Take-home_Ex01
Vedant Mehrotra
10/05/2020
RPubs Link: https://rpubs.com/vedant_1997/IS415_Take-home_Ex01
Objective
In view of this, we are going to conduct a use-case to demonstrate the potential contribution of geospatial analytics in R to integrate, analyse and communicate the analysis results by using open data provided by different government agencies. The specific objectives of the study are as follow:
Calibrating a simple linear regression to reveal the relation between public bus commuters’ flows (i.e. tap-in or tap-out) data and residential population at the planning sub-zone level.
Performing spatial autocorrelation analysis on the residual of the regression model to test if the model conforms to the randomization assumption.
Performing localized geospatial statistics analysis by using commuters’ tap-in and tap-out data to identify geographical clustering.
Data Sources
Passenger Volume By Bus Stop (https://www.mytransport.sg/content/mytransport/home/dataMall/dynamic-data.html#Geospatial)
Bus Stop Locations (https://www.mytransport.sg/content/mytransport/home/dataMall/static-data.html#Whole%20Island)
Population data (https://www.singstat.gov.sg/publications/population-trends)
Subzone Boundary (https://data.gov.sg/dataset/master-plan-2014-subzone-boundary-web)
Installation of required R packages
packages = c('rgdal', 'spdep', 'tmap', 'tidyverse', 'sf', 'mgcv')
for (p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p,character.only = T)
}Reading aspatial and geospatial data
bus_stops <- st_read(dsn = "data/geospatial", layer = "BusStop")## Reading layer `BusStop' from data source `D:\Geospatial Analytics\Take_home\Take-home_Ex01\data\geospatial' using driver `ESRI Shapefile'
## Simple feature collection with 5040 features and 3 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 4427.938 ymin: 26482.1 xmax: 48282.5 ymax: 52983.82
## proj4string: +proj=tmerc +lat_0=1.366666666666667 +lon_0=103.8333333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +datum=WGS84 +units=m +no_defssubzones <- st_read(dsn = "data/geospatial",
layer = "MP14_SUBZONE_WEB_PL")## Reading layer `MP14_SUBZONE_WEB_PL' from data source `D:\Geospatial Analytics\Take_home\Take-home_Ex01\data\geospatial' using driver `ESRI Shapefile'
## Simple feature collection with 323 features and 15 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
## proj4string: +proj=tmerc +lat_0=1.366666666666667 +lon_0=103.8333333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +datum=WGS84 +units=m +no_defspassenger_volume <- read_csv("data/aspatial/passenger volume by busstop.csv")## Parsed with column specification:
## cols(
## YEAR_MONTH = col_character(),
## DAY_TYPE = col_character(),
## TIME_PER_HOUR = col_double(),
## PT_TYPE = col_character(),
## PT_CODE = col_character(),
## TOTAL_TAP_IN_VOLUME = col_double(),
## TOTAL_TAP_OUT_VOLUME = col_double()
## )population <- read_csv("data/aspatial/respopagesextod2011to2019.csv")## Parsed with column specification:
## cols(
## PA = col_character(),
## SZ = col_character(),
## AG = col_character(),
## Sex = col_character(),
## TOD = col_character(),
## Pop = col_double(),
## Time = col_double()
## )Data Wrangling
Check that the CRS for the 2 geospatial datas are the same
bus_stops3414 <- st_set_crs(bus_stops, 3414)
subzones3414 <- st_set_crs(subzones, 3414)st_transform(bus_stops3414, 3414)## Simple feature collection with 5040 features and 3 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 4427.938 ymin: 26482.1 xmax: 48282.5 ymax: 52983.82
## CRS: EPSG:3414
## First 10 features:
## BUS_STOP_N BUS_ROOF_N LOC_DESC geometry
## 1 78221 B06 <NA> POINT (42227.96 39563.16)
## 2 63359 B01 HOUGANG SWIM CPLX POINT (34065.75 39047.46)
## 3 64141 B13 AFT JLN TELAWI POINT (36335.3 38525.74)
## 4 83139 B07 AFT JOO CHIAT PL POINT (36530.26 32981.18)
## 5 55231 B02 OPP SBST EAST DISTRICT POINT (29669.93 40841.51)
## 6 55351 B03 OPP FUDU WALK P/G POINT (28404.77 41300.92)
## 7 92089 B10 CHIJ KATONG CON POINT (37378.19 32166.81)
## 8 80271 B06 OPP BLK 2 POINT (33524.15 31938.86)
## 9 82059 B02 ONE AMBER POINT (35142.59 31500.49)
## 10 97089 B02 OPP SELARANG PK DRUG REH. POINT (44329.52 39204.74)st_transform(subzones3414, 3414)## Simple feature collection with 323 features and 15 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
## CRS: EPSG:3414
## First 10 features:
## OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND PLN_AREA_N
## 1 1 1 MARINA SOUTH MSSZ01 Y MARINA SOUTH
## 2 2 1 PEARL'S HILL OTSZ01 Y OUTRAM
## 3 3 3 BOAT QUAY SRSZ03 Y SINGAPORE RIVER
## 4 4 8 HENDERSON HILL BMSZ08 N BUKIT MERAH
## 5 5 3 REDHILL BMSZ03 N BUKIT MERAH
## 6 6 7 ALEXANDRA HILL BMSZ07 N BUKIT MERAH
## 7 7 9 BUKIT HO SWEE BMSZ09 N BUKIT MERAH
## 8 8 2 CLARKE QUAY SRSZ02 Y SINGAPORE RIVER
## 9 9 13 PASIR PANJANG 1 QTSZ13 N QUEENSTOWN
## 10 10 7 QUEENSWAY QTSZ07 N QUEENSTOWN
## PLN_AREA_C REGION_N REGION_C INC_CRC FMEL_UPD_D X_ADDR
## 1 MS CENTRAL REGION CR 5ED7EB253F99252E 2014-12-05 31595.84
## 2 OT CENTRAL REGION CR 8C7149B9EB32EEFC 2014-12-05 28679.06
## 3 SR CENTRAL REGION CR C35FEFF02B13E0E5 2014-12-05 29654.96
## 4 BM CENTRAL REGION CR 3775D82C5DDBEFBD 2014-12-05 26782.83
## 5 BM CENTRAL REGION CR 85D9ABEF0A40678F 2014-12-05 26201.96
## 6 BM CENTRAL REGION CR 9D286521EF5E3B59 2014-12-05 25358.82
## 7 BM CENTRAL REGION CR 7839A8577144EFE2 2014-12-05 27680.06
## 8 SR CENTRAL REGION CR 48661DC0FBA09F7A 2014-12-05 29253.21
## 9 QT CENTRAL REGION CR 1F721290C421BFAB 2014-12-05 22077.34
## 10 QT CENTRAL REGION CR 3580D2AFFBEE914C 2014-12-05 24168.31
## Y_ADDR SHAPE_Leng SHAPE_Area geometry
## 1 29220.19 5267.381 1630379.3 MULTIPOLYGON (((31495.56 30...
## 2 29782.05 3506.107 559816.2 MULTIPOLYGON (((29092.28 30...
## 3 29974.66 1740.926 160807.5 MULTIPOLYGON (((29932.33 29...
## 4 29933.77 3313.625 595428.9 MULTIPOLYGON (((27131.28 30...
## 5 30005.70 2825.594 387429.4 MULTIPOLYGON (((26451.03 30...
## 6 29991.38 4428.913 1030378.8 MULTIPOLYGON (((25899.7 297...
## 7 30230.86 3275.312 551732.0 MULTIPOLYGON (((27746.95 30...
## 8 30222.86 2208.619 290184.7 MULTIPOLYGON (((29351.26 29...
## 9 29893.78 6571.323 1084792.3 MULTIPOLYGON (((20996.49 30...
## 10 30104.18 3454.239 631644.3 MULTIPOLYGON (((24472.11 29...population_2019 <- population %>%
filter(Time == 2019) %>%
mutate(SUBZONE_N = toupper(SZ)) %>%
group_by(SUBZONE_N) %>%
summarize(SUBZONE_POPULATION = sum(Pop))Join the population_2019 and subzones
subzone_population <- left_join(subzones3414, population_2019, by = "SUBZONE_N")passenger_volume_cleaned <- passenger_volume %>%
select(`PT_CODE`, `TOTAL_TAP_IN_VOLUME`, `TOTAL_TAP_OUT_VOLUME`) %>%
group_by(PT_CODE) %>%
summarize(TAP_IN = sum(TOTAL_TAP_IN_VOLUME),
TAP_OUT = sum(TOTAL_TAP_OUT_VOLUME)) bus_stops_cleaned <- bus_stops3414 %>%
select(BUS_STOP_N)Join bus stop location and passenger volume
bus_stop_passengers <- left_join(bus_stops_cleaned, passenger_volume_cleaned, by = c("BUS_STOP_N" = "PT_CODE"))subzone_geom <- subzone_population %>%
select(OBJECTID, SUBZONE_N, SUBZONE_POPULATION)coords <- st_coordinates(bus_stops_cleaned)
x <- coords[,1]
y <- coords[,2]
object <- bus_stops_cleaned$BUS_STOP_N
points <- data.frame(object, x, y)coords1 <- st_coordinates(subzone_geom)
x <- coords1[,1]
y <- coords1[,2]
pol <- coords1[,5]
poly <- data.frame(pol, x, y)x <- split(poly$x, poly$pol)
y <- split(poly$y, poly$pol)
todo <- 1:nrow(points)
Area <- rep.int("", nrow(points))
pol <- names(x)
for (i in 1:length(x)) {
bnd <- cbind(x[[i]], y[[i]])
xy <- with(points, cbind(x[todo], y[todo]))
inbnd <- in.out(bnd, xy)
Area[todo[inbnd]] <- pol[i]
todo <- todo[!inbnd]
}
points$Area <- Areapoints_cleaned <- points %>%
select(object, Area) %>%
rename("BUS_STOP_N" = object,
"OBJECTID" = Area)
bus_stop_passengers_cleaned <- st_set_geometry(bus_stop_passengers, NULL)
bus <- left_join(bus_stop_passengers_cleaned, points_cleaned, by = "BUS_STOP_N")
bus_new <- bus %>%
mutate(OBJECTID = as.numeric(OBJECTID),
BUS_STOP_N = as.numeric(BUS_STOP_N))overall <- left_join(subzone_population, bus_new, by = "OBJECTID")
overall <- overall %>% drop_na()new_overall <- overall %>%
select(SUBZONE_N, SUBZONE_POPULATION, TAP_IN, TAP_OUT) %>%
group_by(SUBZONE_N, SUBZONE_POPULATION) %>%
summarise(TAP_IN = sum(TAP_IN),
TAP_OUT = sum(TAP_OUT))
new_overall <- st_make_valid(new_overall)tmap_mode("plot")## tmap mode set to plottingDistribution of population, tap in, tap out
tm_shape(new_overall) +
tm_fill(col = "SUBZONE_POPULATION",
n = 5,
style = "equal",
title = "POPULATION") +
tm_borders(alpha = 0.5) +
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
tm_shape(new_overall) +
tm_fill(col = "TAP_OUT",
n = 5,
style = "equal") +
tm_borders(alpha = 0.5) +
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
tm_shape(new_overall) +
tm_fill(col = "TAP_OUT",
n = 5,
style = "equal") +
tm_borders(alpha = 0.5) +
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
#Linear Regression for Tap In vs Population
Tap In(x) vs Population(y)
The Linear Regression is significant as p-value < 0.05
stat <- lm(TAP_IN ~ SUBZONE_POPULATION, new_overall)
plot(stat)
summary(stat)##
## Call:
## lm(formula = TAP_IN ~ SUBZONE_POPULATION, data = new_overall)
##
## Residuals:
## Min 1Q Median 3Q Max
## -865527 -124146 -64237 39142 1949703
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.254e+05 2.110e+04 5.944 7.64e-09 ***
## SUBZONE_POPULATION 1.926e+01 9.383e-01 20.524 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 298200 on 303 degrees of freedom
## Multiple R-squared: 0.5816, Adjusted R-squared: 0.5802
## F-statistic: 421.2 on 1 and 303 DF, p-value: < 2.2e-16Plotting a fitted line
ggplot(new_overall, aes(TAP_IN, SUBZONE_POPULATION))+
geom_point()+
geom_smooth(method = "lm", se = FALSE)## `geom_smooth()` using formula 'y ~ x'
##Global Moran’s I prep
Adaptive distance weight matrix (fixed neighbours)
coords <- st_centroid(new_overall$geometry)
wm_knn8 <- knn2nb(knearneigh(coords, k = 8))Plotting the neighbours of the subzones
plot(new_overall$geometry, border = "lightgrey")
plot(wm_knn8, st_centroid(new_overall$geometry), add = TRUE, col = "red")
Row-standardised weights matrix
rswm_knn8 <- nb2listw(wm_knn8, style="B", zero.policy = TRUE)
rswm_knn8## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 305
## Number of nonzero links: 2440
## Percentage nonzero weights: 2.622951
## Average number of links: 8
## Non-symmetric neighbours list
##
## Weights style: B
## Weights constants summary:
## n nn S0 S1 S2
## B 305 93025 2440 4412 79922Spatially lagged values of Tap In Residuals
new_overall$TAP_IN_Residuals <- stat$residuals
TAP_IN_Residuals.lag <- lag.listw(rswm_knn8, new_overall$TAP_IN_Residuals)
new_overall$TAP_IN_Residuals_lag <- TAP_IN_Residuals.lagPlotting Tap In Residual values alongside Tap In Residual Lag values
The mappings below show how the Tap In Residuals and the Tap In Residuals Lag are distributed. The Tap in Residual Lags have a larger range of values.
tap_in_residual <- tm_shape(new_overall) +
tm_fill(col = "TAP_IN_Residuals",
n = 6 ,
style = "equal",
palette = "-RdBu") +
tm_borders(alpha = 0.5)+
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
tap_in_residual_lag <- tm_shape(new_overall) +
tm_fill(col = "TAP_IN_Residuals_lag",
n = 6 ,
style = "equal",
palette = "-RdBu") +
tm_borders(alpha = 0.5)+
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
tmap_arrange(tap_in_residual, tap_in_residual_lag, asp = 1, ncol = 2)
Compute Global Moran’s I for Residual
moran.test(new_overall$TAP_IN_Residuals, listw = rswm_knn8, zero.policy = TRUE, na.action = na.omit)##
## Moran I test under randomisation
##
## data: new_overall$TAP_IN_Residuals
## weights: rswm_knn8
##
## Moran I statistic standard deviate = -0.63611, p-value = 0.7376
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.0200608661 -0.0032894737 0.0006951389Computing Monte Carlo for Global Moran’s I
Since the p-value > 0.05. We do not reject the null hypothesis. This affirms that the linear regression model conforms to the randomisation assumption.
