IS415_Take_Home_Exercise_01
Ng Xun Jie
9/18/2020
Objectives
In this Take-home Exercise, we are going to conduct a use-case to demonstrate the potential contribution of geospatial analytics in R to integrate, analyse and communicate teh analysis results by using open data provided by different government agencies. The specific objectives of the study are as follow:
- To gain understanding on the supply and demand of childcare and kindergarten services at planning subzone level.
- To gain understanding on the geographic distribution of childcare and kindergarten in Singapore.
Loading packages
packages = c('sp','rgdal', 'rgeos','sf', 'spdep', 'tmap', 'tidyverse','rvest','spatstat', 'raster', 'maptools')
for (p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p,character.only = T)
}## Loading required package: sp## Loading required package: rgdal## rgdal: version: 1.5-16, (SVN revision 1050)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.0.4, released 2020/01/28
## Path to GDAL shared files: C:/Users/John Ng/Documents/R/win-library/4.0/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 6.3.1, February 10th, 2020, [PJ_VERSION: 631]
## Path to PROJ shared files: C:/Users/John Ng/Documents/R/win-library/4.0/rgdal/proj
## Linking to sp version:1.4-2
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.## Loading required package: rgeos## rgeos version: 0.5-3, (SVN revision 634)
## GEOS runtime version: 3.8.0-CAPI-1.13.1
## Linking to sp version: 1.4-2
## Polygon checking: TRUE## Loading required package: sf## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1## Loading required package: spdep## Loading required package: spData## To access larger datasets in this package, install the spDataLarge
## package with: `install.packages('spDataLarge',
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## v tidyr 1.1.1 v stringr 1.4.0
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## x dplyr::filter() masks stats::filter()
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## method from
## print.boxx cli##
## spatstat 1.64-1 (nickname: 'Help you I can, yes!')
## For an introduction to spatstat, type 'beginner'##
## Note: spatstat version 1.64-1 is out of date by more than 5 months; we recommend upgrading to the latest version.## Loading required package: raster##
## Attaching package: 'raster'## The following objects are masked from 'package:spatstat':
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## extract## Loading required package: maptools## Checking rgeos availability: TRUEThe task
In this take-home exercise, you are tasked to analyse the grographic distribution of childcare and kindergarten services by using appropriate map analysis and spatial point patterns analysis techniques. The specific tasks are as follows:
Section A: The supply and demand of childcare and kindergarten services by planning subzone
1) Exploratory Spatial Data Analysis
Import shapefile
sf_mpsz <- st_read(dsn = "Data/shapefile", layer = "MP14_SUBZONE_NO_SEA_PL")## Reading layer `MP14_SUBZONE_NO_SEA_PL' from data source `D:\Users\John_Ng\Year 3 Term 1\(Monday) IS415 Geospatial Analytics & Applns (SMU-X)\Take home exercise\IS415_Take_Home_Exercise_01_NG_XUN_JIE\Data\shapefile' 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
## projected CRS: SVY21Import Sg boundary data
sg <- readOGR(dsn = 'Data/shapefile' , layer = 'CostalOutline')## OGR data source with driver: ESRI Shapefile
## Source: "D:\Users\John_Ng\Year 3 Term 1\(Monday) IS415 Geospatial Analytics & Applns (SMU-X)\Take home exercise\IS415_Take_Home_Exercise_01_NG_XUN_JIE\Data\shapefile", layer: "CostalOutline"
## with 60 features
## It has 4 fieldssummary(sg)## Object of class SpatialPolygonsDataFrame
## Coordinates:
## min max
## x 2663.926 56047.79
## y 16357.981 50244.03
## Is projected: TRUE
## proj4string :
## [+proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1
## +x_0=28001.642 +y_0=38744.572 +datum=WGS84 +units=m +no_defs]
## Data attributes:
## GDO_GID MSLINK MAPID COSTAL_NAM
## Min. : 1.00 Min. : 1.00 Min. :0 Length:60
## 1st Qu.:15.75 1st Qu.:17.75 1st Qu.:0 Class :character
## Median :30.50 Median :33.50 Median :0 Mode :character
## Mean :30.50 Mean :33.77 Mean :0
## 3rd Qu.:45.25 3rd Qu.:49.25 3rd Qu.:0
## Max. :60.00 Max. :67.00 Max. :0Import kml file
This code read the KML file as a Spatial object
sf_preschool = read_sf("data/kml/pre-schools-location-kml.kml")###Inspecting the class of sf_preschool
class(sf_preschool)## [1] "sf" "tbl_df" "tbl" "data.frame"Inspect data in tibble form
It can be seen here that the data that we would like to use like the centre name is hidden in the Description column, within a myriad of html tags.
