0.0626863638 -16.501893
## 11 -0.0010562590 21.0113545 0.83005881 0.0582006254 -16.209199
## 12 0.0082858668 -29.8956647 0.28681377 0.0033694869 -18.157848
## 13 0.0052596883 -18.9699003 0.76676530 -0.0223124335 -32.611194
## 14 0.0003902200 -7.6380677 0.76119075 0.0375015662 -29.283099
## 15 0.0027531897 -23.5746108 0.73779216 0.0094980485 -36.049000
## 16 0.0124247809 -26.7307815 0.27049269 -0.0460827075 -15.088427
## 17 0.0038173577 -8.7895416 0.86974546 -0.0180693453 -29.918755
## 18 0.0224533100 -24.4257647 -0.40563654 -0.0930775894 -5.534786
## 19 0.0037523933 37.0991266 1.02096047 -0.0319778536 -15.233735
## 20 0.0024932073 -22.9087254 0.74588699 0.0116714019 -36.376380
## 21 0.0039247192 65.6639054 1.08451329 -0.0357133688 -12.741762
## 22 0.0120762574 -26.7087353 0.21029501 -0.0358363664 -14.766712
## 23 0.0160069195 -22.6422854 -0.03125749 -0.0564296382 -10.242261
## 24 0.0135338611 -22.1490322 0.05886385 -0.0333070298 -11.223577
## 25 0.0037485696 -33.3140519 0.55590333 0.0193641444 -30.950516
## 26 -0.0005682506 135.5097724 1.21395049 0.0219171955 -19.847216
## 27 0.0021562444 -8.5655117 0.82601401 0.0078041536 -29.245003
## 28 0.0024687348 -8.8210405 0.83499326 0.0028054307 -29.348077
## 29 0.0003950092 22.8573638 0.89872249 0.0310764539 -16.173845
## 30 -0.0004245156 4.8877260 0.76801243 0.0502062348 -21.510847
## 31 0.0002153670 6.5979852 0.81047634 0.0378909331 -21.017597
## 32 0.0204403934 -18.7382031 -0.27537703 -0.0798245577 -6.057230
## 33 0.0043831798 -22.9747724 0.71656784 -0.0066957196 -33.120011
## 34 -0.0032486589 10.6577831 0.68273333 0.1002867970 -17.827706
## 35 0.0059490561 -29.8752393 0.50540876 -0.0002073464 -26.277920Pengujian Asumsi GWR
residual_gwr <- GWRFB$SDF$gwr.e
#Normalitas Residual
shapiro.test(residual_gwr)##
## Shapiro-Wilk normality test
##
## data: residual_gwr
## W = 0.93092, p-value = 0.02979#Autokorelasi Spasial
moran.test(residual_gwr, WLJ4)##
## Moran I test under randomisation
##
## data: residual_gwr
## weights: WLJ4
##
## Moran I statistic standard deviate = 1.9721, p-value = 0.0243
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.16943682 -0.02941176 0.01016703#Breusch Pagan
bptest(residual_gwr ~ GWRFB$SDF$x1+GWRFB$SDF$x2+GWRFB$SDF$x3+GWRFB$SDF$x4++GWRFB$SDF$x5)##
## studentized Breusch-Pagan test
##
## data: residual_gwr ~ GWRFB$SDF$x1 + GWRFB$SDF$x2 + GWRFB$SDF$x3 + GWRFB$SDF$x4 + +GWRFB$SDF$x5
## BP = 4.8211, df = 5, p-value = 0.4381Membuat PLot Splot
coef_x1 <- GWRFB$SDF$x1
coef_x2 <- GWRFB$SDF$x2
coef_x3 <- GWRFB$SDF$x3
coef_x4 <- GWRFB$SDF$x4
coef_x5 <- GWRFB$SDF$x5spplot(GWRFB$SDF, "x1", main = "Koefisien GWR untuk x1", col.regions = terrain.colors(100))
spplot(GWRFB$SDF, "x2", main = "Koefisien GWR untuk x2", col.regions = terrain.colors(100))
spplot(GWRFB$SDF, "x3", main = "Koefisien GWR untuk x1", col.regions = terrain.colors(100))
Membuat plot -gglot2
gwr_df <- as.data.frame(GWRFB$SDF)
ggplot(gwr_df, aes(x = longitude, y = latitude)) +
geom_point(aes(color = x1), size = 2) +
scale_color_viridis_c() +
labs(title = "Koefisien GWR untuk x1", color = "Koefisien") +
theme_minimal()
ggplot(gwr_df, aes(x = longitude, y = latitude)) +
geom_point(aes(color = x2), size = 2) +
scale_color_viridis_c() +
labs(title = "Koefisien GWR untuk x2", color = "Koefisien") +
theme_minimal()
ggplot(gwr_df, aes(x = longitude, y = latitude)) +
geom_point(aes(color = x3), size = 2) +
scale_color_viridis_c() +
labs(title = "Koefisien GWR untuk x3", color = "Koefisien") +
theme_minimal()
Membuat Surface Plot
x.range <- range(longitude)
y.range <- range(latitude)grd <- expand.grid(
longitude = seq(from = x.range[1], to = x.range[2], length.out = 100),
latitude = seq(from = y.range[1], to = y.range[2], length.out = 100)
)
coordinates(grd) <- ~longitude + latitude
proj4string(grd) <- CRS("+proj=longlat +datum=WGS84 +no_defs")residual_gwr = GWRFB$SDF$gwr.e
library(dplyr)