set.seed(1234)
bperm <- moran.mc(new_overall$TAP_IN_Residuals, listw=rswm_knn8, nsim=999, zero.policy = TRUE, na.action=na.omit)
bperm##
## Monte-Carlo simulation of Moran I
##
## data: new_overall$TAP_IN_Residuals
## weights: rswm_knn8
## number of simulations + 1: 1000
##
## statistic = -0.020061, observed rank = 277, p-value = 0.723
## alternative hypothesis: greaterVisualising Monte Carlo for Global Moran’s I of Residual
mean(bperm$res[1:999])## [1] -0.003718384var(bperm$res[1:999])## [1] 0.0006685426summary(bperm$res[1:999])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.078989 -0.021945 -0.005632 -0.003718 0.012736 0.082032hist(bperm$res, freq = TRUE, breaks = 20, xlab = "Simulated Moran's I")
abline(v = 0, col = "red") 
Visualising spatially lagged residual values using Moran Scatterplot
MS <- moran.plot(new_overall$TAP_IN_Residuals, rswm_knn8, zero.policy = TRUE, spChk = FALSE, labels = as.character(new_overall$SUBZONE_N), xlab="Residuals", ylab="Spatially Lag Residuals")
Compute Global Moran’s I correlogram and plot for residual
MI_corr <- sp.correlogram(wm_knn8, new_overall$TAP_IN_Residuals, order = 6, method = "I", style = "B")
plot(MI_corr)
Compute Global Moran’s I for Tap In
moran.test(new_overall$TAP_IN, listw = rswm_knn8, zero.policy = TRUE, na.action = na.omit)##
## Moran I test under randomisation
##
## data: new_overall$TAP_IN
## weights: rswm_knn8
##
## Moran I statistic standard deviate = 5.8318, p-value = 2.742e-09
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.1494330699 -0.0032894737 0.0006858098Computing Monte Carlo for Global Moran’s I
Since p-value < 0.05, we reject the null hypothesis. Since I > 0, there is a sign of clustering taking place. To investigate the subzones involved. We need to conduct a localised analysis.
set.seed(1234)
bperm <- moran.mc(new_overall$TAP_IN, listw=rswm_knn8, nsim=999, zero.policy = TRUE, na.action=na.omit)
bperm##
## Monte-Carlo simulation of Moran I
##
## data: new_overall$TAP_IN
## weights: rswm_knn8
## number of simulations + 1: 1000
##
## statistic = 0.14943, observed rank = 1000, p-value = 0.001
## alternative hypothesis: greaterVisualising Monte Carlo for Global Moran’s I of Tap In
mean(bperm$res[1:999])## [1] -0.004297922var(bperm$res[1:999])## [1] 0.0006157783summary(bperm$res[1:999])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.080555 -0.021328 -0.006372 -0.004298 0.010760 0.081246hist(bperm$res, freq = TRUE, breaks = 20, xlab = "Simulated Moran's I")
abline(v = 0, col = "red") 
Visualising spatially lagged Tap In values using Moran Scatterplot
MS <- moran.plot(new_overall$TAP_IN, rswm_knn8, zero.policy = TRUE, spChk=FALSE, labels=as.character(new_overall$SUBZONE_N), xlab="TAP_IN", ylab="Spatially Lag TAP_IN")
Compute Global Moran’s I correlogram and plot for Tap In
MI_corr <- sp.correlogram(wm_knn8, new_overall$TAP_IN, order = 6, method = "I", style = "B")
plot(MI_corr)
Cluster and Outlier Analysis
Computing Local Moran’s I for Tap In
fips <- order(new_overall$SUBZONE_N)
localMI <- localmoran(new_overall$TAP_IN, rswm_knn8)
head(localMI)## Ii E.Ii Var.Ii Z.Ii Pr(z > 0)
## 1 0.6454265 -0.02631579 7.422407 0.2465644 0.4026227
## 2 -1.3061542 -0.02631579 7.422407 -0.4697673 0.6807394
## 3 -0.6297459 -0.02631579 7.422407 -0.2214903 0.5876446
## 4 0.3484603 -0.02631579 7.422407 0.1375623 0.4452932
## 5 2.4327928 -0.02631579 7.422407 0.9026208 0.1833636
## 6 -1.0097970 -0.02631579 7.422407 -0.3609888 0.6409461Values ordered by Subzone Names
printCoefmat(data.frame(localMI[fips,], row.names = new_overall$SUBZONE_N[fips]), check.names = FALSE)## Ii E.Ii Var.Ii Z.Ii