sf_preschool %>%
glimpse()## Rows: 1,925
## Columns: 3
## $ Name <chr> "kml_1", "kml_2", "kml_3", "kml_4", "kml_5", "kml_6", "...
## $ Description <chr> "<center><table><tr><th colspan='2' align='center'><em>...
## $ geometry <POINT [°]> POINT Z (103.7009 1.338325 0), POINT Z (103.8987 ...Map out sf_preschool
plot(sf_preschool)
Get the attributes for each observation
This code chunk require rvest package find out more about that package using a simple lapply
attributes <- lapply(X = 1:nrow(sf_preschool),
FUN = function(x) {
sf_preschool %>%
slice(x) %>%
pull(Description) %>%
read_html() %>%
html_node("table") %>%
html_table(header = TRUE, trim = TRUE, dec = ".", fill = TRUE) %>%
as_tibble(.name_repair = ~ make.names(c("Attribute", "Value"))) %>%
pivot_wider(names_from = Attribute, values_from = Value)
})Bind the attributes to each observation as new columns
sf_preschool_attr <-
sf_preschool %>%
bind_cols(bind_rows(attributes)) %>%
dplyr::select(-Description)Using glimpse() function, we are able to see the different datas hidden in Description before.
sf_preschool_attr %>%
glimpse()## Rows: 1,925
## Columns: 8
## $ Name <chr> "kml_1", "kml_2", "kml_3", "kml_4", "kml_5", "kml_6", "...
## $ CENTRE_NAME <chr> "BRILLIANT TOTS PTE. LTD.", "BUBBLESLAND PLAYHOUSE PTE ...
## $ CENTRE_CODE <chr> "PT9334", "PT7680", "PT9527", "PT3150", "PT9117", "PT90...
## $ ADDRESS <chr> "610, JURONG WEST STREET 65, #01 - 534, S 640610", "238...
## $ POSTAL_CODE <chr> "640610", "540238", "737856", "730369", "542327", "8212...
## $ INC_CRC <chr> "0523C7904478A63D", "18BED05A501AA168", "C88B9AC31EE71B...
## $ FMEL_UPD_D <chr> "20200812235534", "20200812235534", "20200812235534", "...
## $ geometry <POINT [°]> POINT Z (103.7009 1.338325 0), POINT Z (103.8987 ...Finding out the contents of a SpatialDataframe
summary(sf_preschool_attr)## Name CENTRE_NAME CENTRE_CODE ADDRESS
## Length:1925 Length:1925 Length:1925 Length:1925
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## POSTAL_CODE INC_CRC FMEL_UPD_D geometry
## Length:1925 Length:1925 Length:1925 POINT Z :1925
## Class :character Class :character Class :character epsg:4326 : 0
## Mode :character Mode :character Mode :character +proj=long...: 0head(sf_preschool_attr, n=4)## Simple feature collection with 4 features and 7 fields
## geometry type: POINT
## dimension: XYZ
## bbox: xmin: 103.7009 ymin: 1.338325 xmax: 103.8987 ymax: 1.438017
## z_range: zmin: 0 zmax: 0
## geographic CRS: WGS 84
## Name CENTRE_NAME CENTRE_CODE
## 1 kml_1 BRILLIANT TOTS PTE. LTD. PT9334
## 2 kml_2 BUBBLESLAND PLAYHOUSE PTE LTD PT7680
## 3 kml_3 BUCKET HOUSE PRESCHOOL PT9527
## 4 kml_4 BUMBLE BEE CHILD CARE CENTRE PT3150
## ADDRESS POSTAL_CODE
## 1 610, JURONG WEST STREET 65, #01 - 534, S 640610 640610
## 2 238, COMPASSVALE WALK, #01 - 542, S 540238 540238
## 3 39, WOODLANDS CLOSE, #01 - 62, MEGA@WOODLANDS, S 737856 737856
## 4 369, WOODLANDS AVENUE 1, #01 - 853, S 730369 730369
## INC_CRC FMEL_UPD_D geometry
## 1 0523C7904478A63D 20200812235534 POINT Z (103.7009 1.338325 0)
## 2 18BED05A501AA168 20200812235534 POINT Z (103.8987 1.39044 0)
## 3 C88B9AC31EE71BF6 20200812235534 POINT Z (103.8068 1.438017 0)
## 4 64AB8FACA8F60129 20200812235534 POINT Z (103.7874 1.433436 0)class(sf_preschool_attr)## [1] "sf" "data.frame"Finding the differences of the childcare services + kindergartens dataset from the pre-schools location dataset
sf_kindergartens = read_sf("data/kml/kindergartens-kml.kml")
sf_childcare_services = read_sf("data/kml/child-care-services-kml.kml")
