Library
library(rsample)
## Warning: package 'rsample' was built under R version 4.3.2
library(DataExplorer)
## Warning: package 'DataExplorer' was built under R version 4.3.1
library(sjPlot)
## Warning: package 'sjPlot' was built under R version 4.3.2
## Learn more about sjPlot with 'browseVignettes("sjPlot")'.
library(lmtest)
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
library(openxlsx)
library(readxl)
library(plm)
## Warning: package 'plm' was built under R version 4.3.1
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plm':
##
## between, lag, lead
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(raster)
## Loading required package: sp
##
## Attaching package: 'raster'
## The following object is masked from 'package:dplyr':
##
## select
library(GWmodel)
## Warning: package 'GWmodel' was built under R version 4.3.1
## Loading required package: maptools
## Warning: package 'maptools' was built under R version 4.3.1
## Please note that 'maptools' will be retired during October 2023,
## plan transition at your earliest convenience (see
## https://r-spatial.org/r/2023/05/15/evolution4.html and earlier blogs
## for guidance);some functionality will be moved to 'sp'.
## Checking rgeos availability: FALSE
## Loading required package: robustbase
## Loading required package: Rcpp
##
## Attaching package: 'Rcpp'
## The following object is masked from 'package:rsample':
##
## populate
## Loading required package: spatialreg
## Loading required package: spData
## To access larger datasets in this package, install the spDataLarge
## package with: `install.packages('spDataLarge',
## repos='https://nowosad.github.io/drat/', type='source')`
## Loading required package: Matrix
## Loading required package: sf
## Linking to GEOS 3.11.1, GDAL 3.6.2, PROJ 9.1.1; sf_use_s2() is TRUE
## Welcome to GWmodel version 2.2-9.
library(spdep)
##
## Attaching package: 'spdep'
## The following objects are masked from 'package:spatialreg':
##
## get.ClusterOption, get.coresOption, get.mcOption,
## get.VerboseOption, get.ZeroPolicyOption, set.ClusterOption,
## set.coresOption, set.mcOption, set.VerboseOption,
## set.ZeroPolicyOption
library(sp)
library(ggplot2)
library(ggrepel)
## Warning: package 'ggrepel' was built under R version 4.3.1
Data
df<-read_excel("D:/Kuliah/Skripsi/Proses/Data Penelitian Kedungsepur.xlsx", sheet = "Mix")
tipologi<-read_excel("D:/Kuliah/Skripsi/Proses/Data Penelitian Kedungsepur.xlsx", sheet = "Tipologi_Klassen")
head(df)
## # A tibble: 6 × 14
## Kab Tahun Perkapita TPAK UHH APM_SMA Jalan B_Modal Penduduk Penduduk2
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Kabupate… 2017 12.9 72.2 74.5 53.6 597. 6.03e8 0.56 0.501
## 2 Kabupate… 2018 13.6 70.8 74.6 48.8 662. 4.23e8 0.469 0.469
## 3 Kabupate… 2019 14.4 69.2 74.6 49.7 700. 4.49e8 0.450 0.450
## 4 Kabupate… 2020 13.4 69.8 74.8 50.3 724. 3.67e8 5.50 5.50
## 5 Kabupate… 2021 13.8 72.9 74.8 50.3 537. 2.90e8 0.824 0.824
## 6 Kabupate… 2022 14.5 72.0 74.9 50.2 803. 4.62e8 2.43 2.43
## # ℹ 4 more variables: TPT <dbl>, RLS <dbl>, PAD <dbl>, Pop <dbl>
spas<-read_excel("D:/Kuliah/Skripsi/Proses/Untuk Spasial.xlsx")
head(spas)
## # A tibble: 6 × 9
## Kab koor_X koor_Y Y.2017 Y.2018 Y.2019 Y.2020 Y.2021 Y.2022
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Grobogan 111. -7.08 12.9 13.6 14.4 13.4 13.8 14.5
## 2 Demak 111. -6.89 14.5 15.2 15.9 15.3 15.6 16.2
## 3 Semarang 110. -7.14 31.2 32.5 34.0 33 33.9 35.4
## 4 Kendal 110. -6.93 28.9 30.3 32.0 30.0 30.9 32.4
## 5 Salatiga 111. -7.33 45.6 47.6 50.0 49.5 50.8 53.1
## 6 Kota Semarang 110. -7.01 70.1 73.5 77.6 83.2 87.4 92.2
Konvergensi Beta Absolut