## ADMIRALTY 0.64542650 -0.02631579 7.42240664 0.24656437
## AIRPORT ROAD -1.30615425 -0.02631579 7.42240664 -0.46976730
## ALEXANDRA HILL -0.62974591 -0.02631579 7.42240664 -0.22149025
## ALEXANDRA NORTH 0.34846033 -0.02631579 7.42240664 0.13756233
## ALJUNIED 2.43279282 -0.02631579 7.42240664 0.90262080
## ANAK BUKIT -1.00979699 -0.02631579 7.42240664 -0.36098877
## ANCHORVALE 1.01849525 -0.02631579 7.42240664 0.38350001
## ANG MO KIO TOWN CENTRE 2.62514354 -0.02631579 7.42240664 0.97322352
## ANSON 2.26264961 -0.02631579 7.42240664 0.84016939
## BALESTIER 2.54934188 -0.02631579 7.42240664 0.94540037
## BANGKIT 0.02758578 -0.02631579 7.42240664 0.01978468
## BAYFRONT SUBZONE 3.08832471 -0.02631579 7.42240664 1.14323511
## BAYSHORE -5.00031891 -0.02631579 7.42240664 -1.82571793
## BEDOK NORTH 33.95327536 -0.02631579 7.42240664 12.47227785
## BEDOK RESERVOIR 4.73526361 -0.02631579 7.42240664 1.74774737
## BEDOK SOUTH 10.89873586 -0.02631579 7.42240664 4.01006237
## BENCOOLEN 1.85471009 -0.02631579 7.42240664 0.69043437
## BENDEMEER 0.34642560 -0.02631579 7.42240664 0.13681548
## BENOI SECTOR 3.68002510 -0.02631579 7.42240664 1.36041994
## BIDADARI 0.17068271 -0.02631579 7.42240664 0.07230870
## BISHAN EAST -0.68467338 -0.02631579 7.42240664 -0.24165149
## BOAT QUAY 3.23251692 -0.02631579 7.42240664 1.19616116
## BOON KENG -0.14025460 -0.02631579 7.42240664 -0.04182147
## BOON LAY PLACE 4.23769159 -0.02631579 7.42240664 1.56511256
## BOON TECK -0.74973916 -0.02631579 7.42240664 -0.26553402
## BOULEVARD -3.26408894 -0.02631579 7.42240664 -1.18843120
## BRADDELL -0.73576232 -0.02631579 7.42240664 -0.26040379
## BRAS BASAH 0.42954826 -0.02631579 7.42240664 0.16732582
## BRICKWORKS -0.09616525 -0.02631579 7.42240664 -0.02563839
## BUGIS -0.33129287 -0.02631579 7.42240664 -0.11194245
## BUKIT BATOK CENTRAL -0.71410851 -0.02631579 7.42240664 -0.25245571
## BUKIT BATOK EAST -0.07043923 -0.02631579 7.42240664 -0.01619560
## BUKIT BATOK SOUTH -0.07794685 -0.02631579 7.42240664 -0.01895129
## BUKIT BATOK WEST -0.80457111 -0.02631579 7.42240664 -0.28566019
## BUKIT HO SWEE 0.83551920 -0.02631579 7.42240664 0.31633828
## BUKIT MERAH 0.08770551 -0.02631579 7.42240664 0.04185175
## CECIL 2.33770384 -0.02631579 7.42240664 0.86771820
## CENTRAL SUBZONE 2.87991559 -0.02631579 7.42240664 1.06673812
## CENTRAL WATER CATCHMENT 0.52166848 -0.02631579 7.42240664 0.20113874
## CHANGI AIRPORT -0.72616047 -0.02631579 7.42240664 -0.25687941
## CHANGI POINT -0.23273711 -0.02631579 7.42240664 -0.07576736
## CHANGI WEST -2.42527179 -0.02631579 7.42240664 -0.88054166
## CHATSWORTH 1.21485429 -0.02631579 7.42240664 0.45557399
## CHENG SAN 2.71785819 -0.02631579 7.42240664 1.00725462
## CHIN BEE -4.17363344 -0.02631579 7.42240664 -1.52228136
## CHINA SQUARE 2.10291104 -0.02631579 7.42240664 0.78153703
## CHINATOWN -1.05233562 -0.02631579 7.42240664 -0.37660266
## CHOA CHU KANG CENTRAL 0.10775462 -0.02631579 7.42240664 0.04921082
## CHOA CHU KANG NORTH -0.10611988 -0.02631579 7.42240664 -0.02929225
## CHONG BOON 2.72803271 -0.02631579 7.42240664 1.01098920
## CITY HALL 0.18042850 -0.02631579 7.42240664 0.07588591
## CITY TERMINALS 2.22061576 -0.02631579 7.42240664 0.82474078
## CLARKE QUAY 1.14196648 -0.02631579 7.42240664 0.42882038
## CLEMENTI CENTRAL -4.73476455 -0.02631579 7.42240664 -1.72824567
## CLEMENTI NORTH -0.63999324 -0.02631579 7.42240664 -0.22525155
## CLEMENTI WEST -0.10472038 -0.02631579 7.42240664 -0.02877856
## CLEMENTI WOODS 0.02230401 -0.02631579 7.42240664 0.01784599
## CLIFFORD PIER 2.70402521 -0.02631579 7.42240664 1.00217720
## COMMONWEALTH 0.73382625 -0.02631579 7.42240664 0.27901168
## COMPASSVALE -1.02579931 -0.02631579 7.42240664 -0.36686245
## CORONATION ROAD 1.01424374 -0.02631579 7.42240664 0.38193949
## CRAWFORD 0.47203280 -0.02631579 7.42240664 0.18291986
## DAIRY FARM -0.10878883 -0.02631579 7.42240664 -0.03027189
## DEFU INDUSTRIAL PARK -0.46140878 -0.02631579 7.42240664 -0.15970176
## DEPOT ROAD 0.04147064 -0.02631579 7.42240664 0.02488115
## DHOBY GHAUT 0.40654590 -0.02631579 7.42240664 0.15888276
## DOVER 0.52215351 -0.02631579 7.42240664 0.20131677
## DUNEARN 1.93402852 -0.02631579 7.42240664 0.71954835
## EAST COAST -2.92726581 -0.02631579 7.42240664 -1.06479959
## EVERTON PARK 1.31795579 -0.02631579 7.42240664 0.49341761
## FABER 0.12952292 -0.02631579 7.42240664 0.05720091
## FAJAR -0.11132407 -0.02631579 7.42240664 -0.03120246
## FARRER COURT 2.12672237 -0.02631579 7.42240664 0.79027702
## FARRER PARK -0.80808363 -0.02631579 7.42240664 -0.28694947
## FERNVALE -0.20037271 -0.02631579 7.42240664 -0.06388795
## FLORA DRIVE -3.79249177 -0.02631579 7.42240664 -1.38238253
## FORT CANNING 2.42430946 -0.02631579 7.42240664 0.89950697
## FRANKEL 12.32780820 -0.02631579 7.42240664 4.53460627
## GALI BATU 0.43295159 -0.02631579 7.42240664 0.16857502
## GEYLANG BAHRU -0.17900092 -0.02631579 7.42240664 -0.05604339
## GEYLANG EAST 10.10739340 -0.02631579 7.42240664 3.71959851
## GHIM MOH 1.19905660 -0.02631579 7.42240664 0.44977542
## GOMBAK -0.16601876 -0.02631579 7.42240664 -0.05127826
## GOODWOOD PARK 1.80860022 -0.02631579 7.42240664 0.67350964
## GREENWOOD PARK -6.08585685 -0.02631579 7.42240664 -2.22416684
## GUILIN -0.09810368 -0.02631579 7.42240664 -0.02634989
## GUL BASIN 3.93394293 -0.02631579 7.42240664 1.45362100
## GUL CIRCLE 3.12195415 -0.02631579 7.42240664 1.15557887
## HENDERSON HILL -0.01963817 -0.02631579 7.42240664 0.00245103
## HILLCREST 2.02895256 -0.02631579 7.42240664 0.75439042
## HILLVIEW 0.07900158 -0.02631579 7.42240664 0.03865695
## HOLLAND DRIVE 1.02563223 -0.02631579 7.42240664 0.38611966
## HOLLAND ROAD -0.41933122 -0.02631579 7.42240664 -0.14425711
## HONG KAH 0.47591550 -0.02631579 7.42240664 0.18434501
## HONG KAH NORTH 0.00447039 -0.02631579 7.42240664 0.01130013
## HOUGANG CENTRAL 3.58483099 -0.02631579 7.42240664 1.32547875
## HOUGANG EAST 0.17393108 -0.02631579 7.42240664 0.07350102
## HOUGANG WEST 4.85732565 -0.02631579 7.42240664 1.79255050
## INSTITUTION HILL 2.37396824 -0.02631579 7.42240664 0.88102912
## INTERNATIONAL BUSINESS PARK -0.21006410 -0.02631579 7.42240664 -0.06744519
## ISTANA NEGARA 2.03661222 -0.02631579 7.42240664 0.75720191
## JELEBU -1.90321636 -0.02631579 7.42240664 -0.68892016
## JOO KOON 0.22869557 -0.02631579 7.42240664 0.09360244
## JOO SENG 0.35955949 -0.02631579 7.42240664 0.14163630
## JURONG GATEWAY -0.61764204 -0.02631579 7.42240664 -0.21704750
## JURONG PORT 1.95242425 -0.02631579 7.42240664 0.72630054
## JURONG RIVER 0.06519628 -0.02631579 7.42240664 0.03358969
## JURONG WEST CENTRAL 2.39012490 -0.02631579 7.42240664 0.88695946
## KAKI BUKIT 4.47095277 -0.02631579 7.42240664 1.65073155
## KALLANG BAHRU 0.24008400 -0.02631579 7.42240664 0.09778258
## KALLANG WAY -0.93643635 -0.02631579 7.42240664 -0.33406160
## KAMPONG BUGIS 1.02884976 -0.02631579 7.42240664 0.38730065
## KAMPONG GLAM 1.29971654 -0.02631579 7.42240664 0.48672286
## KAMPONG JAVA 0.98475023 -0.02631579 7.42240664 0.37111383
## KAMPONG TIONG BAHRU 0.32289122 -0.02631579 7.42240664 0.12817714
## KAMPONG UBI 4.58983393 -0.02631579 7.42240664 1.69436712
## KANGKAR 0.19215342 -0.02631579 7.42240664 0.08018957
## KATONG -1.59302997 -0.02631579 7.42240664 -0.57506562
## KEAT HONG -0.02750395 -0.02631579 7.42240664 -0.00043612
## KEBUN BAHRU 0.20501112 -0.02631579 7.42240664 0.08490901
## KEMBANGAN 9.48969188 -0.02631579 7.42240664 3.49286992
## KENT RIDGE 1.31920836 -0.02631579 7.42240664 0.49387737
## KHATIB 0.15371512 -0.02631579 7.42240664 0.06608071
## KIAN TECK -1.89252066 -0.02631579 7.42240664 -0.68499428
## KIM KEAT -0.09679243 -0.02631579 7.42240664 -0.02586860
## KOVAN 1.37818415 -0.02631579 7.42240664 0.51552455
## KRANJI -1.82362535 -0.02631579 7.42240664 -0.65970612
## LAKESIDE -0.97123199 -0.02631579 7.42240664 -0.34683341
## LAVENDER -1.55031756 -0.02631579 7.42240664 -0.55938794
## LEEDON PARK 1.65654152 -0.02631579 7.42240664 0.61769619
## LEONIE HILL 1.67070447 -0.02631579 7.42240664 0.62289473
## LIM CHU KANG 0.44478169 -0.02631579 7.42240664 0.17291728
## LITTLE INDIA 1.54096408 -0.02631579 7.42240664 0.57527326
## LIU FANG 2.28101225 -0.02631579 7.42240664 0.84690944
## LORONG 8 TOA PAYOH 0.15263617 -0.02631579 7.42240664 0.06568468
## LORONG AH SOO 3.96861436 -0.02631579 7.42240664 1.46634721
## LORONG CHUAN -1.38542409 -0.02631579 7.42240664 -0.49886346
## LORONG HALUS -1.15723960 -0.02631579 7.42240664 -0.41510788
## LOWER SELETAR -0.99625461 -0.02631579 7.42240664 -0.35601801
## LOYANG EAST -5.05340809 -0.02631579 7.42240664 -1.84520443
## LOYANG WEST -4.81172521 -0.02631579 7.42240664 -1.75649423
## MACKENZIE 1.84249682 -0.02631579 7.42240664 0.68595146
## MACPHERSON 2.02667595 -0.02631579 7.42240664 0.75355478
## MALCOLM 0.25496845 -0.02631579 7.42240664 0.10324595
## MANDAI EAST -2.39866050 -0.02631579 7.42240664 -0.87077394
## MANDAI ESTATE -4.47900679 -0.02631579 7.42240664 -1.63436926
## MANDAI WEST -5.68129301 -0.02631579 7.42240664 -2.07567086
## MARGARET DRIVE 0.50298522 -0.02631579 7.42240664 0.19428101
## MARINA CENTRE 0.77023939 -0.02631579 7.42240664 0.29237719
## MARINA EAST (MP) 0.30973149 -0.02631579 7.42240664 0.12334684
## MARINA SOUTH 3.51995394 -0.02631579 7.42240664 1.30166550
## MARINE PARADE 1.73337665 -0.02631579 7.42240664 0.64589868
## MARITIME SQUARE -1.28953980 -0.02631579 7.42240664 -0.46366893
## MARYMOUNT 1.57879010 -0.02631579 7.42240664 0.58915737
## MATILDA 2.84008946 -0.02631579 7.42240664 1.05211986
## MAXWELL 3.17713807 -0.02631579 7.42240664 1.17583424
## MEI CHIN 0.56092017 -0.02631579 7.42240664 0.21554615
## MIDVIEW 2.30788166 -0.02631579 7.42240664 0.85677191
## MONK'S HILL 0.95075296 -0.02631579 7.42240664 0.35863507
## MOULMEIN -0.42492822 -0.02631579 7.42240664 -0.14631150
## MOUNT PLEASANT -0.03629903 -0.02631579 7.42240664 -0.00366437
## MOUNTBATTEN -1.47801359 -0.02631579 7.42240664 -0.53284862
## NASSIM 0.94692740 -0.02631579 7.42240664 0.35723089
## NATIONAL UNIVERSITY OF S'PORE 0.53504822 -0.02631579 7.42240664 0.20604980
## NATURE RESERVE 0.38090231 -0.02631579 7.42240664 0.14947023
## NEE SOON -2.15610940 -0.02631579 7.42240664 -0.78174507
## NEWTON CIRCUS 0.69833059 -0.02631579 7.42240664 0.26598292
## NORTH COAST 12.60146829 -0.02631579 7.42240664 4.63505376
## NORTHLAND 1.49357034 -0.02631579 7.42240664 0.55787729
## NORTHSHORE -3.45564037 -0.02631579 7.42240664 -1.25874054
## ONE NORTH 0.43752656 -0.02631579 7.42240664 0.17025427
## ONE TREE HILL 1.78049767 -0.02631579 7.42240664 0.66319455
## ORANGE GROVE 2.59211068 -0.02631579 7.42240664 0.96109874
## OXLEY 2.17797054 -0.02631579 7.42240664 0.80908777
## PANDAN -0.52825620 -0.02631579 7.42240664 -0.18423825
## PANG SUA -0.04545560 -0.02631579 7.42240664 -0.00702531
## PASIR PANJANG 1 0.38452027 -0.02631579 7.42240664 0.15079821
## PASIR PANJANG 2 0.96019455 -0.02631579 7.42240664 0.36210062
## PASIR RIS CENTRAL 8.69607331 -0.02631579 7.42240664 3.20157061
## PASIR RIS DRIVE 7.09719027 -0.02631579 7.42240664 2.61469735
## PASIR RIS PARK -4.36456334 -0.02631579 7.42240664 -1.59236256
## PASIR RIS WAFER FAB PARK -1.04132953 -0.02631579 7.42240664 -0.37256285
## PASIR RIS WEST 3.24799493 -0.02631579 7.42240664 1.20184239
## PATERSON 1.14515699 -0.02631579 7.42240664 0.42999146
## PAYA LEBAR EAST -0.58464150 -0.02631579 7.42240664 -0.20493458
## PAYA LEBAR NORTH 1.39862416 -0.02631579 7.42240664 0.52302710
## PAYA LEBAR WEST 0.34759380 -0.02631579 7.42240664 0.13724427
## PEARL'S HILL 0.63006407 -0.02631579 7.42240664 0.24092556
## PEI CHUN 0.10582153 -0.02631579 7.42240664 0.04850127
## PENG SIANG 0.24055766 -0.02631579 7.42240664 0.09795644
## PENJURU CRESCENT 0.95612216 -0.02631579 7.42240664 0.36060584
## PEOPLE'S PARK 0.86311543 -0.02631579 7.42240664 0.32646753
## PHILLIP 3.24673971 -0.02631579 7.42240664 1.20138166
## PIONEER SECTOR 3.47621553 -0.02631579 7.42240664 1.28561123
## PLAB 2.14096509 -0.02631579 7.42240664 0.79550484
## PORT 1.38402726 -0.02631579 7.42240664 0.51766928
## POTONG PASIR 0.63253559 -0.02631579 7.42240664 0.24183274
## PUNGGOL FIELD 1.05405834 -0.02631579 7.42240664 0.39655352
## PUNGGOL TOWN CENTRE -2.08234872 -0.02631579 7.42240664 -0.75467106
## QUEENSWAY 1.26963610 -0.02631579 7.42240664 0.47568177
## RAFFLES PLACE 3.76212872 -0.02631579 7.42240664 1.39055624
## REDHILL 0.08679050 -0.02631579 7.42240664 0.04151589
## RESERVOIR VIEW 0.95482610 -0.02631579 7.42240664 0.36013012
## RIDOUT 2.19114506 -0.02631579 7.42240664 0.81392350
## RIVERVALE 3.74215658 -0.02631579 7.42240664 1.38322543
## ROBERTSON QUAY 1.81104922 -0.02631579 7.42240664 0.67440855
## ROCHOR CANAL 1.32719929 -0.02631579 7.42240664 0.49681046
## SAFTI -2.40451420 -0.02631579 7.42240664 -0.87292255
## SAMULUN 3.95094860 -0.02631579 7.42240664 1.45986296
## SAUJANA -0.81899364 -0.02631579 7.42240664 -0.29095401
## SELEGIE 1.74332949 -0.02631579 7.42240664 0.64955189
## SELETAR -0.96120484 -0.02631579 7.42240664 -0.34315292
## SELETAR AEROSPACE PARK 1.06814556 -0.02631579 7.42240664 0.40172426
## SELETAR HILLS -0.57196800 -0.02631579 7.42240664 -0.20028275
## SEMBAWANG CENTRAL -0.61197497 -0.02631579 7.42240664 -0.21496739
## SEMBAWANG EAST -0.51168710 -0.02631579 7.42240664 -0.17815652
## SEMBAWANG HILLS -1.51294269 -0.02631579 7.42240664 -0.54566942
## SEMBAWANG NORTH 0.16668467 -0.02631579 7.42240664 0.07084121
## SEMBAWANG SPRINGS -0.54716218 -0.02631579 7.42240664 -0.19117772
## SEMBAWANG STRAITS -1.14269131 -0.02631579 7.42240664 -0.40976790
## SENGKANG TOWN CENTRE 9.43970596 -0.02631579 7.42240664 3.47452248
## SENGKANG WEST 2.32475411 -0.02631579 7.42240664 0.86296498
## SENJA -0.43006884 -0.02631579 7.42240664 -0.14819838
## SENNETT 0.37376705 -0.02631579 7.42240664 0.14685122
## SENOKO NORTH -0.37676801 -0.02631579 7.42240664 -0.12863420
## SENOKO SOUTH -0.62677189 -0.02631579 7.42240664 -0.22039863
## SENOKO WEST -3.36017826 -0.02631579 7.42240664 -1.22370098
## SENTOSA 1.64243088 -0.02631579 7.42240664 0.61251685
## SERANGOON CENTRAL -0.72539375 -0.02631579 7.42240664 -0.25659798
## SERANGOON GARDEN 1.98809743 -0.02631579 7.42240664 0.73939446
## SERANGOON NORTH 0.43377677 -0.02631579 7.42240664 0.16887791
## SERANGOON NORTH IND ESTATE -2.33953795 -0.02631579 7.42240664 -0.84907289
## SHANGRI-LA -0.66502124 -0.02631579 7.42240664 -0.23443813
## SHIPYARD 3.65186967 -0.02631579 7.42240664 1.35008543
## SIGLAP -4.28767353 -0.02631579 7.42240664 -1.56414000
## SIMEI 15.33816121 -0.02631579 7.42240664 5.63956244
## SINGAPORE GENERAL HOSPITAL 0.32428683 -0.02631579 7.42240664 0.12868940
## SINGAPORE POLYTECHNIC -0.23070466 -0.02631579 7.42240664 -0.07502135
## SOMERSET -1.03651644 -0.02631579 7.42240664 -0.37079620
## SPRINGLEAF 1.51932375 -0.02631579 7.42240664 0.56733013
## STRAITS VIEW 4.41624639 -0.02631579 7.42240664 1.63065146
## SUNGEI ROAD 0.90786184 -0.02631579 7.42240664 0.34289179
## SUNSET WAY -1.18632993 -0.02631579 7.42240664 -0.42578554
## SWISS CLUB 1.05164925 -0.02631579 7.42240664 0.39566925
## TAGORE 0.09879419 -0.02631579 7.42240664 0.04592187
## TAI SENG 0.00547515 -0.02631579 7.42240664 0.01166893
## TAMAN JURONG 0.87874340 -0.02631579 7.42240664 0.33220381
## TAMPINES EAST 29.12259347 -0.02631579 7.42240664 10.69916627
## TAMPINES NORTH -3.96454420 -0.02631579 7.42240664 -1.44553473
## TAMPINES WEST 32.95499708 -0.02631579 7.42240664 12.10585778
## TANGLIN 2.46292538 -0.02631579 7.42240664 0.91368102
## TANGLIN HALT 0.65785155 -0.02631579 7.42240664 0.25112501
## TANJONG PAGAR 2.84161108 -0.02631579 7.42240664 1.05267838
## TANJONG RHU 0.11236777 -0.02631579 7.42240664 0.05090408
## TEBAN GARDENS -0.01164304 -0.02631579 7.42240664 0.00538566
## TECK WHYE 0.65280318 -0.02631579 7.42240664 0.24927199
## TELOK BLANGAH DRIVE -0.01678095 -0.02631579 7.42240664 0.00349978
## TELOK BLANGAH RISE 0.02242528 -0.02631579 7.42240664 0.01789051
## TELOK BLANGAH WAY -0.33860968 -0.02631579 7.42240664 -0.11462810
## TENGAH -1.38215314 -0.02631579 7.42240664 -0.49766285
## TENGEH 3.67332286 -0.02631579 7.42240664 1.35795987
## THE WHARVES 1.45309895 -0.02631579 7.42240664 0.54302218
## TIONG BAHRU 0.44429951 -0.02631579 7.42240664 0.17274030
## TIONG BAHRU STATION -1.61790281 -0.02631579 7.42240664 -0.58419524
## TOA PAYOH CENTRAL 0.65395631 -0.02631579 7.42240664 0.24969525
## TOA PAYOH WEST -0.92912372 -0.02631579 7.42240664 -0.33137748
## TOH GUAN -0.00957386 -0.02631579 7.42240664 0.00614516
## TOH TUCK 0.06647805 -0.02631579 7.42240664 0.03406017
## TOWNSVILLE 0.88756789 -0.02631579 7.42240664 0.33544286
## TRAFALGAR 2.60171733 -0.02631579 7.42240664 0.96462489
## TUAS BAY 2.75254595 -0.02631579 7.42240664 1.01998684
## TUAS NORTH 2.81036435 -0.02631579 7.42240664 1.04120920
## TUAS PROMENADE 2.51201022 -0.02631579 7.42240664 0.93169771
## TUAS VIEW 3.71056572 -0.02631579 7.42240664 1.37162994
## TUAS VIEW EXTENSION 4.08790695 -0.02631579 7.42240664 1.51013380
## TUKANG -2.50396076 -0.02631579 7.42240664 -0.90942461
## TURF CLUB 0.44231409 -0.02631579 7.42240664 0.17201155
## TYERSALL 1.38969751 -0.02631579 7.42240664 0.51975055
## ULU PANDAN 0.97287575 -0.02631579 7.42240664 0.36675528
## UPPER PAYA LEBAR -0.27469456 -0.02631579 7.42240664 -0.09116793
## UPPER THOMSON 2.72397431 -0.02631579 7.42240664 1.00949956
## VICTORIA -1.13486781 -0.02631579 7.42240664 -0.40689627
## WATERWAY EAST -1.28562631 -0.02631579 7.42240664 -0.46223248
## WENYA -3.56443051 -0.02631579 7.42240664 -1.29867218
## WEST COAST -0.39161655 -0.02631579 7.42240664 -0.13408438
## WESTERN WATER CATCHMENT 1.39175721 -0.02631579 7.42240664 0.52050657
## WOODGROVE 0.69164563 -0.02631579 7.42240664 0.26352919
## WOODLANDS EAST 7.58740575 -0.02631579 7.42240664 2.79463193
## WOODLANDS REGIONAL CENTRE 17.48478967 -0.02631579 7.42240664 6.42748677
## WOODLANDS SOUTH 1.40347827 -0.02631579 7.42240664 0.52480881
## WOODLANDS WEST 3.18904811 -0.02631579 7.42240664 1.18020584
## WOODLEIGH 2.06134244 -0.02631579 7.42240664 0.76627919
## XILIN -7.68415989 -0.02631579 7.42240664 -2.81082721
## YEW TEE 0.18063789 -0.02631579 7.42240664 0.07596277
## YIO CHU KANG 0.16325192 -0.02631579 7.42240664 0.06958121
## YIO CHU KANG EAST -0.28613817 -0.02631579 7.42240664 -0.09536833
## YIO CHU KANG NORTH 0.17518937 -0.02631579 7.42240664 0.07396288
## YIO CHU KANG WEST 0.00887667 -0.02631579 7.42240664 0.01291746
## YISHUN CENTRAL 7.09243589 -0.02631579 7.42240664 2.61295224
## YISHUN EAST 4.51863492 -0.02631579 7.42240664 1.66823337
## YISHUN SOUTH 3.87340725 -0.02631579 7.42240664 1.43140125
## YISHUN WEST 6.86939135 -0.02631579 7.42240664 2.53108329
## YUHUA EAST 0.40079412 -0.02631579 7.42240664 0.15677156
## YUHUA WEST 0.65352354 -0.02631579 7.42240664 0.24953640
## YUNNAN 3.97281330 -0.02631579 7.42240664 1.46788844
## Pr.z...0.
## ADMIRALTY 0.4026
## AIRPORT ROAD 0.6807
## ALEXANDRA HILL 0.5876
## ALEXANDRA NORTH 0.4453
## ALJUNIED 0.1834
## ANAK BUKIT 0.6409
## ANCHORVALE 0.3507
## ANG MO KIO TOWN CENTRE 0.1652
## ANSON 0.2004
## BALESTIER 0.1722
## BANGKIT 0.4921
## BAYFRONT SUBZONE 0.1265
## BAYSHORE 0.9661
## BEDOK NORTH 0.0000
## BEDOK RESERVOIR 0.0403
## BEDOK SOUTH 0.0000
## BENCOOLEN 0.2450
## BENDEMEER 0.4456
## BENOI SECTOR 0.0868
## BIDADARI 0.4712
## BISHAN EAST 0.5955
## BOAT QUAY 0.1158
## BOON KENG 0.5167
## BOON LAY PLACE 0.0588
## BOON TECK 0.6047
## BOULEVARD 0.8827
## BRADDELL 0.6027
## BRAS BASAH 0.4336
## BRICKWORKS 0.5102
## BUGIS 0.5446
## BUKIT BATOK CENTRAL 0.5997
## BUKIT BATOK EAST 0.5065
## BUKIT BATOK SOUTH 0.5076
## BUKIT BATOK WEST 0.6124
## BUKIT HO SWEE 0.3759
## BUKIT MERAH 0.4833
## CECIL 0.1928
## CENTRAL SUBZONE 0.1430
## CENTRAL WATER CATCHMENT 0.4203
## CHANGI AIRPORT 0.6014
## CHANGI POINT 0.5302
## CHANGI WEST 0.8107
## CHATSWORTH 0.3243
## CHENG SAN 0.1569
## CHIN BEE 0.9360
## CHINA SQUARE 0.2172
## CHINATOWN 0.6468
## CHOA CHU KANG CENTRAL 0.4804
## CHOA CHU KANG NORTH 0.5117
## CHONG BOON 0.1560
## CITY HALL 0.4698
## CITY TERMINALS 0.2048
## CLARKE QUAY 0.3340
## CLEMENTI CENTRAL 0.9580
## CLEMENTI NORTH 0.5891
## CLEMENTI WEST 0.5115
## CLEMENTI WOODS 0.4929
## CLIFFORD PIER 0.1581
## COMMONWEALTH 0.3901
## COMPASSVALE 0.6431
## CORONATION ROAD 0.3513
## CRAWFORD 0.4274
## DAIRY FARM 0.5121
## DEFU INDUSTRIAL PARK 0.5634
## DEPOT ROAD 0.4901
## DHOBY GHAUT 0.4369
## DOVER 0.4202
## DUNEARN 0.2359
## EAST COAST 0.8565
## EVERTON PARK 0.3109
## FABER 0.4772
## FAJAR 0.5124
## FARRER COURT 0.2147
## FARRER PARK 0.6129
## FERNVALE 0.5255
## FLORA DRIVE 0.9166
## FORT CANNING 0.1842
## FRANKEL 0.0000
## GALI BATU 0.4331
## GEYLANG BAHRU 0.5223
## GEYLANG EAST 0.0001
## GHIM MOH 0.3264
## GOMBAK 0.5204
## GOODWOOD PARK 0.2503
## GREENWOOD PARK 0.9869
## GUILIN 0.5105
## GUL BASIN 0.0730
## GUL CIRCLE 0.1239
## HENDERSON HILL 0.4990
## HILLCREST 0.2253
## HILLVIEW 0.4846
## HOLLAND DRIVE 0.3497
## HOLLAND ROAD 0.5574
## HONG KAH 0.4269
## HONG KAH NORTH 0.4955
## HOUGANG CENTRAL 0.0925
## HOUGANG EAST 0.4707
## HOUGANG WEST 0.0365
## INSTITUTION HILL 0.1892
## INTERNATIONAL BUSINESS PARK 0.5269
## ISTANA NEGARA 0.2245
## JELEBU 0.7546
## JOO KOON 0.4627
## JOO SENG 0.4437
## JURONG GATEWAY 0.5859
## JURONG PORT 0.2338
## JURONG RIVER 0.4866
## JURONG WEST CENTRAL 0.1876
## KAKI BUKIT 0.0494
## KALLANG BAHRU 0.4611
## KALLANG WAY 0.6308
## KAMPONG BUGIS 0.3493
## KAMPONG GLAM 0.3132
## KAMPONG JAVA 0.3553
## KAMPONG TIONG BAHRU 0.4490
## KAMPONG UBI 0.0451
## KANGKAR 0.4680
## KATONG 0.7174
## KEAT HONG 0.5002
## KEBUN BAHRU 0.4662
## KEMBANGAN 0.0002
## KENT RIDGE 0.3107
## KHATIB 0.4737
## KIAN TECK 0.7533
## KIM KEAT 0.5103
## KOVAN 0.3031
## KRANJI 0.7453
## LAKESIDE 0.6356
## LAVENDER 0.7121
## LEEDON PARK 0.2684
## LEONIE HILL 0.2667
## LIM CHU KANG 0.4314
## LITTLE INDIA 0.2826
## LIU FANG 0.1985
## LORONG 8 TOA PAYOH 0.4738
## LORONG AH SOO 0.0713
## LORONG CHUAN 0.6911
## LORONG HALUS 0.6610
## LOWER SELETAR 0.6391
## LOYANG EAST 0.9675
## LOYANG WEST 0.9605
## MACKENZIE 0.2464
## MACPHERSON 0.2256
## MALCOLM 0.4589
## MANDAI EAST 0.8081
## MANDAI ESTATE 0.9489
## MANDAI WEST 0.9810
## MARGARET DRIVE 0.4230
## MARINA CENTRE 0.3850
## MARINA EAST (MP) 0.4509
## MARINA SOUTH 0.0965
## MARINE PARADE 0.2592
## MARITIME SQUARE 0.6786
## MARYMOUNT 0.2779
## MATILDA 0.1464
## MAXWELL 0.1198
## MEI CHIN 0.4147
## MIDVIEW 0.1958
## MONK'S HILL 0.3599
## MOULMEIN 0.5582
## MOUNT PLEASANT 0.5015
## MOUNTBATTEN 0.7029
## NASSIM 0.3605
## NATIONAL UNIVERSITY OF S'PORE 0.4184
## NATURE RESERVE 0.4406
## NEE SOON 0.7828
## NEWTON CIRCUS 0.3951
## NORTH COAST 0.0000
## NORTHLAND 0.2885
## NORTHSHORE 0.8959
## ONE NORTH 0.4324
## ONE TREE HILL 0.2536
## ORANGE GROVE 0.1683
## OXLEY 0.2092
## PANDAN 0.5731
## PANG SUA 0.5028
## PASIR PANJANG 1 0.4401
## PASIR PANJANG 2 0.3586
## PASIR RIS CENTRAL 0.0007
## PASIR RIS DRIVE 0.0045
## PASIR RIS PARK 0.9443
## PASIR RIS WAFER FAB PARK 0.6453
## PASIR RIS WEST 0.1147
## PATERSON 0.3336
## PAYA LEBAR EAST 0.5812
## PAYA LEBAR NORTH 0.3005
## PAYA LEBAR WEST 0.4454
## PEARL'S HILL 0.4048
## PEI CHUN 0.4807
## PENG SIANG 0.4610
## PENJURU CRESCENT 0.3592
## PEOPLE'S PARK 0.3720
## PHILLIP 0.1148
## PIONEER SECTOR 0.0993
## PLAB 0.2132
## PORT 0.3023
## POTONG PASIR 0.4045
## PUNGGOL FIELD 0.3458
## PUNGGOL TOWN CENTRE 0.7748
## QUEENSWAY 0.3172
## RAFFLES PLACE 0.0822
## REDHILL 0.4834
## RESERVOIR VIEW 0.3594
## RIDOUT 0.2078
## RIVERVALE 0.0833
## ROBERTSON QUAY 0.2500
## ROCHOR CANAL 0.3097
## SAFTI 0.8086
## SAMULUN 0.0722
## SAUJANA 0.6145
## SELEGIE 0.2580
## SELETAR 0.6343
## SELETAR AEROSPACE PARK 0.3439
## SELETAR HILLS 0.5794
## SEMBAWANG CENTRAL 0.5851
## SEMBAWANG EAST 0.5707
## SEMBAWANG HILLS 0.7074
## SEMBAWANG NORTH 0.4718
## SEMBAWANG SPRINGS 0.5758
## SEMBAWANG STRAITS 0.6590
## SENGKANG TOWN CENTRE 0.0003
## SENGKANG WEST 0.1941
## SENJA 0.5589
## SENNETT 0.4416
## SENOKO NORTH 0.5512
## SENOKO SOUTH 0.5872
## SENOKO WEST 0.8895
## SENTOSA 0.2701
## SERANGOON CENTRAL 0.6013
## SERANGOON GARDEN 0.2298
## SERANGOON NORTH 0.4329
## SERANGOON NORTH IND ESTATE 0.8021
## SHANGRI-LA 0.5927
## SHIPYARD 0.0885
## SIGLAP 0.9411
## SIMEI 0.0000
## SINGAPORE GENERAL HOSPITAL 0.4488
## SINGAPORE POLYTECHNIC 0.5299
## SOMERSET 0.6446
## SPRINGLEAF 0.2852
## STRAITS VIEW 0.0515
## SUNGEI ROAD 0.3658
## SUNSET WAY 0.6649
## SWISS CLUB 0.3462
## TAGORE 0.4817
## TAI SENG 0.4953
## TAMAN JURONG 0.3699
## TAMPINES EAST 0.0000
## TAMPINES NORTH 0.9258
## TAMPINES WEST 0.0000
## TANGLIN 0.1804
## TANGLIN HALT 0.4009
## TANJONG PAGAR 0.1462
## TANJONG RHU 0.4797
## TEBAN GARDENS 0.4979
## TECK WHYE 0.4016
## TELOK BLANGAH DRIVE 0.4986
## TELOK BLANGAH RISE 0.4929
## TELOK BLANGAH WAY 0.5456
## TENGAH 0.6906
## TENGEH 0.0872
## THE WHARVES 0.2936
## TIONG BAHRU 0.4314
## TIONG BAHRU STATION 0.7205
## TOA PAYOH CENTRAL 0.4014
## TOA PAYOH WEST 0.6298
## TOH GUAN 0.4975
## TOH TUCK 0.4864
## TOWNSVILLE 0.3686
## TRAFALGAR 0.1674
## TUAS BAY 0.1539
## TUAS NORTH 0.1489
## TUAS PROMENADE 0.1757
## TUAS VIEW 0.0851
## TUAS VIEW EXTENSION 0.0655
## TUKANG 0.8184
## TURF CLUB 0.4317
## TYERSALL 0.3016
## ULU PANDAN 0.3569
## UPPER PAYA LEBAR 0.5363
## UPPER THOMSON 0.1564
## VICTORIA 0.6580
## WATERWAY EAST 0.6780
## WENYA 0.9030
## WEST COAST 0.5533
## WESTERN WATER CATCHMENT 0.3014
## WOODGROVE 0.3961
## WOODLANDS EAST 0.0026
## WOODLANDS REGIONAL CENTRE 0.0000
## WOODLANDS SOUTH 0.2999
## WOODLANDS WEST 0.1190
## WOODLEIGH 0.2218
## XILIN 0.9975
## YEW TEE 0.4697
## YIO CHU KANG 0.4723
## YIO CHU KANG EAST 0.5380
## YIO CHU KANG NORTH 0.4705
## YIO CHU KANG WEST 0.4948
## YISHUN CENTRAL 0.0045
## YISHUN EAST 0.0476
## YISHUN SOUTH 0.0762
## YISHUN WEST 0.0057
## YUHUA EAST 0.4377
## YUHUA WEST 0.4015
## YUNNAN 0.0711Mapping Local Moran’s I values and p-values for Tap In
The the dark red zones on the left map and the dark blue zones on the right map indicate the subzones responsible for the clusters.