sf_kindergartens %>%
glimpse()## Rows: 448
## Columns: 3
## $ Name <chr> "kml_1", "kml_2", "kml_3", "kml_4", "kml_5", "kml_6", "...
## $ Description <chr> "<center><table><tr><th colspan='2' align='center'><em>...
## $ geometry <POINT [°]> POINT Z (103.8409 1.37915 0), POINT Z (103.7397 1...sf_childcare_services %>%
glimpse()## Rows: 1,545
## Columns: 3
## $ Name <chr> "kml_1", "kml_2", "kml_3", "kml_4", "kml_5", "kml_6", "...
## $ Description <chr> "<center><table><tr><th colspan='2' align='center'><em>...
## $ geometry <POINT [°]> POINT Z (103.8331 1.42972 0), POINT Z (103.8138 1...Since kml data is mainly stored in the Description column, and hidden between html tags, we will employ the same data extraction method from before.
Using a simple lapply
For sf_kindergartens
attributes_k <- lapply(X = 1:nrow(sf_kindergartens),
FUN = function(x) {
sf_kindergartens %>%
slice(x) %>%
pull(Description) %>%
read_html() %>%
html_node("table") %>%
html_table(header = TRUE, trim = TRUE, dec = ".", fill = TRUE) %>%
as_tibble(.name_repair = ~ make.names(c("Attribute", "Value"))) %>%
pivot_wider(names_from = Attribute, values_from = Value)
})For sf_childcare_services
attributes_c <- lapply(X = 1:nrow(sf_childcare_services),
FUN = function(x) {
sf_childcare_services %>%
slice(x) %>%
pull(Description) %>%
read_html() %>%
html_node("table") %>%
html_table(header = TRUE, trim = TRUE, dec = ".", fill = TRUE) %>%
as_tibble(.name_repair = ~ make.names(c("Attribute", "Value"))) %>%
pivot_wider(names_from = Attribute, values_from = Value)