beta_abs<-pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) | lag(log(Perkapita),2:5), data = df, effect = "individual", model = "onestep", transformation = "d")
## Warning in pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) |
## lag(log(Perkapita), : the second-step matrix is singular, a general inverse is
## used
hasil_betaabs<-summary(beta_abs)
## Warning in vcovHC.pgmm(object): a general inverse is used
hasil_betaabs
## Oneway (individual) effect One-step model Difference GMM
##
## Call:
## pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) | lag(log(Perkapita),
## 2:5), data = df, effect = "individual", model = "onestep",
## transformation = "d")
##
## Balanced Panel: n = 6, T = 6, N = 36
##
## Number of Observations Used: 24
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.106175 0.013059 0.023333 0.009156 0.031709 0.077257
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(log(Perkapita)) 0.65155 0.19237 3.387 0.0007067 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(9) = 6 (p-value = 0.73992)
## Autocorrelation test (1): normal = -2.261701 (p-value = 0.023716)
## Autocorrelation test (2): normal = -1.090421 (p-value = 0.27553)
## Wald test for coefficients: chisq(1) = 11.47164 (p-value = 0.00070666)
Konvergensi Sigma
th_2017<-df%>%filter(Tahun==2017)
th_2018<-df%>%filter(Tahun==2018)
th_2019<-df%>%filter(Tahun==2019)
th_2020<-df%>%filter(Tahun==2020)
th_2021<-df%>%filter(Tahun==2021)
th_2022<-df%>%filter(Tahun==2022)
coef.v<-function(x){
sd(x)/mean(x)
}
kapita<-cbind(th_2017$Perkapita, th_2018$Perkapita, th_2019$Perkapita, th_2020$Perkapita, th_2021$Perkapita, th_2022$Perkapita)
hasil<-apply(kapita,2, coef.v)
data.frame(hasil)
## hasil
## 1 0.6327042
## 2 0.6327681
## 3 0.6354291
## 4 0.6962686
## 5 0.7080072
## 6 0.7121494
absis<-c(2017:2022)
konv_sigma<-cbind(absis,hasil)
ggplot(data.frame(konv_sigma), aes(absis,hasil))+geom_line(color = "steelblue", size = 2, linetype = 1)+labs(x = "Tahun", y = "Koefisien Variasi PDRB per Kapita")+theme_classic()+theme_classic()+geom_point(color = "steelblue", size = 4)+ylim(0.4,0.8)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Konvergensi Beta Kondisional
beta_kond<-pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) + TPT + RLS + log(Jalan) + log(B_Modal) | lag(log(Perkapita),2:5), data = df, effect = "individual", model = "twosteps", transformation = "d")
## Warning in pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) + TPT + RLS + :
## the second-step matrix is singular, a general inverse is used
summary(beta_kond)
## Warning in vcovHC.pgmm(object): a general inverse is used
## Oneway (individual) effect Two-steps model Difference GMM
##
## Call:
## pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) + TPT + RLS +
## log(Jalan) + log(B_Modal) | lag(log(Perkapita), 2:5), data = df,
## effect = "individual", model = "twosteps", transformation = "d")
##
## Balanced Panel: n = 6, T = 6, N = 36
##
## Number of Observations Used: 24
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.109317 -0.020834 0.009369 -0.001540 0.024204 0.071120
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(log(Perkapita)) 0.38673967 0.19632665 1.9699 0.04885 *
## TPT -0.00081507 0.00343708 -0.2371 0.81255
## RLS 0.13506110 0.05679198 2.3782 0.01740 *
## log(Jalan) -0.06548987 0.01249063 -5.2431 1.579e-07 ***
## log(B_Modal) 0.02534520 0.04542690 0.5579 0.57689
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(9) = 2.406028 (p-value = 0.9833)
## Autocorrelation test (1): normal = -0.9723791 (p-value = 0.33086)
## Autocorrelation test (2): normal = 0.2784977 (p-value = 0.78063)
## Wald test for coefficients: chisq(5) = 188.3352 (p-value = < 2.22e-16)
beta_kond1<-pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) + TPAK + RLS + log(Jalan) + log(PAD) + log(Penduduk) | lag(log(Perkapita),2:5), data = df, effect = "individual", model = "twosteps", transformation = "d")