library(broom)## Warning: package 'broom' was built under R version 4.3.3names(GWRFB$SDF)## [1] "sum.w" "(Intercept)" "x1"
## [4] "x2" "x3" "x4"
## [7] "x5" "(Intercept)_se" "x1_se"
## [10] "x2_se" "x3_se" "x4_se"
## [13] "x5_se" "gwr.e" "pred"
## [16] "pred.se" "localR2" "(Intercept)_se_EDF"
## [19] "x1_se_EDF" "x2_se_EDF" "x3_se_EDF"
## [22] "x4_se_EDF" "x5_se_EDF" "pred.se"names(Jawa_Tengah_merged)## [1] "GID_0" "NAME_0" "GID_1" "NAME_1" "NL_NAME_1"
## [6] "GID_2" "NAME_2" "VARNAME_2" "NL_NAME_2" "TYPE_2"
## [11] "ENGTYPE_2" "CC_2" "HASC_2" "id" "Kota"
## [16] "BGK" "PUS" "AirLayak" "Posyandu" "Imunisasi"
## [21] "Sanitasi" "geometry" "BGK_Discrete" "locm" "p_value"gwr_results <- data.frame(
id = Jawa_Tengah_merged$id,
intercept = GWRFB$SDF$`(Intercept)`,
x1 = GWRFB$SDF$x1,
x2 = GWRFB$SDF$x2,
x3 = GWRFB$SDF$x3,
x4 = GWRFB$SDF$x4,
x5 = GWRFB$SDF$x5,
intercept_se = GWRFB$SDF$`(Intercept)_se`,
x1_se = GWRFB$SDF$x1_se,
x2_se = GWRFB$SDF$x2_se,
x3_se = GWRFB$SDF$x3_se,
x4_se = GWRFB$SDF$x4_se,
x5_se = GWRFB$SDF$x5_se
)
length(Jawa_Tengah_merged$id)## [1] 35length(GWRFB$SDF$Intercept)## [1] 0num_params <- length(coef(GWRFB))
## Pemetaan GWR
gwr_results$t_intercept <- gwr_results$intercept / gwr_results$intercept_se
gwr_results$p_intercept <- 2 * (1 - pt(abs(gwr_results$t_intercept), df = nrow(Jawa_Tengah_merged) - num_params))
gwr_results$t_x1 <- gwr_results$x1 / gwr_results$x1_se
gwr_results$p_x1 <- 2 * (1 - pt(abs(gwr_results$t_x1), df = nrow(Jawa_Tengah_merged) - num_params))
gwr_results$t_x2 <- gwr_results$x2 / gwr_results$x2_se
gwr_results$p_x2 <- 2 * (1 - pt(abs(gwr_results$t_x2), df = nrow(Jawa_Tengah_merged) - num_params))
gwr_results$t_x3 <- gwr_results$x3 / gwr_results$x3_se
gwr_results$p_x3 <- 2 * (1 - pt(abs(gwr_results$t_x3), df = nrow(Jawa_Tengah_merged) - num_params))
gwr_results$t_x4 <- gwr_results$x4 / gwr_results$x4_se
gwr_results$p_x4 <- 2 * (1 - pt(abs(gwr_results$t_x4), df = nrow(Jawa_Tengah_merged) - num_params))
gwr_results$t_x5 <- gwr_results$x5 / gwr_results$x5_se
gwr_results$p_x6 <- 2 * (1 - pt(abs(gwr_results$t_x5), df = nrow(Jawa_Tengah_merged) - num_params))
gwr_results$significant_intercept <- ifelse(gwr_results$p_intercept < 0.05, "Significant", "Not Significant")
gwr_results$significant_x1 <- ifelse(gwr_results$p_x1 < 0.05, "Significant", "Not Significant")
gwr_results$significant_x2 <- ifelse(gwr_results$p_x2 < 0.05, "Significant", "Not Significant")
gwr_results$significant_x3 <- ifelse(gwr_results$p_x3 < 0.05, "Significant", "Not Significant")
gwr_results$significant_x4 <- ifelse(gwr_results$p_x2 < 0.05, "Significant", "Not Significant")
gwr_results$significant_x5 <- ifelse(gwr_results$p_x3 < 0.05, "Significant", "Not Significant")
Jawa_Tengah_combined <- Jawa_Tengah_merged %>% left_join(gwr_results, by = "id")
#Membuat peta untuk intercept
ggplot(data = Jawa_Tengah_combined) +
geom_sf(aes(fill = significant_intercept)) +
scale_fill_manual(values = c("Significant" = "green", "Not Significant" = "red")) +
labs(title = "Significance of Intercept in GWR Model for Jawa_Tengah") +
theme_minimal()
#Membuat peta untuk variabel x1
ggplot(data = Jawa_Tengah_combined) +
geom_sf(aes(fill = significant_x1)) +
scale_fill_manual(values = c("Significant" = "green", "Not Significant" = "red")) +
labs(title = "Significance of x1 in GWR Model for Jawa_Tengah") +
theme_minimal()
#Membuat peta untuk variabel x2
ggplot(data = Jawa_Tengah_combined) +
geom_sf(aes(fill = significant_x2)) +
scale_fill_manual(values = c("Significant" = "green", "Not Significant" = "red")) +
labs(title = "Significance of x2 in GWR Model for Jawa_Tengah") +
theme_minimal()
#Membuat peta untuk variabel x3
ggplot(data = Jawa_Tengah_combined) +
geom_sf(aes(fill = significant_x3)) +
scale_fill_manual(values = c("Significant" = "green", "Not Significant" = "red")) +
labs(title = "Significance of x3 in GWR Model for Jawa_Tengah") +
theme_minimal()
#Membuat peta untuk variabel x4