})Bind the attributes to each observation as new columns
For sf_kindergartens
sf_kindergartens_attr <-
sf_kindergartens %>%
bind_cols(bind_rows(attributes_k)) %>%
dplyr::select(-Description)sf_kindergartens_attr %>%
glimpse()## Rows: 448
## Columns: 17
## $ Name <chr> "kml_1", "kml_2", "kml_3", "kml_4", "kml_5"...
## $ ADDRESSBLOCKHOUSENUMBER <chr> "", "", "", "", "", "", "", "", "", "", "",...
## $ ADDRESSBUILDINGNAME <chr> "", "", "", "", "", "", "", "", "", "", "",...
## $ ADDRESSFLOORNUMBER <chr> "", "", "", "", "", "", "", "", "", "", "",...
## $ ADDRESSPOSTALCODE <chr> "560644", "600251", "600317", "671455", "67...
## $ ADDRESSSTREETNAME <chr> "644 Ang Mo Kio Ave 4 #01-850 S(560644)", ...
## $ ADDRESSTYPE <chr> "", "", "", "", "", "", "", "", "", "", "",...
## $ DESCRIPTION <chr> "Kindergartens", "Kindergartens", "Kinderga...
## $ HYPERLINK <chr> "", "", "", "", "", "", "", "", "", "", "",...
## $ LANDXADDRESSPOINT <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0"...
## $ LANDYADDRESSPOINT <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0"...
## $ NAME <chr> "PCF Sparkletots Preschool @ Yio Chu Kang B...
## $ PHOTOURL <chr> "", "", "", "", "", "", "", "", "", "", "",...
## $ INC_CRC <chr> "904D106E26156265", "F735342764BD6BCC", "56...
## $ FMEL_UPD_D <chr> "20200813015028", "20200813015028", "202008...
## $ ADDRESSUNITNUMBER <chr> "", "", "", "", "", "", "", "", "", "", "",...
## $ geometry <POINT [°]> POINT Z (103.8409 1.37915 0), POINT Z...For sf_childcare_services
sf_childcare_services_attr <-
sf_childcare_services %>%
bind_cols(bind_rows(attributes_c)) %>%
dplyr::select(-Description)sf_childcare_services_attr %>%
glimpse()## Rows: 1,545
## Columns: 17
## $ Name <chr> "kml_1", "kml_2", "kml_3", "kml_4", "kml_5"...
## $ ADDRESSBLOCKHOUSENUMBER <chr> "", "", "", "", "", "", "", "", "", "", "",...
## $ ADDRESSBUILDINGNAME <chr> "", "", "", "", "", "", "", "", "", "", "",...
## $ ADDRESSPOSTALCODE <chr> "760742", "159053", "556912", "569139", "46...
## $ ADDRESSSTREETNAME <chr> "742, YISHUN AVENUE 5, #01 - 470, SINGAPORE...
## $ ADDRESSTYPE <chr> "", "", "", "", "", "", "", "", "", "", "",...
## $ DESCRIPTION <chr> "Child Care Services", "Child Care Services...
## $ HYPERLINK <chr> "", "", "", "", "", "", "", "", "", "", "",...
## $ LANDXADDRESSPOINT <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0"...
## $ LANDYADDRESSPOINT <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0"...
## $ NAME <chr> "AVERBEL CHILD DEVELOPMENT CENTRE PTE LTD",...
## $ PHOTOURL <chr> "", "", "", "", "", "", "", "", "", "", "",...
## $ ADDRESSFLOORNUMBER <chr> "", "", "", "", "", "", "", "", "", "", "",...
## $ INC_CRC <chr> "AEA27114446235CE", "86B24416FB1663C6", "F9...
## $ FMEL_UPD_D <chr> "20200826094036", "20200826094036", "202008...
## $ ADDRESSUNITNUMBER <chr> "", "", "", "", "", "", "", "", "", "", "",...
## $ geometry <POINT [°]> POINT Z (103.8331 1.42972 0), POINT Z...Import csv file
popdata <- read_csv("data/attributes/respopagesextod2011to2019.csv")
popdata## # A tibble: 883,728 x 7