## Warning in get(.Generic)(x, ...): NaNs produced
## Warning in pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) + TPAK + : the
## second-step matrix is singular, a general inverse is used
summary(beta_kond1)
## Warning in vcovHC.pgmm(object): a general inverse is used
## Oneway (individual) effect Two-steps model Difference GMM
##
## Call:
## pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) + TPAK +
## RLS + log(Jalan) + log(PAD) + log(Penduduk) | lag(log(Perkapita),
## 2:5), data = df, effect = "individual", model = "twosteps",
## transformation = "d")
##
## Balanced Panel: n = 6, T = 6, N = 36
##
## Number of Observations Used: 22
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.108515 -0.021180 0.000000 0.002133 0.047256 0.136449
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(log(Perkapita)) 0.71569813 0.28771209 2.4875 0.012863 *
## TPAK -0.00027157 0.00326552 -0.0832 0.933723
## RLS 0.15316193 0.03741034 4.0941 4.238e-05 ***
## log(Jalan) -0.16636583 0.07616459 -2.1843 0.028941 *
## log(PAD) -0.37797574 0.17606344 -2.1468 0.031808 *
## log(Penduduk) -0.05733333 0.01802192 -3.1813 0.001466 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(9) = -1.694308e-14 (p-value = 1)
## Autocorrelation test (1): normal = -1.417319 (p-value = 0.15639)
## Autocorrelation test (2): normal = -0.09798819 (p-value = 0.92194)
## Wald test for coefficients: chisq(6) = 451.8795 (p-value = < 2.22e-16)
beta_kond2<-pgmm(formula = Perkapita ~ lag(Perkapita) + TPT + APM_SMA + log(Jalan) + log(PAD) + Penduduk | lag(Perkapita,2:5), data = df, effect = "individual", model = "twosteps", transformation = "d")
## Warning in pgmm(formula = Perkapita ~ lag(Perkapita) + TPT + APM_SMA +
## log(Jalan) + : the second-step matrix is singular, a general inverse is used
summary(beta_kond2)
## Warning in vcovHC.pgmm(object): a general inverse is used
## Oneway (individual) effect Two-steps model Difference GMM
##
## Call:
## pgmm(formula = Perkapita ~ lag(Perkapita) + TPT + APM_SMA + log(Jalan) +
## log(PAD) + Penduduk | lag(Perkapita, 2:5), data = df, effect = "individual",
## model = "twosteps", transformation = "d")
##
## Balanced Panel: n = 6, T = 6, N = 36
##
## Number of Observations Used: 24
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.089453 -0.787216 0.175919 0.002156 0.677130 3.430970
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(Perkapita) 1.10455 0.11711 9.4316 < 2e-16 ***
## TPT -0.61738 0.52998 -1.1649 0.24405
## APM_SMA 0.20067 0.16622 1.2072 0.22734
## log(Jalan) 0.46005 2.90282 0.1585 0.87408
## log(PAD) 2.15837 5.23770 0.4121 0.68028
## Penduduk -0.38094 0.14552 -2.6178 0.00885 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(9) = 7.293026e-16 (p-value = 1)
## Autocorrelation test (1): normal = -1.1002 (p-value = 0.27125)
## Autocorrelation test (2): normal = -1.078665 (p-value = 0.28074)
## Wald test for coefficients: chisq(6) = 2370.822 (p-value = < 2.22e-16)
beta_kond3<-pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) + TPT + APM_SMA + log(Jalan) + log(B_Modal) + log(Pop) | lag(log(Perkapita),2:5), data = df, effect = "individual", model = "onestep", transformation = "d")
## Warning in pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) + TPT + APM_SMA
## + : the second-step matrix is singular, a general inverse is used
summary(beta_kond3)