new_overall.localMI <- cbind(new_overall, localMI)
localMI.map <- tm_shape(new_overall.localMI) +
tm_fill(col = "Ii",
breaks = c(-Inf, -5, 0, 10, Inf),
palette = "-RdBu",
title = "local Moran's I statistics") +
tm_borders(alpha = 0.5) +
tm_layout(legend.position = c("RIGHT", "BOTTOM"))
pvalue.map <- tm_shape(new_overall.localMI) +
tm_fill(col = "Pr.z...0.",
breaks = c(-Inf, 0.05, Inf),
palette = "-Blues",
title = "local Moran's I p-values") +
tm_borders(alpha = 0.5) +
tm_layout(legend.position = c("RIGHT", "BOTTOM"))
tmap_arrange(localMI.map, pvalue.map, asp = 1, ncol = 2)
Spatially lagged Tap In values
TAP_IN.lag <- lag.listw(rswm_knn8, new_overall$TAP_IN)
TAP_IN.lag## [1] 2360689 3810264 1909319 2808248 3454081 2530076 5367231 3807032
## [9] 1476891 3742097 3094827 969605 7339746 5854459 8082570 7247906
## [17] 1664422 3326373 782330 2910594 2789308 958886 4014323 6463505
## [25] 4177272 1136205 4425529 2092079 3286976 1585895 2777896 3249361
## [33] 3495794 4396903 2117436 2360088 894967 890617 1248921 2634562
## [41] 3227990 6287172 1311923 4090012 5935829 1267776 947917 3066772
## [49] 3626234 4974136 1646720 1538016 1251979 2002715 2640447 3382580
## [57] 3362163 1010528 1698224 6544517 1580091 2139550 3115937 3457492
## [65] 3006646 1589242 2042558 1190669 4682816 1587298 2933606 3233406
## [73] 1460926 1337657 2468871 7043029 1409121 7399685 2444465 3783444
## [81] 4935568 1868535 3408758 1943362 7218109 3208887 766812 795395
## [89] 2333457 1313031 2799989 1614170 1920492 3151774 3051669 4451777
## [97] 5090005 4838521 1549476 3173853 1878304 2506912 4066787 2554825
## [105] 2872208 1826229 2988598 3264132 4734441 2304733 4257455 2191763
## [113] 2127388 1892828 2642240 6216975 3580993 6558493 4678732 3891157
## [121] 8030964 2089526 4093074 4463780 3132265 6464651 4939436 4005924
## [129] 1561398 1565980 1977368 2772954 2093610 1437229 2209550 4518409
## [137] 3985306 3687393 4213805 6442924 6429150 1410088 5245016 2783820
## [145] 4921727 5746818 6289962 1907643 2083086 2865646 1032096 3864180
## [153] 2408304 4117441 4102714 1199888 1935939 6780533 2499900 2281133
## [161] 3061954 4537558 1411549 2168616 2740144 4395563 1947045 6017924
## [169] 5095544 5031765 1669534 1823106 1575148 1700176 3413548 3064390
## [177] 1740674 1887152 6027085 5646516 6835692 3706915 6234541 1674632
## [185] 3377835 1923931 2838399 1796812 2904022 3441490 2390549 1423601
## [193] 1226713 877954 1707521 1945619 2143207 3820271 4901730 2030572
## [201] 604984 2603364 2451129 1443764 4566844 1107730 2137068 4497685
## [209] 779136 4150743 1820949 3929169 2372060 3515213 2759657 3570112
## [217] 4145919 1910029 3619735 3720541 4753609 1688670 4322266 1979301
## [225] 3356166 3733930 6110871 2108269 2888384 4534312 5082507 4960709
## [233] 4883878 800756 6853869 10154621 2565261 3551857 1406620 2096151
## [241] 526299 2295471 3800357 2064380 2951852 2940485 3324901 4859952
## [249] 8046239 9355089 1586294 1728382 1269505 2915763 2971103 4345954
## [257] 2648588 2983413 3522482 3869082 967107 2074847 2482641 2104241
## [265] 3158159 5232290 3662530 2945576 5918139 4057342 876962 904051
## [273] 870706 519444 590970 4642233 2205339 1260567 2316859 3475308
## [281] 4310848 1187419 4725736 6898085 3278857 6419080 4317959 4451471
## [289] 5215358 5490284 5112455 1667298 10995485 3276423 3273281 3277823
## [297] 2910665 2806340 4426568 5018853 4930773 4463238 3816677 3902611
## [305] 4119939new_overall$TAP_IN_lag <- TAP_IN.lagPlotting Tap In values alongside Tap In lag values
tap_in <- tm_shape(new_overall) +
tm_fill(col = "TAP_IN",
n = 6 ,
style = "equal",
palette = "Blues") +
tm_borders(alpha = 0.5)+
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
tap_in_lag <- tm_shape(new_overall) +
tm_fill(col = "TAP_IN_lag",
n = 6 ,
style = "equal",
palette = "Blues") +
tm_borders(alpha = 0.5)+
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
tmap_arrange(tap_in, tap_in_lag, asp = 1, ncol = 2)
Plotting Moran scatterplot as standardised variables
new_overall$Z.TAP_IN <- scale(new_overall$TAP_IN) %>% as.vector()
nci2 <- moran.plot(new_overall$Z.TAP_IN, rswm_knn8, labels = as.character(new_overall$SUBZONE_N), xlab = "z-TAP_IN", ylab = "Spatially Lag z-TAP_IN")
Creating the quadrants
quadrant <- vector(mode="numeric",length=nrow(localMI))
DV <- new_overall$TAP_IN
C_mI <- new_overall$TAP_IN_lag
signif <- 0.05
quadrant[DV >0 & C_mI>0] <- 4
quadrant[DV <0 & C_mI<0] <- 1
quadrant[DV <0 & C_mI>0] <- 2
quadrant[DV >0 & C_mI<0] <- 3
quadrant[localMI[,5]>signif] <- 0Mapping LISA Cluster
new_overall.localMI$quadrant <- quadrant
colors <- c("#ffffff", "#2c7bb6", "#abd9e9", "#fdae61", "#d7191c")
clusters <- c("insignificant", "low-low", "low-high", "high-low", "high-high")
tm_shape(new_overall.localMI) +
tm_fill(col = "quadrant", style = "cat", palette = colors[c(sort(unique(quadrant)))+1], labels = clusters[c(sort(unique(quadrant)))+1], popup.vars = c("SUBZONE_N")) +
tm_view(set.zoom.limits = c(11,17)) +
tm_borders(alpha=0.5) +
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
Hot Spot and Cold Spot Area Analysis
Create adaptive proximity matrix
coords <- st_centroid(new_overall)## Warning in st_centroid.sf(new_overall): st_centroid assumes attributes are
## constant over geometries of xknb <- knn2nb(knearneigh(coords, k = 8, longlat = FALSE), row.names = row.names(new_overall$TAP_IN))## Warning in knearneigh(coords, k = 8, longlat = FALSE): dnearneigh: longlat
## argument overrides objectknb_lw <- nb2listw(knb, style = 'B')
summary(knb_lw)## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 305
## Number of nonzero links: 2440
## Percentage nonzero weights: 2.622951
## Average number of links: 8
## Non-symmetric neighbours list
## Link number distribution:
##
## 8
## 305
## 305 least connected regions:
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 with 8 links
## 305 most connected regions:
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 with 8 links
##
## Weights style: B
## Weights constants summary:
## n nn S0 S1 S2
## B 305 93025 2440 4412 79922Plotting the subzones and their neighbours
plot(new_overall$geometry, border = "lightgrey")
plot(knb, st_centroid(new_overall$geometry), pch = 19, cex = 0.6, add = TRUE, col = "red")
Gi stats using adaptive distance
fips <- order(new_overall$SUBZONE_N)
gi.adaptive <- localG(new_overall$TAP_IN, knb_lw)
new_overall.gi <- cbind(new_overall, as.matrix(gi.adaptive))
names(new_overall.gi)[8] <- "gstat_adaptive"Mapping out the hot and cold spots
tm_shape(new_overall.gi) +
tm_fill(col = "gstat_adaptive",
style = "equal",
palette = "-RdBu",
title = "local Gi") +
tm_borders(alpha = 0.5) +
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
#Linear Regression for Tap Out vs Population
Tap Out(x) vs Population(y)
The Linear Regression is significant as p-value < 0.05.
stat2 <- lm(TAP_OUT ~ SUBZONE_POPULATION, new_overall)
plot(stat2)
summary(stat2)##
## Call:
## lm(formula = TAP_OUT ~ SUBZONE_POPULATION, data = new_overall)
##
## Residuals:
## Min 1Q Median 3Q Max
## -824702 -123573 -59179 34208 1643316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.259e+05 2.043e+04 6.163 2.27e-09 ***
## SUBZONE_POPULATION 1.917e+01 9.088e-01 21.088 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 288800 on 303 degrees of freedom
## Multiple R-squared: 0.5948, Adjusted R-squared: 0.5934
## F-statistic: 444.7 on 1 and 303 DF, p-value: < 2.2e-16Plotting a fitted line
ggplot(new_overall, aes(TAP_OUT, SUBZONE_POPULATION))+
geom_point()+
geom_smooth(method = "lm", se = FALSE)## `geom_smooth()` using formula 'y ~ x'
##Global Moran’s I Prep
Adaptive distance weight matrix (fixed neighbours)
coords <- st_centroid(new_overall$geometry)
wm_knn8 <- knn2nb(knearneigh(coords, k = 8))Plotting the neighbours of the subzones
plot(new_overall$geometry, border = "lightgrey")
plot(wm_knn8, st_centroid(new_overall$geometry), add = TRUE, col = "red")
Row-standardised weights matrix
rswm_knn8 <- nb2listw(wm_knn8, style="B", zero.policy = TRUE)
rswm_knn8## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 305
## Number of nonzero links: 2440
## Percentage nonzero weights: 2.622951
## Average number of links: 8
## Non-symmetric neighbours list
##
## Weights style: B
## Weights constants summary:
## n nn S0 S1 S2
## B 305 93025 2440 4412 79922Spatially lagged values of Tap Out Residuals
new_overall$TAP_OUT_Residuals <- stat$residuals
TAP_OUT_Residuals.lag <- lag.listw(rswm_knn8, new_overall$TAP_OUT_Residuals)
new_overall$TAP_OUT_Residuals_lag <- TAP_OUT_Residuals.lagPlotting Tap Out Residual values alongside Tap Out Residual Lag values
tap_out_residual <- tm_shape(new_overall) +
tm_fill(col = "TAP_OUT_Residuals",
n = 6 ,
style = "equal",
palette = "-RdBu") +
tm_borders(alpha = 0.5)+
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
tap_out_residual_lag <- tm_shape(new_overall) +
tm_fill(col = "TAP_OUT_Residuals_lag",
n = 6 ,
style = "equal",
palette = "-RdBu") +
tm_borders(alpha = 0.5)+
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
tmap_arrange(tap_out_residual, tap_out_residual_lag, asp = 1, ncol = 2)
Compute Global Moran’s I for Residual
moran.test(new_overall$TAP_OUT_Residuals, listw = rswm_knn8, zero.policy = TRUE, na.action = na.omit)##
## Moran I test under randomisation
##
## data: new_overall$TAP_OUT_Residuals
## weights: rswm_knn8
##
## Moran I statistic standard deviate = -0.63611, p-value = 0.7376
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## -0.0200608661 -0.0032894737 0.0006951389Computing Monte Carlo for Global Moran’s I
Since the p-value > 0.05. We do not reject the null hypothesis. This affirms that the linear regression model conforms to the randomisation assumption.
set.seed(1234)
bperm2 <- moran.mc(new_overall$TAP_OUT_Residuals, listw=rswm_knn8, nsim=999, zero.policy = TRUE, na.action=na.omit)
bperm2##
## Monte-Carlo simulation of Moran I
##
## data: new_overall$TAP_OUT_Residuals
## weights: rswm_knn8
## number of simulations + 1: 1000
##
## statistic = -0.020061, observed rank = 277, p-value = 0.723
## alternative hypothesis: greaterVisualising Monte Carlo for Global Moran’s I of Residual
mean(bperm2$res[1:999])## [1] -0.003718384var(bperm2$res[1:999])## [1] 0.0006685426summary(bperm2$res[1:999])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.078989 -0.021945 -0.005632 -0.003718 0.012736 0.082032hist(bperm2$res, freq = TRUE, breaks = 20, xlab = "Simulated Moran's I")
abline(v = 0, col = "red") 
Visualising spatially lagged residual values using Moran Scatterplot
MS2 <- moran.plot(new_overall$TAP_OUT_Residuals, rswm_knn8, zero.policy = TRUE, spChk = FALSE, labels = as.character(new_overall$SUBZONE_N), xlab = "Tap Out Residuals", ylab = "Spatially Lag Tap Out Residuals")
Compute Global Moran’s I correlogram and plot for residual
MI_corr2 <- sp.correlogram(wm_knn8, new_overall$TAP_OUT_Residuals, order = 6, method = "I", style = "B")
plot(MI_corr2)
Compute Global Moran’s I for Tap Out
moran.test(new_overall$TAP_OUT, listw = rswm_knn8, zero.policy = TRUE, na.action = na.omit)##
## Moran I test under randomisation
##
## data: new_overall$TAP_OUT
## weights: rswm_knn8
##
## Moran I statistic standard deviate = 6.073, p-value = 6.279e-10
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.1558627082 -0.0032894737 0.0006867918Computing Monte Carlo for Global Moran’s I of Tap Out
Since p-value < 0.05, we reject the null hypothesis. Since I > 0, there is a sign of clustering taking place. To investigate the subzones involved. We need to conduct a localised analysis.
set.seed(1234)
bperm2 <- moran.mc(new_overall$TAP_OUT, listw=rswm_knn8, nsim=999, zero.policy = TRUE, na.action=na.omit)
bperm2##