## PA SZ AG Sex TOD Pop Time
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males HDB 1- and 2-Room Flats 0 2011
## 2 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males HDB 3-Room Flats 10 2011
## 3 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males HDB 4-Room Flats 30 2011
## 4 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males HDB 5-Room and Executive~ 50 2011
## 5 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males HUDC Flats (excluding th~ 0 2011
## 6 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males Landed Properties 0 2011
## 7 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males Condominiums and Other A~ 40 2011
## 8 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Males Others 0 2011
## 9 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Femal~ HDB 1- and 2-Room Flats 0 2011
## 10 Ang Mo K~ Ang Mo Kio Town~ 0_to~ Femal~ HDB 3-Room Flats 10 2011
## # ... with 883,718 more rowsdistinct(popdata, AG)## # A tibble: 19 x 1
## AG
## <chr>
## 1 0_to_4
## 2 5_to_9
## 3 10_to_14
## 4 15_to_19
## 5 20_to_24
## 6 25_to_29
## 7 30_to_34
## 8 35_to_39
## 9 40_to_44
## 10 45_to_49
## 11 50_to_54
## 12 55_to_59
## 13 60_to_64
## 14 65_to_69
## 15 70_to_74
## 16 75_to_79
## 17 80_to_84
## 18 85_to_89
## 19 90_and_overData Preparation for population data
popdata2019 <- popdata %>%
filter(Time == 2019) %>%
group_by(PA,SZ,AG)%>%
summarise(`POP` = sum(`Pop`)) %>%
ungroup() %>%
spread(AG, POP)%>%
dplyr::select(1:3, "5_to_9", everything()) %>% # rearrange the order of the spreaded AG column
mutate(`5_to_6_YO` = `5_to_9`/5 * 2 ) %>% # assuming that all age group follows uniform distribution, calculate the number of 5 and 6 years old
mutate(`Num_children_under_6` = rowSums(.[3]) + rowSums(.[22])) %>% # age is from 0 to 6 years old
mutate(`4_YO` = `0_to_4`/5) %>% # this is column 24
mutate(`Num_children_4_to_6` = rowSums(.[24]) + rowSums(.[22])) %>% # age is from 4 to 6 years old
dplyr::select(`PA`, `SZ`, , `0_to_4`, `5_to_9`, `5_to_6_YO`,`Num_children_under_6`, `4_YO`, `Num_children_4_to_6`)## `summarise()` regrouping output by 'PA', 'SZ' (override with `.groups` argument)Create Georelation join
Using planning subzone name as unique identifier
popdata2019 <-popdata2019 %>%
mutate_at(.vars = vars(PA,SZ),
.funs = funs(toupper)) ## Warning: `funs()` is deprecated as of dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.mpszpop2019 <- left_join(sf_mpsz,popdata2019, by = c("SUBZONE_N" = "SZ"))tm_shape(mpszpop2019) +
tm_fill("Num_children_under_6",
n= 6,
style = "quantile",
palette = "Reds") +
tm_layout(main.title = "Distribution of Children under the age of 6",
main.title.position = "center",
main.title.size = 1.2,
legend.height = 0.45,
legend.width = 0.35,
frame = TRUE) +
tm_borders(alpha = 0.5) +
tm_compass(type = "8star", size = 2) +
tm_scale_bar(width = 0.15) +
tm_grid(lwd = 0.5, alpha = 0.5 )
top_n(mpszpop2019, 5, "Num_children_under_6")## Simple feature collection with 323 features and 22 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