ggplot(data = Jawa_Tengah_combined) +
geom_sf(aes(fill = significant_x4)) +
scale_fill_manual(values = c("Significant" = "green", "Not Significant" = "red")) +
labs(title = "Significance of x in GWR Model for Jawa_Tengah") +
theme_minimal()
#Membuat peta untuk variabel x4
ggplot(data = Jawa_Tengah_combined) +
geom_sf(aes(fill = significant_x5)) +
scale_fill_manual(values = c("Significant" = "green", "Not Significant" = "red")) +
labs(title = "Significance of x5 in GWR Model for Jawa_Tengah") +
theme_minimal()
# Menggabungkan variabel signifikan menjadi satu kolom
gwr_results <- gwr_results %>%
mutate(
variabel_signifikan = paste(
significant_x1, significant_x2, significant_x3,significant_x4, significant_x5, sep = ", "
)
) %>%
# Hapus NA dalam hasil variabel_signifikan
mutate(variabel_signifikan = gsub("NA, |, NA", "", variabel_signifikan)) %>%
mutate(variabel_signifikan = ifelse(variabel_signifikan == "NA", "Tidak ada", variabel_signifikan))
gwr_results <- cbind(gwr_results, data$Kota)
# Membuat tabel akhir
tabel_hasil <- gwr_results %>%
dplyr::select(V2, variabel_signifikan)
tabel_hasil## V2
## <char>
## 1: Cilacap
## 2: Banyumas
## 3: Purbalingga
## 4: Banjarnegara
## 5: Kebumen
## 6: Purworejo
## 7: Wonosobo
## 8: Magelang
## 9: Boyolali
## 10: Klaten
## 11: Sukoharjo
## 12: Wonogiri
## 13: Karanganyar
## 14: Sragen
## 15: Grobogan
## 16: Blora
## 17: Rembang
## 18: Pati
## 19: Kudus
## 20: Jepara
## 21: Demak
## 22: Semarang
## 23: Temanggung
## 24: Kendal
## 25: Batang
## 26: Pekalongan
## 27: Pemalang
## 28: Tegal
## 29: Brebes
## 30: Kota Magelang
## 31: Kota Surakarta
## 32: Kota Salatiga
## 33: Kota Semarang
## 34: Kota Pekalongan
## 35: Kota Tegal
## V2
## variabel_signifikan
## <char>
## 1: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 2: Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 3: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 4: Not Significant, Not Significant, Significant, Not Significant, Significant
## 5: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 6: Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 7: Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 8: Not Significant, Not Significant, Significant, Not Significant, Significant
## 9: Not Significant, Not Significant, Significant, Not Significant, Significant
## 10: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 11: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 12: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 13: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 14: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 15: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 16: Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 17: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 18: Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 19: Not Significant, Not Significant, Significant, Not Significant, Significant
## 20: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 21: Not Significant, Not Significant, Significant, Not Significant, Significant
## 22: Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 23: Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 24: Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 25: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 26: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 27: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 28: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 29: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 30: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 31: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 32: Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 33: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 34: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## 35: Not Significant, Not Significant, Not Significant, Not Significant, Not Significant
## variabel_signifikan