## Monte-Carlo simulation of Moran I
##
## data: new_overall$TAP_OUT
## weights: rswm_knn8
## number of simulations + 1: 1000
##
## statistic = 0.15586, observed rank = 1000, p-value = 0.001
## alternative hypothesis: greaterVisualising Monte Carlo for Global Moran’s I of Tap Out
mean(bperm2$res[1:999])## [1] -0.004154364var(bperm2$res[1:999])## [1] 0.000610482summary(bperm2$res[1:999])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.077280 -0.021906 -0.005272 -0.004154 0.011249 0.088465hist(bperm2$res, freq = TRUE, breaks = 20, xlab = "Simulated Moran's I")
abline(v = 0, col = "red") 
Visualising spatially lagged Tap Out values using Moran Scatterplot
MS2 <- moran.plot(new_overall$TAP_OUT, rswm_knn8, zero.policy = TRUE, spChk=FALSE, labels=as.character(new_overall$SUBZONE_N), xlab="TAP_OUT", ylab="Spatially Lag TAP_OUT")
Compute Global Moran’s I correlogram and plot for Tap Out
MI_corr2 <- sp.correlogram(wm_knn8, new_overall$TAP_IN, order = 6, method = "I", style = "B")
plot(MI_corr2)
Cluster and Outlier Analysis
Computing Local Moran’s I for Tap Out
fips <- order(new_overall$SUBZONE_N)
localMI2 <- localmoran(new_overall$TAP_OUT, rswm_knn8)
head(localMI2)## Ii E.Ii Var.Ii Z.Ii Pr(z > 0)
## 1 0.5456460 -0.02631579 7.432713 0.2097941 0.4169142
## 2 -1.6552724 -0.02631579 7.432713 -0.5974971 0.7249122
## 3 -1.1271038 -0.02631579 7.432713 -0.4037662 0.6568077
## 4 0.3182471 -0.02631579 7.432713 0.1263848 0.4497137
## 5 1.4244070 -0.02631579 7.432713 0.5321214 0.2973210
## 6 -0.9347775 -0.02631579 7.432713 -0.3332214 0.6305164Values ordered by Subzone Names
printCoefmat(data.frame(localMI2[fips,], row.names = new_overall$SUBZONE_N[fips]), check.names = FALSE)## Ii E.Ii Var.Ii Z.Ii
## ADMIRALTY 0.54564597 -0.02631579 7.43271338 0.20979412
## AIRPORT ROAD -1.65527238 -0.02631579 7.43271338 -0.59749712
## ALEXANDRA HILL -1.12710379 -0.02631579 7.43271338 -0.40376623
## ALEXANDRA NORTH 0.31824711 -0.02631579 7.43271338 0.12638479
## ALJUNIED 1.42440699 -0.02631579 7.43271338 0.53212142
## ANAK BUKIT -0.93477750 -0.02631579 7.43271338 -0.33322144
## ANCHORVALE 1.04343553 -0.02631579 7.43271338 0.39238206
## ANG MO KIO TOWN CENTRE 2.25592333 -0.02631579 7.43271338 0.83711949
## ANSON 2.40942555 -0.02631579 7.43271338 0.89342371
## BALESTIER 2.64595142 -0.02631579 7.43271338 0.98018080
## BANGKIT 0.01195084 -0.02631579 7.43271338 0.01403610
## BAYFRONT SUBZONE 2.99208673 -0.02631579 7.43271338 1.10714235
## BAYSHORE -6.67697616 -0.02631579 7.43271338 -2.43944526
## BEDOK NORTH 37.28416216 -0.02631579 7.43271338 13.68538815
## BEDOK RESERVOIR 4.00290578 -0.02631579 7.43271338 1.47790820
## BEDOK SOUTH 12.01947338 -0.02631579 7.43271338 4.41836474
## BENCOOLEN 2.16500010 -0.02631579 7.43271338 0.80376908
## BENDEMEER 0.43809951 -0.02631579 7.43271338 0.17034635
## BENOI SECTOR 3.71865369 -0.02631579 7.43271338 1.37364525
## BIDADARI 0.24455135 -0.02631579 7.43271338 0.09935338
## BISHAN EAST -0.14424575 -0.02631579 7.43271338 -0.04325641
## BOAT QUAY 2.69465624 -0.02631579 7.43271338 0.99804560
## BOON KENG -0.24721715 -0.02631579 7.43271338 -0.08102605
## BOON LAY PLACE 4.45887130 -0.02631579 7.43271338 1.64515518
## BOON TECK -0.38991018 -0.02631579 7.43271338 -0.13336549
## BOULEVARD -4.69526507 -0.02631579 7.43271338 -1.71255869
## BRADDELL -0.85273689 -0.02631579 7.43271338 -0.30312915
## BRAS BASAH 0.49210060 -0.02631579 7.43271338 0.19015381
## BRICKWORKS -0.07435569 -0.02631579 7.43271338 -0.01762091
## BUGIS -0.48273958 -0.02631579 7.43271338 -0.16741508
## BUKIT BATOK CENTRAL -0.52628243 -0.02631579 7.43271338 -0.18338649
## BUKIT BATOK EAST -0.16060761 -0.02631579 7.43271338 -0.04925790
## BUKIT BATOK SOUTH -0.06358078 -0.02631579 7.43271338 -0.01366871
## BUKIT BATOK WEST -0.56120900 -0.02631579 7.43271338 -0.19619746
## BUKIT HO SWEE 0.95554664 -0.02631579 7.43271338 0.36014463
## BUKIT MERAH -0.08938195 -0.02631579 7.43271338 -0.02313251
## CECIL 2.49543112 -0.02631579 7.43271338 0.92497033
## CENTRAL SUBZONE 2.47976219 -0.02631579 7.43271338 0.91922301
## CENTRAL WATER CATCHMENT 0.66866224 -0.02631579 7.43271338 0.25491617
## CHANGI AIRPORT -0.85065158 -0.02631579 7.43271338 -0.30236426
## CHANGI POINT -0.26074185 -0.02631579 7.43271338 -0.08598688
## CHANGI WEST -3.71355643 -0.02631579 7.43271338 -1.35247046
## CHATSWORTH 1.49476486 -0.02631579 7.43271338 0.55792850
## CHENG SAN 1.94197363 -0.02631579 7.43271338 0.72196354
## CHIN BEE -4.10605111 -0.02631579 7.43271338 -1.49643651
## CHINA SQUARE 1.91384933 -0.02631579 7.43271338 0.71164762
## CHINATOWN -0.16544963 -0.02631579 7.43271338 -0.05103394
## CHOA CHU KANG CENTRAL 0.15062307 -0.02631579 7.43271338 0.06490072
## CHOA CHU KANG NORTH -0.25596377 -0.02631579 7.43271338 -0.08423430
## CHONG BOON 3.91604220 -0.02631579 7.43271338 1.44604685
## CITY HALL -0.01121360 -0.02631579 7.43271338 0.00553945
## CITY TERMINALS 2.25263717 -0.02631579 7.43271338 0.83591413
## CLARKE QUAY 1.79327713 -0.02631579 7.43271338 0.66742204
## CLEMENTI CENTRAL -4.09991872 -0.02631579 7.43271338 -1.49418717
## CLEMENTI NORTH -0.80498966 -0.02631579 7.43271338 -0.28561559
## CLEMENTI WEST -0.01119436 -0.02631579 7.43271338 0.00554650
## CLEMENTI WOODS 0.00354447 -0.02631579 7.43271338 0.01095267
## CLIFFORD PIER 2.62292252 -0.02631579 7.43271338 0.97173386
## COMMONWEALTH 1.04781595 -0.02631579 7.43271338 0.39398878
## COMPASSVALE -0.97411467 -0.02631579 7.43271338 -0.34765021
## CORONATION ROAD 1.31766630 -0.02631579 7.43271338 0.49296920
## CRAWFORD 0.41265093 -0.02631579 7.43271338 0.16101187
## DAIRY FARM -0.05652138 -0.02631579 7.43271338 -0.01107933
## DEFU INDUSTRIAL PARK -0.45346265 -0.02631579 7.43271338 -0.15667638
## DEPOT ROAD -0.26686224 -0.02631579 7.43271338 -0.08823182
## DHOBY GHAUT 0.17359588 -0.02631579 7.43271338 0.07332709
## DOVER 0.35783279 -0.02631579 7.43271338 0.14090472
## DUNEARN 2.57517690 -0.02631579 7.43271338 0.95422088
## EAST COAST -2.88243109 -0.02631579 7.43271338 -1.04761581
## EVERTON PARK 1.11854894 -0.02631579 7.43271338 0.41993346
## FABER 0.13957746 -0.02631579 7.43271338 0.06084922
## FAJAR -0.07873435 -0.02631579 7.43271338 -0.01922699
## FARRER COURT 2.36539111 -0.02631579 7.43271338 0.87727199
## FARRER PARK -0.57909881 -0.02631579 7.43271338 -0.20275940
## FERNVALE -0.15158598 -0.02631579 7.43271338 -0.04594879
## FLORA DRIVE -3.28127835 -0.02631579 7.43271338 -1.19391196
## FORT CANNING 2.22467748 -0.02631579 7.43271338 0.82565859
## FRANKEL 16.35150346 -0.02631579 7.43271338 6.00734233
## GALI BATU 0.35624179 -0.02631579 7.43271338 0.14032115
## GEYLANG BAHRU -0.19985790 -0.02631579 7.43271338 -0.06365480
## GEYLANG EAST 9.19489059 -0.02631579 7.43271338 3.38231498
## GHIM MOH 1.15473087 -0.02631579 7.43271338 0.43320490
## GOMBAK -0.55290051 -0.02631579 7.43271338 -0.19314993
## GOODWOOD PARK 1.77031320 -0.02631579 7.43271338 0.65899893
## GREENWOOD PARK -5.88331749 -0.02631579 7.43271338 -2.14833329
## GUILIN 0.00254666 -0.02631579 7.43271338 0.01058667
## GUL BASIN 3.79690956 -0.02631579 7.43271338 1.40234931
## GUL CIRCLE 3.54017594 -0.02631579 7.43271338 1.30818007
## HENDERSON HILL 0.13430767 -0.02631579 7.43271338 0.05891628
## HILLCREST 2.27584327 -0.02631579 7.43271338 0.84442607
## HILLVIEW 0.09925998 -0.02631579 7.43271338 0.04606087
## HOLLAND DRIVE 1.02279555 -0.02631579 7.43271338 0.38481136
## HOLLAND ROAD -0.23333460 -0.02631579 7.43271338 -0.07593397
## HONG KAH 1.19699075 -0.02631579 7.43271338 0.44870572
## HONG KAH NORTH -0.00040568 -0.02631579 7.43271338 0.00950376
## HOUGANG CENTRAL 4.00272212 -0.02631579 7.43271338 1.47784083
## HOUGANG EAST 0.08448490 -0.02631579 7.43271338 0.04064141
## HOUGANG WEST 4.77655089 -0.02631579 7.43271338 1.76167925
## INSTITUTION HILL 2.66582043 -0.02631579 7.43271338 0.98746870
## INTERNATIONAL BUSINESS PARK -0.39585537 -0.02631579 7.43271338 -0.13554618
## ISTANA NEGARA 2.50713079 -0.02631579 7.43271338 0.92926175
## JELEBU -1.74301207 -0.02631579 7.43271338 -0.62967982
## JOO KOON -0.03427698 -0.02631579 7.43271338 -0.00292014
## JOO SENG 0.31520790 -0.02631579 7.43271338 0.12527002
## JURONG GATEWAY -0.13467266 -0.02631579 7.43271338 -0.03974502
## JURONG PORT 1.56124479 -0.02631579 7.43271338 0.58231317
## JURONG RIVER -0.12917187 -0.02631579 7.43271338 -0.03772735
## JURONG WEST CENTRAL 3.51873453 -0.02631579 7.43271338 1.30031541
## KAKI BUKIT 5.45847473 -0.02631579 7.43271338 2.01180717
## KALLANG BAHRU -0.01228355 -0.02631579 7.43271338 0.00514699
## KALLANG WAY -1.28010511 -0.02631579 7.43271338 -0.45988673
## KAMPONG BUGIS 0.91471946 -0.02631579 7.43271338 0.34516933
## KAMPONG GLAM 1.47841260 -0.02631579 7.43271338 0.55193053
## KAMPONG JAVA 0.96944875 -0.02631579 7.43271338 0.36524389
## KAMPONG TIONG BAHRU 0.30000253 -0.02631579 7.43271338 0.11969273
## KAMPONG UBI 5.39284283 -0.02631579 7.43271338 1.98773356
## KANGKAR 0.17165034 -0.02631579 7.43271338 0.07261347
## KATONG -1.41361965 -0.02631579 7.43271338 -0.50885952
## KEAT HONG 0.72638201 -0.02631579 7.43271338 0.27608763
## KEBUN BAHRU -0.11372661 -0.02631579 7.43271338 -0.03206206
## KEMBANGAN 9.81414922 -0.02631579 7.43271338 3.60945747
## KENT RIDGE 1.05528892 -0.02631579 7.43271338 0.39672985
## KHATIB 1.47728667 -0.02631579 7.43271338 0.55151755
## KIAN TECK -1.48416267 -0.02631579 7.43271338 -0.53473452
## KIM KEAT -0.54874136 -0.02631579 7.43271338 -0.19162437
## KOVAN 1.15184227 -0.02631579 7.43271338 0.43214537
## KRANJI -1.56159771 -0.02631579 7.43271338 -0.56313749
## LAKESIDE -0.75321046 -0.02631579 7.43271338 -0.26662311
## LAVENDER -1.27356085 -0.02631579 7.43271338 -0.45748631
## LEEDON PARK 1.89566710 -0.02631579 7.43271338 0.70497842
## LEONIE HILL 1.90460731 -0.02631579 7.43271338 0.70825767
## LIM CHU KANG 0.51448039 -0.02631579 7.43271338 0.19836266
## LITTLE INDIA 1.94276429 -0.02631579 7.43271338 0.72225355
## LIU FANG 2.08804221 -0.02631579 7.43271338 0.77554112
## LORONG 8 TOA PAYOH 0.00102112 -0.02631579 7.43271338 0.01002711
## LORONG AH SOO 4.30646441 -0.02631579 7.43271338 1.58925273
## LORONG CHUAN -1.69724234 -0.02631579 7.43271338 -0.61289160
## LORONG HALUS -1.26321362 -0.02631579 7.43271338 -0.45369097
## LOWER SELETAR -1.38252706 -0.02631579 7.43271338 -0.49745483
## LOYANG EAST -5.55075880 -0.02631579 7.43271338 -2.02635161
## LOYANG WEST -3.82763256 -0.02631579 7.43271338 -1.39431330
## MACKENZIE 2.47936594 -0.02631579 7.43271338 0.91907767
## MACPHERSON 1.90334188 -0.02631579 7.43271338 0.70779351
## MALCOLM 0.40061550 -0.02631579 7.43271338 0.15659731
## MANDAI EAST -3.28951132 -0.02631579 7.43271338 -1.19693180
## MANDAI ESTATE -4.75774598 -0.02631579 7.43271338 -1.73547653
## MANDAI WEST -5.93550528 -0.02631579 7.43271338 -2.16747563
## MARGARET DRIVE 0.78992865 -0.02631579 7.43271338 0.29939638
## MARINA CENTRE 0.75748873 -0.02631579 7.43271338 0.28749750
## MARINA EAST (MP) 0.00991489 -0.02631579 7.43271338 0.01328932
## MARINA SOUTH 3.45407779 -0.02631579 7.43271338 1.27659949
## MARINE PARADE 1.46855853 -0.02631579 7.43271338 0.54831609
## MARITIME SQUARE -0.82602029 -0.02631579 7.43271338 -0.29332957
## MARYMOUNT 1.37380861 -0.02631579 7.43271338 0.51356206
## MATILDA 2.92061344 -0.02631579 7.43271338 1.08092612
## MAXWELL 3.26279309 -0.02631579 7.43271338 1.20643675
## MEI CHIN 0.20107978 -0.02631579 7.43271338 0.08340811
## MIDVIEW 2.09064385 -0.02631579 7.43271338 0.77649540
## MONK'S HILL 1.16489044 -0.02631579 7.43271338 0.43693141
## MOULMEIN -0.32296480 -0.02631579 7.43271338 -0.10881010
## MOUNT PLEASANT 0.16503382 -0.02631579 7.43271338 0.07018655
## MOUNTBATTEN -1.36153145 -0.02631579 7.43271338 -0.48975370
## NASSIM 1.17172014 -0.02631579 7.43271338 0.43943652
## NATIONAL UNIVERSITY OF S'PORE 0.55734745 -0.02631579 7.43271338 0.21408619
## NATURE RESERVE 0.26738241 -0.02631579 7.43271338 0.10772775
## NEE SOON -2.39784135 -0.02631579 7.43271338 -0.86986953
## NEWTON CIRCUS 1.18371644 -0.02631579 7.43271338 0.44383673
## NORTH COAST 11.18238038 -0.02631579 7.43271338 4.11132117
## NORTHLAND 1.24363680 -0.02631579 