## projected CRS: SVY21
## First 10 features:
## OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND PLN_AREA_N
## 1 1 2 PEOPLE'S PARK OTSZ02 Y OUTRAM
## 2 2 2 BUKIT MERAH BMSZ02 N BUKIT MERAH
## 3 3 3 CHINATOWN OTSZ03 Y OUTRAM
## 4 4 4 PHILLIP DTSZ04 Y DOWNTOWN CORE
## 5 5 5 RAFFLES PLACE DTSZ05 Y DOWNTOWN CORE
## 6 6 4 CHINA SQUARE OTSZ04 Y OUTRAM
## 7 7 10 TIONG BAHRU BMSZ10 N BUKIT MERAH
## 8 8 12 BAYFRONT SUBZONE DTSZ12 Y DOWNTOWN CORE
## 9 9 4 TIONG BAHRU STATION BMSZ04 N BUKIT MERAH
## 10 10 6 CLIFFORD PIER DTSZ06 Y DOWNTOWN CORE
## PLN_AREA_C REGION_N REGION_C INC_CRC FMEL_UPD_D X_ADDR
## 1 OT CENTRAL REGION CR B4120D23006C932A 2016-05-11 28831.78
## 2 BM CENTRAL REGION CR 1C51019439A68700 2016-05-11 26360.80
## 3 OT CENTRAL REGION CR 0FF1661344C84AED 2016-05-11 29153.97
## 4 DT CENTRAL REGION CR 615D4EDDEF809F8E 2016-05-11 29706.72
## 5 DT CENTRAL REGION CR 72107B11807074F4 2016-05-11 29968.62
## 6 OT CENTRAL REGION CR B609DF5587626C8F 2016-05-11 29509.64
## 7 BM CENTRAL REGION CR A0FB4B68155D164A 2016-05-11 27785.67
## 8 DT CENTRAL REGION CR 197F5E664DA4D5E1 2016-05-11 30806.24
## 9 BM CENTRAL REGION CR 91FFE927ABE3E4DB 2016-05-11 27277.47
## 10 DT CENTRAL REGION CR 945CC212CA80626F 2016-05-11 30379.50
## Y_ADDR SHAPE_Leng SHAPE_Area PA 0_to_4 5_to_9 5_to_6_YO
## 1 29419.65 1822.1927 93140.44 OUTRAM 0 0 0
## 2 29384.14 3074.9632 411722.82 BUKIT MERAH 20 40 16
## 3 29158.04 4297.5999 587222.68 OUTRAM 480 630 252
## 4 29744.91 871.5549 39437.94 DOWNTOWN CORE 0 0 0
## 5 29572.76 1872.7522 188767.49 DOWNTOWN CORE 0 0 0
## 6 29646.45 1605.2797 133006.94 OUTRAM 20 30 12
## 7 29590.40 3303.2149 448127.58 BUKIT MERAH 760 670 268
## 8 29530.17 2897.1264 521200.52 DOWNTOWN CORE 0 0 0
## 9 29607.02 2506.6879 350787.56 BUKIT MERAH 600 770 308
## 10 29776.43 2405.9909 261843.90 DOWNTOWN CORE 0 0 0
## Num_children_under_6 4_YO Num_children_4_to_6 geometry
## 1 0 0 0 MULTIPOLYGON (((29099.02 29...
## 2 36 4 20 MULTIPOLYGON (((26750.09 29...
## 3 732 96 348 MULTIPOLYGON (((29161.2 297...
## 4 0 0 0 MULTIPOLYGON (((29814.11 29...
## 5 0 0 0 MULTIPOLYGON (((30137.77 29...
## 6 32 4 16 MULTIPOLYGON (((29699.44 29...
## 7 1028 152 420 MULTIPOLYGON (((27748.04 29...
## 8 0 0 0 MULTIPOLYGON (((30844.87 29...
## 9 908 120 428 MULTIPOLYGON (((27444.04 29...
## 10 0 0 0 MULTIPOLYGON (((30436.73 29...ggplot(data=mpszpop2019,
aes(x = "",
y = Num_children_under_6)) +
geom_boxplot()
### Work with projection Check the projection of sf_mpsz.
st_crs(sf_mpsz)## Coordinate Reference System:
## User input: SVY21
## wkt:
## PROJCRS["SVY21",
## BASEGEOGCRS["SVY21[WGS84]",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]],
## ID["EPSG",6326]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["Degree",0.0174532925199433]]],
## CONVERSION["unnamed",
## METHOD["Transverse Mercator",
## ID["EPSG",9807]],
## PARAMETER["Latitude of natural origin",1.36666666666667,
## ANGLEUNIT["Degree",0.0174532925199433],
## ID["EPSG",8801]],
## PARAMETER["Longitude of natural origin",103.833333333333,
## ANGLEUNIT["Degree",0.0174532925199433],
## ID["EPSG",8802]],
## PARAMETER["Scale factor at natural origin",1,
## SCALEUNIT["unity",1],
## ID["EPSG",8805]],
## PARAMETER["False easting",28001.642,
## LENGTHUNIT["metre",1],
## ID["EPSG",8806]],
## PARAMETER["False northing",38744.572,
## LENGTHUNIT["metre",1],
## ID["EPSG",8807]]],
## CS[Cartesian,2],
## AXIS["(E)",east,
## ORDER[1],
## LENGTHUNIT["metre",1,
## ID["EPSG",9001]]],
## AXIS["(N)",north,
## ORDER[2],
## LENGTHUNIT["metre",1,
## ID["EPSG",9001]]]]sf_mpsz3414 <- st_set_crs(sf_mpsz, 3414)## Warning: st_crs<- : replacing crs does not reproject data; use st_transform for
## thatst_crs(sf_mpsz3414)## Coordinate Reference System:
## User input: EPSG:3414
## wkt:
## PROJCRS["SVY21 / Singapore TM",
## BASEGEOGCRS["SVY21",
## DATUM["SVY21",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4757]],
## CONVERSION["Singapore Transverse Mercator",
## METHOD["Transverse Mercator",
## ID["EPSG",9807]],
## PARAMETER["Latitude of natural origin",1.36666666666667,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8801]],
## PARAMETER["Longitude of natural