## Warning in vcovHC.pgmm(object): a general inverse is used
## Oneway (individual) effect One-step model Difference GMM
##
## Call:
## pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) + TPT + APM_SMA +
## log(Jalan) + log(B_Modal) + log(Pop) | lag(log(Perkapita),
## 2:5), data = df, effect = "individual", model = "onestep",
## transformation = "d")
##
## Balanced Panel: n = 6, T = 6, N = 36
##
## Number of Observations Used: 24
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.076001 -0.009788 0.012606 0.007274 0.035970 0.074233
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(log(Perkapita)) 0.7220891 0.1514193 4.7688 1.853e-06 ***
## TPT -0.0099624 0.0048137 -2.0696 0.038491 *
## APM_SMA 0.0206435 0.0075782 2.7241 0.006448 **
## log(Jalan) -0.0469333 0.0231477 -2.0276 0.042605 *
## log(B_Modal) 0.0120234 0.0148276 0.8109 0.417432
## log(Pop) -0.6104167 0.1958606 -3.1166 0.001830 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(9) = 6 (p-value = 0.73992)
## Autocorrelation test (1): normal = -1.72813 (p-value = 0.083965)
## Autocorrelation test (2): normal = -0.3295996 (p-value = 0.7417)
## Wald test for coefficients: chisq(6) = 47804357287 (p-value = < 2.22e-16)
#used
beta_kond4<-pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) + TPT + RLS + log(Jalan) + log(PAD) + log(Pop) | lag(log(Perkapita),2:5), data = df, effect = "individual", model = "onestep", transformation = "d")
## Warning in pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) + TPT + RLS + :
## the second-step matrix is singular, a general inverse is used
summary(beta_kond4)
## Warning in vcovHC.pgmm(object): a general inverse is used
## Oneway (individual) effect One-step model Difference GMM
##
## Call:
## pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) + TPT + RLS +
## log(Jalan) + log(PAD) + log(Pop) | lag(log(Perkapita), 2:5),
## data = df, effect = "individual", model = "onestep", transformation = "d")
##
## Balanced Panel: n = 6, T = 6, N = 36
##
## Number of Observations Used: 24
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.049842 -0.013536 0.001241 0.004627 0.025371 0.055739
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(log(Perkapita)) 0.3354926 0.1141577 2.9389 0.0032943 **
## TPT -0.0094963 0.0039892 -2.3805 0.0172888 *
## RLS 0.1552862 0.0428298 3.6257 0.0002882 ***
## log(Jalan) -0.0393359 0.0175230 -2.2448 0.0247800 *
## log(PAD) 0.0525238 0.0453136 1.1591 0.2464086
## log(Pop) -1.1025692 0.2667011 -4.1341 3.563e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(9) = 6 (p-value = 0.73992)
## Autocorrelation test (1): normal = -1.65149 (p-value = 0.098639)
## Autocorrelation test (2): normal = -1.31262 (p-value = 0.18931)
## Wald test for coefficients: chisq(6) = 156037866984 (p-value = < 2.22e-16)
beta_kond5<-pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) + TPT + UHH + log(Jalan) + log(B_Modal) + log(Pop) | lag(log(Perkapita),2:5), data = df, effect = "individual", model = "onestep", transformation = "d")
## Warning in pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) + TPT + UHH + :
## the second-step matrix is singular, a general inverse is used
summary(beta_kond5)
## Warning in vcovHC.pgmm(object): a general inverse is used
## Oneway (individual) effect One-step model Difference GMM
##
## Call:
## pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) + TPT + UHH +
## log(Jalan) + log(B_Modal) + log(Pop) | lag(log(Perkapita),
## 2:5), data = df, effect = "individual", model = "onestep",
## transformation = "d")
##
## Balanced Panel: n = 6, T = 6, N = 36
##
## Number of Observations Used: 24
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.063750 -0.006829 0.006232 0.005193 0.021734 0.055810
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(log(Perkapita)) 0.2005428 0.0952021 2.1065 0.0351613 *