7.43271338 0.46581537
## NORTHSHORE -3.48272416 -0.02631579 7.43271338 -1.26780178
## ONE NORTH 0.05867265 -0.02631579 7.43271338 0.03117354
## ONE TREE HILL 2.05491083 -0.02631579 7.43271338 0.76338862
## ORANGE GROVE 2.81331706 -0.02631579 7.43271338 1.04157009
## OXLEY 2.44112404 -0.02631579 7.43271338 0.90505064
## PANDAN -0.57927268 -0.02631579 7.43271338 -0.20282318
## PANG SUA -0.15707296 -0.02631579 7.43271338 -0.04796140
## PASIR PANJANG 1 0.29560492 -0.02631579 7.43271338 0.11807969
## PASIR PANJANG 2 1.02004691 -0.02631579 7.43271338 0.38380317
## PASIR RIS CENTRAL 9.57626668 -0.02631579 7.43271338 3.52220276
## PASIR RIS DRIVE 6.71150503 -0.02631579 7.43271338 2.47141549
## PASIR RIS PARK -3.73171356 -0.02631579 7.43271338 -1.35913045
## PASIR RIS WAFER FAB PARK -0.97353486 -0.02631579 7.43271338 -0.34743754
## PASIR RIS WEST 2.78069738 -0.02631579 7.43271338 1.02960527
## PATERSON 2.03631167 -0.02631579 7.43271338 0.75656649
## PAYA LEBAR EAST -0.73605021 -0.02631579 7.43271338 -0.26032877
## PAYA LEBAR NORTH 1.43913472 -0.02631579 7.43271338 0.53752351
## PAYA LEBAR WEST 0.19530108 -0.02631579 7.43271338 0.08128850
## PEARL'S HILL 1.05982798 -0.02631579 7.43271338 0.39839476
## PEI CHUN 0.03630611 -0.02631579 7.43271338 0.02296955
## PENG SIANG 0.10305591 -0.02631579 7.43271338 0.04745321
## PENJURU CRESCENT 0.93417515 -0.02631579 7.43271338 0.35230563
## PEOPLE'S PARK 0.50960880 -0.02631579 7.43271338 0.19657577
## PHILLIP 3.42267805 -0.02631579 7.43271338 1.26508214
## PIONEER SECTOR 3.72685568 -0.02631579 7.43271338 1.37665372
## PLAB 2.11598252 -0.02631579 7.43271338 0.78578955
## PORT 1.46793796 -0.02631579 7.43271338 0.54808847
## POTONG PASIR 0.44900943 -0.02631579 7.43271338 0.17434808
## PUNGGOL FIELD 0.86765063 -0.02631579 7.43271338 0.32790460
## PUNGGOL TOWN CENTRE -2.44575423 -0.02631579 7.43271338 -0.88744385
## QUEENSWAY 1.16867095 -0.02631579 7.43271338 0.43831809
## RAFFLES PLACE 3.79494716 -0.02631579 7.43271338 1.40162950
## REDHILL 0.11069087 -0.02631579 7.43271338 0.05025369
## RESERVOIR VIEW 0.46460357 -0.02631579 7.43271338 0.18006797
## RIDOUT 2.18850867 -0.02631579 7.43271338 0.81239196
## RIVERVALE 3.99093431 -0.02631579 7.43271338 1.47351709
## ROBERTSON QUAY 1.86009754 -0.02631579 7.43271338 0.69193160
## ROCHOR CANAL 1.49404835 -0.02631579 7.43271338 0.55766569
## SAFTI -2.47224553 -0.02631579 7.43271338 -0.89716079
## SAMULUN 3.60350464 -0.02631579 7.43271338 1.33140887
## SAUJANA -0.79757794 -0.02631579 7.43271338 -0.28289699
## SELEGIE 1.97282467 -0.02631579 7.43271338 0.73327962
## SELETAR -0.91834708 -0.02631579 7.43271338 -0.32719480
## SELETAR AEROSPACE PARK 1.12437653 -0.02631579 7.43271338 0.42207101
## SELETAR HILLS -0.30813076 -0.02631579 7.43271338 -0.10336901
## SEMBAWANG CENTRAL -0.22943524 -0.02631579 7.43271338 -0.07450370
## SEMBAWANG EAST -0.31833771 -0.02631579 7.43271338 -0.10711289
## SEMBAWANG HILLS -1.11403936 -0.02631579 7.43271338 -0.39897423
## SEMBAWANG NORTH -0.19155620 -0.02631579 7.43271338 -0.06060976
## SEMBAWANG SPRINGS -0.49445579 -0.02631579 7.43271338 -0.17171256
## SEMBAWANG STRAITS -1.02227223 -0.02631579 7.43271338 -0.36531428
## SENGKANG TOWN CENTRE 8.67591420 -0.02631579 7.43271338 3.19195576
## SENGKANG WEST 2.28479768 -0.02631579 7.43271338 0.84771053
## SENJA 0.09648908 -0.02631579 7.43271338 0.04504451
## SENNETT 0.52602943 -0.02631579 7.43271338 0.20259882
## SENOKO NORTH -0.45538991 -0.02631579 7.43271338 -0.15738329
## SENOKO SOUTH -0.89549745 -0.02631579 7.43271338 -0.31881362
## SENOKO WEST -3.24059557 -0.02631579 7.43271338 -1.17898963
## SENTOSA 1.57222995 -0.02631579 7.43271338 0.58634250
## SERANGOON CENTRAL 0.18975183 -0.02631579 7.43271338 0.07925305
## SERANGOON GARDEN 1.76094241 -0.02631579 7.43271338 0.65556175
## SERANGOON NORTH 0.85745044 -0.02631579 7.43271338 0.32416320
## SERANGOON NORTH IND ESTATE -2.45658303 -0.02631579 7.43271338 -0.89141582
## SHANGRI-LA 0.04763764 -0.02631579 7.43271338 0.02712593
## SHIPYARD 3.80555256 -0.02631579 7.43271338 1.40551954
## SIGLAP -5.55954117 -0.02631579 7.43271338 -2.02957296
## SIMEI 15.47847364 -0.02631579 7.43271338 5.68711722
## SINGAPORE GENERAL HOSPITAL 0.44040231 -0.02631579 7.43271338 0.17119101
## SINGAPORE POLYTECHNIC -0.33386737 -0.02631579 7.43271338 -0.11280914
## SOMERSET -0.39249639 -0.02631579 7.43271338 -0.13431411
## SPRINGLEAF 1.34842174 -0.02631579 7.43271338 0.50425022
## STRAITS VIEW 4.46178137 -0.02631579 7.43271338 1.64622259
## SUNGEI ROAD 1.10052419 -0.02631579 7.43271338 0.41332203
## SUNSET WAY -0.97210569 -0.02631579 7.43271338 -0.34691332
## SWISS CLUB 0.66639066 -0.02631579 7.43271338 0.25408296
## TAGORE 0.12616626 -0.02631579 7.43271338 0.05593003
## TAI SENG -0.03647951 -0.02631579 7.43271338 -0.00372803
## TAMAN JURONG 1.29576437 -0.02631579 7.43271338 0.48493563
## TAMPINES EAST 31.13435495 -0.02631579 7.43271338 11.42965455
## TAMPINES NORTH -5.44390666 -0.02631579 7.43271338 -1.98715851
## TAMPINES WEST 36.68440151 -0.02631579 7.43271338 13.46539747
## TANGLIN 2.64238688 -0.02631579 7.43271338 0.97887333
## TANGLIN HALT 0.40998039 -0.02631579 7.43271338 0.16003233
## TANJONG PAGAR 3.02941660 -0.02631579 7.43271338 1.12083485
## TANJONG RHU -0.07421434 -0.02631579 7.43271338 -0.01756906
## TEBAN GARDENS -0.15583006 -0.02631579 7.43271338 -0.04750551
## TECK WHYE 0.42731655 -0.02631579 7.43271338 0.16639118
## TELOK BLANGAH DRIVE -0.14676699 -0.02631579 7.43271338 -0.04418119
## TELOK BLANGAH RISE 0.01407831 -0.02631579 7.43271338 0.01481645
## TELOK BLANGAH WAY -0.49792096 -0.02631579 7.43271338 -0.17298357
## TENGAH -1.31176323 -0.02631579 7.43271338 -0.47149884
## TENGEH 4.04380849 -0.02631579 7.43271338 1.49291120
## THE WHARVES 1.56944907 -0.02631579 7.43271338 0.58532248
## TIONG BAHRU 0.43964168 -0.02631579 7.43271338 0.17091201
## TIONG BAHRU STATION -1.38082819 -0.02631579 7.43271338 -0.49683169
## TOA PAYOH CENTRAL 0.78269895 -0.02631579 7.43271338 0.29674454
## TOA PAYOH WEST -1.10079539 -0.02631579 7.43271338 -0.39411638
## TOH GUAN 0.03048442 -0.02631579 7.43271338 0.02083417
## TOH TUCK 0.09078552 -0.02631579 7.43271338 0.04295246
## TOWNSVILLE -0.62605576 -0.02631579 7.43271338 -0.21998309
## TRAFALGAR 2.68430065 -0.02631579 7.43271338 0.99424720
## TUAS BAY 2.71752085 -0.02631579 7.43271338 1.00643228
## TUAS NORTH 3.06679006 -0.02631579 7.43271338 1.13454334
## TUAS PROMENADE 3.08249555 -0.02631579 7.43271338 1.14030407
## TUAS VIEW 3.05403267 -0.02631579 7.43271338 1.12986396
## TUAS VIEW EXTENSION 3.98892388 -0.02631579 7.43271338 1.47277967
## TUKANG -2.58497098 -0.02631579 7.43271338 -0.93850820
## TURF CLUB 0.22721918 -0.02631579 7.43271338 0.09299598
## TYERSALL 2.02597185 -0.02631579 7.43271338 0.75277387
## ULU PANDAN 1.02434736 -0.02631579 7.43271338 0.38538056
## UPPER PAYA LEBAR -0.04041201 -0.02631579 7.43271338 -0.00517046
## UPPER THOMSON 2.65278867 -0.02631579 7.43271338 0.98268868
## VICTORIA -0.34713167 -0.02631579 7.43271338 -0.11767445
## WATERWAY EAST -0.98047659 -0.02631579 7.43271338 -0.34998375
## WENYA -3.88732289 -0.02631579 7.43271338 -1.41620756
## WEST COAST -0.32615205 -0.02631579 7.43271338 -0.10997918
## WESTERN WATER CATCHMENT 2.05582483 -0.02631579 7.43271338 0.76372387
## WOODGROVE -0.08306680 -0.02631579 7.43271338 -0.02081613
## WOODLANDS EAST 5.77517818 -0.02631579 7.43271338 2.12797319
## WOODLANDS REGIONAL CENTRE 16.09816954 -0.02631579 7.43271338 5.91442009
## WOODLANDS SOUTH 1.18518542 -0.02631579 7.43271338 0.44437555
## WOODLANDS WEST 4.94683055 -0.02631579 7.43271338 1.82413739
## WOODLEIGH 1.66363917 -0.02631579 7.43271338 0.61987117
## XILIN -7.08698701 -0.02631579 7.43271338 -2.58983620
## YEW TEE 0.12334626 -0.02631579 7.43271338 0.05489566
## YIO CHU KANG 0.00121495 -0.02631579 7.43271338 0.01009820
## YIO CHU KANG EAST -0.06927511 -0.02631579 7.43271338 -0.01575737
## YIO CHU KANG NORTH 0.38946872 -0.02631579 7.43271338 0.15250870
## YIO CHU KANG WEST 0.03379906 -0.02631579 7.43271338 0.02204997
## YISHUN CENTRAL 7.57077067 -0.02631579 7.43271338 2.78659194
## YISHUN EAST 4.43231892 -0.02631579 7.43271338 1.63541584
## YISHUN SOUTH 2.32369149 -0.02631579 7.43271338 0.86197667
## YISHUN WEST 7.22975445 -0.02631579 7.43271338 2.66150806
## YUHUA EAST 0.46338463 -0.02631579 7.43271338 0.17962086
## YUHUA WEST 0.73535209 -0.02631579 7.43271338 0.27937784
## YUNNAN 3.80691103 -0.02631579 7.43271338 1.40601782
## Pr.z...0.
## ADMIRALTY 0.4169
## AIRPORT ROAD 0.7249
## ALEXANDRA HILL 0.6568
## ALEXANDRA NORTH 0.4497
## ALJUNIED 0.2973
## ANAK BUKIT 0.6305
## ANCHORVALE 0.3474
## ANG MO KIO TOWN CENTRE 0.2013
## ANSON 0.1858
## BALESTIER 0.1635
## BANGKIT 0.4944
## BAYFRONT SUBZONE 0.1341
## BAYSHORE 0.9926
## BEDOK NORTH 0.0000
## BEDOK RESERVOIR 0.0697
## BEDOK SOUTH 0.0000
## BENCOOLEN 0.2108
## BENDEMEER 0.4324
## BENOI SECTOR 0.0848
## BIDADARI 0.4604
## BISHAN EAST 0.5173
## BOAT QUAY 0.1591
## BOON KENG 0.5323
## BOON LAY PLACE 0.0500
## BOON TECK 0.5530
## BOULEVARD 0.9566
## BRADDELL 0.6191
## BRAS BASAH 0.4246
## BRICKWORKS 0.5070
## BUGIS 0.5665
## BUKIT BATOK CENTRAL 0.5728
## BUKIT BATOK EAST 0.5196
## BUKIT BATOK SOUTH 0.5055
## BUKIT BATOK WEST 0.5778
## BUKIT HO SWEE 0.3594
## BUKIT MERAH 0.5092
## CECIL 0.1775
## CENTRAL SUBZONE 0.1790
## CENTRAL WATER CATCHMENT 0.3994
## CHANGI AIRPORT 0.6188
## CHANGI POINT 0.5343
## CHANGI WEST 0.9119
## CHATSWORTH 0.2884
## CHENG SAN 0.2352
## CHIN BEE 0.9327
## CHINA SQUARE 0.2383
## CHINATOWN 0.5204
## CHOA CHU KANG CENTRAL 0.4741
## CHOA CHU KANG NORTH 0.5336
## CHONG BOON 0.0741
## CITY HALL 0.4978
## CITY TERMINALS 0.2016
## CLARKE QUAY 0.2523
## CLEMENTI CENTRAL 0.9324
## CLEMENTI NORTH 0.6124
## CLEMENTI WEST 0.4978
## CLEMENTI WOODS 0.4956
## CLIFFORD PIER 0.1656
## COMMONWEALTH 0.3468
## COMPASSVALE 0.6359
## CORONATION ROAD 0.3110
## CRAWFORD 0.4360
## DAIRY FARM 0.5044
## DEFU INDUSTRIAL PARK 0.5623
## DEPOT ROAD 0.5352
## DHOBY GHAUT 0.4708
## DOVER 0.4440
## DUNEARN 0.1700
## EAST COAST 0.8526
## EVERTON PARK 0.3373
## FABER 0.4757
## FAJAR 0.5077
## FARRER COURT 0.1902
## FARRER PARK 0.5803
## FERNVALE 0.5183
## FLORA DRIVE 0.8837
## FORT CANNING 0.2045
## FRANKEL 0.0000
## GALI BATU 0.4442
## GEYLANG BAHRU 0.5254
## GEYLANG EAST 0.0004
## GHIM MOH 0.3324
## GOMBAK 0.5766
## GOODWOOD PARK 0.2549
## GREENWOOD PARK 0.9842
## GUILIN 0.4958
## GUL BASIN 0.0804
## GUL CIRCLE 0.0954
## HENDERSON HILL 0.4765
## HILLCREST 0.1992
## HILLVIEW 0.4816
## HOLLAND DRIVE 0.3502
## HOLLAND ROAD 0.5303
## HONG KAH 0.3268
## HONG KAH NORTH 0.4962
## HOUGANG CENTRAL 0.0697
## HOUGANG EAST 0.4838
## HOUGANG WEST 0.0391
## INSTITUTION HILL 0.1617
## INTERNATIONAL BUSINESS PARK 0.5539
## ISTANA NEGARA 0.1764
## JELEBU 0.7355
## JOO KOON 0.5012
## JOO SENG 0.4502
## JURONG GATEWAY 0.5159
## JURONG PORT 0.2802
## JURONG RIVER 0.5150
## JURONG WEST CENTRAL 0.0967
## KAKI BUKIT 0.0221
## KALLANG BAHRU 0.4979
## KALLANG WAY 0.6772
## KAMPONG BUGIS 0.3650
## KAMPONG GLAM 0.2905
## KAMPONG JAVA 0.3575
## KAMPONG TIONG BAHRU 0.4524
## KAMPONG UBI 0.0234
## KANGKAR 0.4711
## KATONG 0.6946
## KEAT HONG 0.3912
## KEBUN BAHRU 0.5128
## KEMBANGAN 0.0002
## KENT RIDGE 0.3458
## KHATIB 0.2906
## KIAN TECK 0.7036
## KIM KEAT 0.5760
## KOVAN 0.3328
## KRANJI 0.7133
## LAKESIDE 0.6051
## LAVENDER 0.6763
## LEEDON PARK 0.2404
## LEONIE HILL 0.2394
## LIM CHU KANG 0.4214
## LITTLE INDIA 0.2351
## LIU FANG 0.2190
## LORONG 8 TOA PAYOH 0.4960
## LORONG AH SOO 0.0560
## LORONG CHUAN 0.7300
## LORONG HALUS 0.6750
## LOWER SELETAR 0.6906
## LOYANG EAST 0.9786
## LOYANG WEST 0.9184
## MACKENZIE 0.1790
## MACPHERSON 0.2395
## MALCOLM 0.4378
## MANDAI EAST 0.8843
## MANDAI ESTATE 0.9587
## MANDAI WEST 0.9849
## MARGARET DRIVE 0.3823
## MARINA CENTRE 0.3869
## MARINA EAST (MP) 0.4947
## MARINA SOUTH 0.1009
## MARINE PARADE 0.2917
## MARITIME SQUARE 