## TPT -0.0080818 0.0027110 -2.9811 0.0028723 **
## UHH 0.2593844 0.0539367 4.8091 1.516e-06 ***
## log(Jalan) -0.0466807 0.0165201 -2.8257 0.0047178 **
## log(B_Modal) 0.0309465 0.0178288 1.7358 0.0826070 .
## log(Pop) -0.7408489 0.2088507 -3.5473 0.0003893 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(9) = 6 (p-value = 0.73992)
## Autocorrelation test (1): normal = -1.736184 (p-value = 0.082531)
## Autocorrelation test (2): normal = 0.5091246 (p-value = 0.61066)
## Wald test for coefficients: chisq(6) = 45711014767 (p-value = < 2.22e-16)
beta_kond6<-pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) + TPAK + APM_SMA + log(Jalan) + log(B_Modal) + log(Pop) | lag(log(Perkapita),2:5), data = df, effect = "individual", model = "onestep", transformation = "d")
## Warning in pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) + TPAK + : the
## second-step matrix is singular, a general inverse is used
summary(beta_kond6)
## Warning in vcovHC.pgmm(object): a general inverse is used
## Oneway (individual) effect One-step model Difference GMM
##
## Call:
## pgmm(formula = log(Perkapita) ~ lag(log(Perkapita)) + TPAK +
## APM_SMA + log(Jalan) + log(B_Modal) + log(Pop) | lag(log(Perkapita),
## 2:5), data = df, effect = "individual", model = "onestep",
## transformation = "d")
##
## Balanced Panel: n = 6, T = 6, N = 36
##
## Number of Observations Used: 24
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.085510 -0.021720 0.021293 0.008125 0.035250 0.056287
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(log(Perkapita)) 0.5419092 0.1224288 4.4263 9.585e-06 ***
## TPAK 0.0022953 0.0035790 0.6413 0.5213
## APM_SMA 0.0134020 0.0069381 1.9317 0.0534 .
## log(Jalan) -0.0554194 0.0139432 -3.9747 7.048e-05 ***
## log(B_Modal) 0.0138707 0.0169750 0.8171 0.4139
## log(Pop) -0.4645985 0.2039076 -2.2785 0.0227 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(9) = 6 (p-value = 0.73992)
## Autocorrelation test (1): normal = -1.987902 (p-value = 0.046823)
## Autocorrelation test (2): normal = -0.4859579 (p-value = 0.627)
## Wald test for coefficients: chisq(6) = 5.957979e+12 (p-value = < 2.22e-16)
Uji Morans I
spas.2017 = spas$Y.2017
spas.2018 = spas$Y.2018
spas.2019 = spas$Y.2019
spas.2020 = spas$Y.2020
spas.2021 = spas$Y.2021
spas.2022 = spas$Y.2022
longlat = cbind(spas$koor_X, spas$koor_Y)
#1/Jarak
w1<-as.matrix(1/dist(longlat))
dlist1 = mat2listw(w1, style = "W")
#kNN
k1 = knn2nb(knearneigh(longlat, k = 1))
W<-nb2mat(k1)
dlist2 = mat2listw(W, style = "W")
spKedungsepur = shapefile("D:/Kuliah/Skripsi/Proses/Peta/Kedungsepur.shp")
queen.nb=poly2nb(spKedungsepur)
#Pembobot queen
queen.kedungsepur=nb2listw(queen.nb,style="W",zero.policy=TRUE)
#Menyimpan Matriks Pembobot
bobot.queen = listw2mat(queen.kedungsepur)
bobot.queen = mat2listw(bobot.queen)
## Warning in mat2listw(bobot.queen): style is M (missing); style should be set to
## a valid value
Dengan Bobot Queen
moran.test(spas.2022, queen.kedungsepur, randomisation = F, zero.policy = T)
##
## Moran I test under normality
##
## data: spas.2022
## weights: queen.kedungsepur
##
## Moran I statistic standard deviate = 0.32644, p-value = 0.372
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.12639092 -0.20000000 0.05084656
moran.test(spas.2021, queen.kedungsepur, randomisation = F, zero.policy = T)
##
## Moran I test under normality
##
## data: spas.2021
## weights: queen.kedungsepur
##
## Moran I statistic standard deviate = 0.34884, p-value = 0.3636
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.12133894 -0.20000000 0.05084656