0.6154
## MARYMOUNT 0.3038
## MATILDA 0.1399
## MAXWELL 0.1138
## MEI CHIN 0.4668
## MIDVIEW 0.2187
## MONK'S HILL 0.3311
## MOULMEIN 0.5433
## MOUNT PLEASANT 0.4720
## MOUNTBATTEN 0.6878
## NASSIM 0.3302
## NATIONAL UNIVERSITY OF S'PORE 0.4152
## NATURE RESERVE 0.4571
## NEE SOON 0.8078
## NEWTON CIRCUS 0.3286
## NORTH COAST 0.0000
## NORTHLAND 0.3207
## NORTHSHORE 0.8976
## ONE NORTH 0.4876
## ONE TREE HILL 0.2226
## ORANGE GROVE 0.1488
## OXLEY 0.1827
## PANDAN 0.5804
## PANG SUA 0.5191
## PASIR PANJANG 1 0.4530
## PASIR PANJANG 2 0.3506
## PASIR RIS CENTRAL 0.0002
## PASIR RIS DRIVE 0.0067
## PASIR RIS PARK 0.9129
## PASIR RIS WAFER FAB PARK 0.6359
## PASIR RIS WEST 0.1516
## PATERSON 0.2247
## PAYA LEBAR EAST 0.6027
## PAYA LEBAR NORTH 0.2955
## PAYA LEBAR WEST 0.4676
## PEARL'S HILL 0.3452
## PEI CHUN 0.4908
## PENG SIANG 0.4811
## PENJURU CRESCENT 0.3623
## PEOPLE'S PARK 0.4221
## PHILLIP 0.1029
## PIONEER SECTOR 0.0843
## PLAB 0.2160
## PORT 0.2918
## POTONG PASIR 0.4308
## PUNGGOL FIELD 0.3715
## PUNGGOL TOWN CENTRE 0.8126
## QUEENSWAY 0.3306
## RAFFLES PLACE 0.0805
## REDHILL 0.4800
## RESERVOIR VIEW 0.4285
## RIDOUT 0.2083
## RIVERVALE 0.0703
## ROBERTSON QUAY 0.2445
## ROCHOR CANAL 0.2885
## SAFTI 0.8152
## SAMULUN 0.0915
## SAUJANA 0.6114
## SELEGIE 0.2317
## SELETAR 0.6282
## SELETAR AEROSPACE PARK 0.3365
## SELETAR HILLS 0.5412
## SEMBAWANG CENTRAL 0.5297
## SEMBAWANG EAST 0.5427
## SEMBAWANG HILLS 0.6550
## SEMBAWANG NORTH 0.5242
## SEMBAWANG SPRINGS 0.5682
## SEMBAWANG STRAITS 0.6426
## SENGKANG TOWN CENTRE 0.0007
## SENGKANG WEST 0.1983
## SENJA 0.4820
## SENNETT 0.4197
## SENOKO NORTH 0.5625
## SENOKO SOUTH 0.6251
## SENOKO WEST 0.8808
## SENTOSA 0.2788
## SERANGOON CENTRAL 0.4684
## SERANGOON GARDEN 0.2561
## SERANGOON NORTH 0.3729
## SERANGOON NORTH IND ESTATE 0.8136
## SHANGRI-LA 0.4892
## SHIPYARD 0.0799
## SIGLAP 0.9788
## SIMEI 0.0000
## SINGAPORE GENERAL HOSPITAL 0.4320
## SINGAPORE POLYTECHNIC 0.5449
## SOMERSET 0.5534
## SPRINGLEAF 0.3070
## STRAITS VIEW 0.0499
## SUNGEI ROAD 0.3397
## SUNSET WAY 0.6357
## SWISS CLUB 0.3997
## TAGORE 0.4777
## TAI SENG 0.5015
## TAMAN JURONG 0.3139
## TAMPINES EAST 0.0000
## TAMPINES NORTH 0.9765
## TAMPINES WEST 0.0000
## TANGLIN 0.1638
## TANGLIN HALT 0.4364
## TANJONG PAGAR 0.1312
## TANJONG RHU 0.5070
## TEBAN GARDENS 0.5189
## TECK WHYE 0.4339
## TELOK BLANGAH DRIVE 0.5176
## TELOK BLANGAH RISE 0.4941
## TELOK BLANGAH WAY 0.5687
## TENGAH 0.6814
## TENGEH 0.0677
## THE WHARVES 0.2792
## TIONG BAHRU 0.4321
## TIONG BAHRU STATION 0.6903
## TOA PAYOH CENTRAL 0.3833
## TOA PAYOH WEST 0.6533
## TOH GUAN 0.4917
## TOH TUCK 0.4829
## TOWNSVILLE 0.5871
## TRAFALGAR 0.1601
## TUAS BAY 0.1571
## TUAS NORTH 0.1283
## TUAS PROMENADE 0.1271
## TUAS VIEW 0.1293
## TUAS VIEW EXTENSION 0.0704
## TUKANG 0.8260
## TURF CLUB 0.4630
## TYERSALL 0.2258
## ULU PANDAN 0.3500
## UPPER PAYA LEBAR 0.5021
## UPPER THOMSON 0.1629
## VICTORIA 0.5468
## WATERWAY EAST 0.6368
## WENYA 0.9216
## WEST COAST 0.5438
## WESTERN WATER CATCHMENT 0.2225
## WOODGROVE 0.5083
## WOODLANDS EAST 0.0167
## WOODLANDS REGIONAL CENTRE 0.0000
## WOODLANDS SOUTH 0.3284
## WOODLANDS WEST 0.0341
## WOODLEIGH 0.2677
## XILIN 0.9952
## YEW TEE 0.4781
## YIO CHU KANG 0.4960
## YIO CHU KANG EAST 0.5063
## YIO CHU KANG NORTH 0.4394
## YIO CHU KANG WEST 0.4912
## YISHUN CENTRAL 0.0027
## YISHUN EAST 0.0510
## YISHUN SOUTH 0.1944
## YISHUN WEST 0.0039
## YUHUA EAST 0.4287
## YUHUA WEST 0.3900
## YUNNAN 0.0799Mapping Local Moran’s I values and p-values for Tap Out
The red zones on the left map and the dark blue zones on the right map indicate the subzones responsible for the clusters.
new_overall.localMI2 <- cbind(new_overall, localMI2)
localMI2.map <- tm_shape(new_overall.localMI2) +
tm_fill(col = "Ii",
breaks = c(-Inf, -5, 10, Inf),
palette = "-RdBu",
title = "local Moran's I statistics") +
tm_borders(alpha = 0.5) +
tm_layout(legend.position = c("RIGHT", "BOTTOM"))
pvalue.map <- tm_shape(new_overall.localMI2) +
tm_fill(col = "Pr.z...0.",
breaks=c(-Inf, 0.05, Inf),
palette="-Blues",
title = "local Moran's I p-values") +
tm_borders(alpha = 0.5) +
tm_layout(legend.position = c("RIGHT", "BOTTOM"))
tmap_arrange(localMI2.map, pvalue.map, asp = 1, ncol = 2)
Spatially lagged Tap Out values
TAP_OUT.lag <- lag.listw(rswm_knn8, new_overall$TAP_OUT)
TAP_OUT.lag## [1] 2289401 3987285 2006909 2839789 3252319 2511011 5273948 3717216
## [9] 1490427 3731967 3069444 1053980 7480573 6000923 8476530 7436300
## [17] 1535837 3348510 779656 2869637 2993041 960415 4158088 6525117
## [25] 4158885 871048 4485587 2004077 3266874 1507921 2831454 3305803
## [33] 3533648 4316143 1987746 2234066 867905 758472 1298480 2612084
## [41] 3246823 6328602 1109803 4055098 6043145 1200377 960297 3076283
## [49] 3702263 4723559 1675889 1464052 1388630 2005633 2536968 3202036
## [57] 3214812 1139133 1809588 6461055 1513076 2066129 3074336 3582311
## [65] 3240224 1411742 2086565 1130333 4597497 1574210 2926645 3165565
## [73] 1256893 1269176 2526014 6962639 1445786 7198053 2524642 3945790
## [81] 5002051 1877199 3611569 1935935 7087524 3018788 666685 736270
## [89] 2319638 1240995 2645702 1711775 1900111 3309386 3119209 4330668
## [97] 5089233 4904078 1346952 3278334 1653962 2554244 4016379 2603492
## [105] 2992910 2025310 3124578 3364559 4804725 2315247 4366235 2235507
## [113] 2038149 1747900 2572120 6120897 3637226 6651466 4363955 3706273
## [121] 8126364 2319750 3803380 4344694 3404990 6459115 4760133 4005176
## [129] 1654008 1500370 1859121 2743038 1871402 1640662 2496402 4504089
## [137] 4194561 3724547 4404995 6432105 6335205 1301132 5103322 2567910
## [145] 5184465 5739513 6328115 2046202 2055615 3027125 1111364 3856273
## [153] 2643032 4449357 3995630 1153668 2041763 6673384 2383236 2261941
## [161] 2926796 4407032 1449739 2258204 2826650 4501019 1888181 6027175
## [169] 4888390 4981245 1727897 1668313 1441284 1607379 3413180 3121592
## [177] 1711489 2138777 5936653 5590372 6744076 3686944 6172696 1683218
## [185] 3449739 1906464 2921431 1728992 2972584 3344571 2408654 1192256
## [193] 1127507 903062 1842121 1940690 2155742 3867286 4885662 2178929
## [201] 778938 2596945 2753826 1308787 4514036 1023124 2042813 4396408
## [209] 834603 4096324 1713179 3874069 2339081 3359736 2912786 3430682
## [217] 3959943 1870218 3519213 3621888 4665647 1740761 4122890 2121365
## [225] 3457892 3871411 5864047 2111199 3077521 4318819 4948904 5005998
## [233] 4384347 859111 6957098 10146964 2512499 3478599 1401592 2180265
## [241] 556139 2141047 3651375 1948778 2922294 3164159 3407368 4972955
## [249] 8092886 9207701 1525932 1740300 1300687 3108509 2747469 4251469
## [257] 2696325 3000118 3569939 3875918 821806 2144888 2392458 2104394
## [265] 3177907 5342472 3679354 2864241 5937183 4190611 834033 822575
## [273] 824244 498919 604363 4728211 2494397 1107810 2239402 3296953
## [281] 4238076 1190261 4658062 6981215 3259042 6280100 4373834 3969435
## [289] 5400621 5481320 4540169 1746997 11036414 3157567 3034207 3098345
## [297] 2764008 2830913 4348599 5138934 5188771 4657827 3768576 4131017
## [305] 4059399new_overall$TAP_OUT_lag <- TAP_OUT.lagPlotting Tap Out values alongside Tap Out lag values
tap_out <- tm_shape(new_overall) +
tm_fill(col = "TAP_OUT",
n = 6,
style = "equal",
palette = "Reds") +
tm_borders(alpha = 0.5)+
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
tap_out_lag <- tm_shape(new_overall) +
tm_fill(col = "TAP_OUT_lag",
n = 6 ,
style = "equal",
palette = "Blues") +
tm_borders(alpha = 0.5)+
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
tmap_arrange(tap_out, tap_out_lag, asp = 1, ncol = 2)
Plotting Moran scatterplot as standardised variables
new_overall$Z.TAP_OUT <- scale(new_overall$TAP_OUT) %>% as.vector()
nci3 <- moran.plot(new_overall$Z.TAP_OUT, rswm_knn8, labels = as.character(new_overall$SUBZONE_N), xlab = "z-TAP_OUT", ylab = "Spatially Lag z-TAP_OUT")
Creating the quadrants
quadrant2 <- vector(mode="numeric",length=nrow(localMI2))
DV2 <- new_overall$TAP_OUT
C_mI2 <- new_overall$TAP_OUT_lag
signif <- 0.05
quadrant2[DV2 >0 & C_mI2>0] <- 4
quadrant2[DV2 <0 & C_mI2<0] <- 1
quadrant2[DV2 <0 & C_mI2>0] <- 2
quadrant2[DV2 >0 & C_mI2<0] <- 3
quadrant2[localMI2[,5]>signif] <- 0Mapping LISA Cluster
new_overall.localMI2$quadrant2 <- quadrant2
colors <- c("#ffffff", "#2c7bb6", "#abd9e9", "#fdae61", "#d7191c")
clusters <- c("insignificant", "low-low", "low-high", "high-low", "high-high")
tm_shape(new_overall.localMI2) +
tm_fill(col = "quadrant2", style = "cat", palette = colors[c(sort(unique(quadrant2)))+1], labels = clusters[c(sort(unique(quadrant2)))+1], popup.vars = c("SUBZONE_N")) +
tm_view(set.zoom.limits = c(11,17)) +
tm_borders(alpha=0.5)+
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
Hot Spot and Cold Spot Area Analysis
Create adaptive proximity matrix
coords <- st_centroid(new_overall)## Warning in st_centroid.sf(new_overall): st_centroid assumes attributes are
## constant over geometries of xknb2 <- knn2nb(knearneigh(coords, k = 8, longlat = FALSE), row.names = row.names(new_overall$TAP_OUT))## Warning in knearneigh(coords, k = 8, longlat = FALSE): dnearneigh: longlat
## argument overrides objectknb_lw2 <- nb2listw(knb, style = 'B')
summary(knb_lw2)## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 305
## Number of nonzero links: 2440
## Percentage nonzero weights: 2.622951
## Average number of links: 8
## Non-symmetric neighbours list
## Link number distribution:
##
## 8
## 305
## 305 least connected regions:
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 with 8 links
## 305 most connected regions:
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 with 8 links
##
## Weights style: B
## Weights constants summary:
## n nn S0 S1 S2
## B 305 93025 2440 4412 79922Plotting the subzones and their neighbours
plot(new_overall$geometry, border = "lightgrey")
plot(knb, st_centroid(new_overall$geometry), pch = 19, cex = 0.6, add = TRUE, col = "red")
Gi stats using adaptive distance
fips <- order(new_overall$SUBZONE_N)
gi.adaptive2 <- localG(new_overall$TAP_OUT, knb_lw2)
new_overall.gi2 <- cbind(new_overall, as.matrix(gi.adaptive2))
names(new_overall.gi2)[8] <- "gstat_adaptive2"Mapping out the hot and cold spots
tm_shape(new_overall.gi2) +
tm_fill(col = "gstat_adaptive2",
style = "equal",
palette = "-RdBu",
title = "local Gi") +
tm_borders(alpha = 0.5) +
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
Tap In vs Tap Out LISA Cluster Mapping
The Tap In and Tap Out maps share similar clustering patterns below
tap_in <- tm_shape(new_overall.localMI) +
tm_fill(col = "quadrant", style = "cat", palette = colors[c(sort(unique(quadrant)))+1], labels = clusters[c(sort(unique(quadrant)))+1], popup.vars = c("SUBZONE_N")) +
tm_view(set.zoom.limits = c(11,17)) +
tm_borders(alpha=0.5) +
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
tap_out <- tm_shape(new_overall.localMI2) +
tm_fill(col = "quadrant2", style = "cat", palette = colors[c(sort(unique(quadrant2)))+1], labels = clusters[c(sort(unique(quadrant2)))+1], popup.vars = c("SUBZONE_N")) +
tm_view(set.zoom.limits = c(11,17)) +
tm_borders(alpha=0.5) +
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
tmap_arrange(tap_in, tap_out, asp = 1, ncol = 2)
Tap In vs Tap Out Hot and Cold Spot Analysis
The Tap In and Tap Out maps have similar outliers as indicated by the hotspots below
tap_in <- tm_shape(new_overall.gi) +
tm_fill(col = "gstat_adaptive",
style = "pretty",
palette = "-RdBu",
title = "local Gi") +
tm_borders(alpha = 0.5) +
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
tap_out <- tm_shape(new_overall.gi2) +
tm_fill(col = "gstat_adaptive2",
style = "pretty",
palette = "-RdBu",
title = "local Gi") +
tm_borders(alpha = 0.5) +
tm_layout(legend.width = 0.5, legend.position = c("RIGHT", "BOTTOM"))
tmap_arrange(tap_in, tap_out, asp = 1, ncol = 2)