moran.test(spas.2020, queen.kedungsepur, randomisation = F, zero.policy = T)
##
## Moran I test under normality
##
## data: spas.2020
## weights: queen.kedungsepur
##
## Moran I statistic standard deviate = 0.4101, p-value = 0.3409
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.10752499 -0.20000000 0.05084656
moran.test(spas.2019, queen.kedungsepur, randomisation = F, zero.policy = T)
##
## Moran I test under normality
##
## data: spas.2019
## weights: queen.kedungsepur
##
## Moran I statistic standard deviate = 0.59458, p-value = 0.2761
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.06592791 -0.20000000 0.05084656
moran.test(spas.2018, queen.kedungsepur, randomisation = F, zero.policy = T)
##
## Moran I test under normality
##
## data: spas.2018
## weights: queen.kedungsepur
##
## Moran I statistic standard deviate = 0.61717, p-value = 0.2686
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.06083221 -0.20000000 0.05084656
moran.test(spas.2017, queen.kedungsepur, randomisation = F, zero.policy = T)
##
## Moran I test under normality
##
## data: spas.2017
## weights: queen.kedungsepur
##
## Moran I statistic standard deviate = 0.63358, p-value = 0.2632
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.05713311 -0.20000000 0.05084656
Dengan Bobot k-NN
moran.test(spas.2022, dlist2, randomisation = F, zero.policy = T)
##
## Moran I test under normality
##
## data: spas.2022
## weights: dlist2
##
## Moran I statistic standard deviate = -0.55493, p-value = 0.7105
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.3937518 -0.2000000 0.1219048
moran.test(spas.2021, dlist2, randomisation = F, zero.policy = T)
##
## Moran I test under normality
##
## data: spas.2021
## weights: dlist2
##
## Moran I statistic standard deviate = -0.53598, p-value = 0.704
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.3871384 -0.2000000 0.1219048
moran.test(spas.2020, dlist2, randomisation = F, zero.policy = T)
##
## Moran I test under normality
##
## data: spas.2020
## weights: dlist2
##
## Moran I statistic standard deviate = -0.50824, p-value = 0.6944
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.3774524 -0.2000000 0.1219048
moran.test(spas.2019, dlist2, randomisation = F, zero.policy = T)
##
## Moran I test under normality
##
## data: spas.2019
## weights: dlist2
##
## Moran I statistic standard deviate = -0.34222, p-value = 0.6339
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.3194868 -0.2000000 0.1219048
moran.test(spas.2018, dlist2, randomisation = F, zero.policy = T)
##
## Moran I test under normality
##
## data: spas.2018
## weights: dlist2
##
## Moran I statistic standard deviate = -0.32032, p-value = 0.6256
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.3118390 -0.2000000 0.1219048
moran.test(spas.2017, dlist2, randomisation = F, zero.policy = T)
##
## Moran I test under normality
##
## data: spas.2017
## weights: dlist2
##
## Moran I statistic standard deviate = -0.30922, p-value = 0.6214
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.3079628 -0.2000000 0.1219048
Dengan Bobot 1/Jarak
moran.test(spas.2022, dlist1, randomisation = F, zero.policy = T)
##
## Moran I test under normality
##
## data: spas.2022
## weights: dlist1
##
## Moran I statistic standard deviate = 0.016467, p-value = 0.4934
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.198699485 -0.200000000 0.006237433
moran.test(spas.2021, dlist1, randomisation = F, zero.policy = T)
##
## Moran I test under normality
##
## data: spas.2021
## weights: dlist1
##
## Moran I statistic standard deviate = 0.03418, p-value = 0.4864
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.197300591 -0.200000000 0.006237433
moran.test(spas.2020, dlist1, randomisation = F, zero.policy = T)
##
## Moran I test under normality
##
## data: spas.2020
## weights: dlist1
##
## Moran I statistic standard deviate = 0.06047, p-value = 0.4759
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.195224227 -0.200000000 0.006237433
moran.test(spas.2019, dlist1, randomisation = F, zero.policy = T)
##
## Moran I test under normality
##
## data: spas.2019
## weights: dlist1
##
## Moran I statistic standard deviate = 0.25539, p-value = 0.3992
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.179829563 -0.200000000 0.006237433
moran.test(spas.2018, dlist1, randomisation = F, zero.policy = T)
##
## Moran I test under normality
##
## data: spas.2018
## weights: dlist1
##
## Moran I statistic standard deviate = 0.27348, p-value = 0.3922
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.178401412 -0.200000000 0.006237433
moran.test(spas.2017, dlist1, randomisation = F, zero.policy = T)
##
## Moran I test under normality
##
## data: spas.2017
## weights: dlist1
##
## Moran I statistic standard deviate = 0.28068, p-value = 0.3895
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.177832850 -0.200000000 0.006237433
Tipologi Klassen
# create empty theme to clear the plot area
empty_theme <- theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
axis.text.y = element_text(angle = 90)
)
# create sample data
tipologi_2017<-cbind(tipologi$Daerah,tipologi$Perkapita_2017,tipologi$PE_2017)
tipologi_2018<-cbind(tipologi$Perkapita_2018,tipologi$PE_2018)
tipologi_2019<-cbind(tipologi$Perkapita_2019,tipologi$PE_2019)
tipologi_2020<-cbind(tipologi$Perkapita_2020,tipologi$PE_2020)
tipologi_2021<-cbind(tipologi$Perkapita_2021,tipologi$PE_2021)
tipologi_2022<-cbind(tipologi$Perkapita_2022,tipologi$PE_2022)
tipologi_2017<-as.data.frame(tipologi_2017)
# create plot
ggplot(tipologi, aes(x = Perkapita_2017, y = PE_2017, label = Daerah)) +
coord_fixed() +
scale_x_continuous(expand = c(0, 0), limits = c(0, 80), breaks = c(12,60),
labels=c("12" = "Low", "60" = "High")) +
scale_y_continuous(expand = c(0, 0), limits = c(-20,20), breaks = c(-8,16),
labels=c("5.5" = "Low", "6.5" = "High")) +
empty_theme +
labs(title = "Tipologi Klassen Kedungsepur Tahun 2017",
x = "PDRB per kapita",
y = "Pertumbuhan Ekonomi") +
geom_vline(xintercept = 35.08) +
geom_hline(yintercept = 6.26) +
geom_point(colour = "black", size = 1) +
geom_label_repel(size = 3,
fill = "deepskyblue",
colour = "black",
min.segment.length = unit(0, "lines"))
# create plot
ggplot(tipologi, aes(x = Perkapita_2022, y = PE_2022, label = Daerah)) +
coord_fixed() +
scale_x_continuous(expand = c(0, 0), limits = c(0, 120), breaks = c(18,80),
labels=c("12" = "Low", "60" = "High")) +
scale_y_continuous(expand = c(0, 0), limits = c(-20,20), breaks = c(-8,16),
labels=c("5.5" = "Low", "6.5" = "High")) +
empty_theme +
labs(title = "Tipologi Klassen Kedungsepur Tahun 2022",
x = "PDRB per kapita",
y = "Pertumbuhan Ekonomi") +
geom_vline(xintercept = 41.47) +
geom_hline(yintercept = 5.65) +
geom_point(colour = "black", size = 1) +
geom_label_repel(size = 3,
fill = "deepskyblue",
colour = "black",
min.segment.length = unit(0, "lines"))