origin",103.833333333333,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8802]],
## PARAMETER["Scale factor at natural origin",1,
## SCALEUNIT["unity",1],
## ID["EPSG",8805]],
## PARAMETER["False easting",28001.642,
## LENGTHUNIT["metre",1],
## ID["EPSG",8806]],
## PARAMETER["False northing",38744.572,
## LENGTHUNIT["metre",1],
## ID["EPSG",8807]]],
## CS[Cartesian,2],
## AXIS["northing (N)",north,
## ORDER[1],
## LENGTHUNIT["metre",1]],
## AXIS["easting (E)",east,
## ORDER[2],
## LENGTHUNIT["metre",1]],
## USAGE[
## SCOPE["unknown"],
## AREA["Singapore"],
## BBOX[1.13,103.59,1.47,104.07]],
## ID["EPSG",3414]]Transforming the projection from wgs81 to svy21
Check the projection of sf_preschool_attr
st_crs(sf_preschool_attr)## Coordinate Reference System:
## User input: WGS 84
## wkt:
## GEOGCRS["WGS 84",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["geodetic latitude (Lat)",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["geodetic longitude (Lon)",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4326]]Transform sf_preschool_attr simple feature dataframe onto svy21 projected coordinate system (i.e. EPSG 3414)
sf_preschool_attr3414 <- st_transform(sf_preschool_attr, 3414)
st_crs(sf_preschool_attr3414)## Coordinate Reference System:
## User input: EPSG:3414
## wkt:
## PROJCRS["SVY21 / Singapore TM",
## BASEGEOGCRS["SVY21",
## DATUM["SVY21",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4757]],
## CONVERSION["Singapore Transverse Mercator",
## METHOD["Transverse Mercator",
## ID["EPSG",9807]],
## PARAMETER["Latitude of natural origin",1.36666666666667,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8801]],
## PARAMETER["Longitude of natural origin",103.833333333333,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8802]],
## PARAMETER["Scale factor at natural origin",1,
## SCALEUNIT["unity",1],
## ID["EPSG",8805]],
## PARAMETER["False easting",28001.642,
## LENGTHUNIT["metre",1],
## ID["EPSG",8806]],
## PARAMETER["False northing",38744.572,
## LENGTHUNIT["metre",1],
## ID["EPSG",8807]]],
## CS[Cartesian,2],
## AXIS["northing (N)",north,
## ORDER[1],
## LENGTHUNIT["metre",1]],
## AXIS["easting (E)",east,
## ORDER[2],
## LENGTHUNIT["metre",1]],
## USAGE[
## SCOPE["unknown"],
## AREA["Singapore"],
## BBOX[1.13,103.59,1.47,104.07]],
## ID["EPSG",3414]]Point-in-polygon count
Finding out numbers of pre-school in each Planning Subzone using st_intersects(). Then, the length() is used to calculate numbers of pre-school fall inside each planning subzone.
sf_mpsz3414$`PreSch Count`<- lengths(st_intersects(sf_mpsz3414, sf_preschool_attr3414))Summary statistics of the newly derived PreSch Count field by using summary() are as shown in the code chunk below.
summary(sf_mpsz3414$`PreSch Count`)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 3.00 5.96 9.00 58.00To list the planning subzone with the most number of of pre-school, the top_n() of dplyr package is used as shown in the code chunk below.
top_n(sf_mpsz3414, 1, `PreSch Count`)## Simple feature collection with 1 feature and 16 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 39655.33 ymin: 35966 xmax: 42940.57 ymax: 38622.37
## projected CRS: SVY21 / Singapore TM
## OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND PLN_AREA_N PLN_AREA_C
## 1 142 2 TAMPINES EAST TMSZ02 N TAMPINES TM
## REGION_N REGION_C INC_CRC FMEL_UPD_D X_ADDR Y_ADDR SHAPE_Leng
## 1 EAST REGION ER 21658EAAF84F4D8D 2016-05-11 41122.55 37392.39 10180.62
## SHAPE_Area geometry PreSch Count
## 1 4339824 MULTIPOLYGON (((42196.76 38... 58tmap_mode("plot")## tmap mode set to plottingtm_shape(sf_mpsz3414)+
tm_bubbles(col = "red",
size = 1,
border.col = "black",
border.lwd = 1)
Create Georelation join
Using planning subzone name as unique identifier
popdata2019 <-popdata2019 %>%
mutate_at(.vars = vars(PA,SZ),
.funs = funs(toupper))
sf_mpsz3414pop2019 <- left_join(sf_mpsz3414,popdata2019, by = c("SUBZONE_N" = "SZ"))tmap_mode("plot")## tmap mode set to plotting## tmap mode set to plotting
tm1 <- tm_shape(sf_mpsz3414pop2019) +
tm_fill("Num_children_under_6",
n= 6,
style = "equal",
palette = "Reds") +
tm_layout(main.title = "Distribution of Children under the age of 6",
main.title.position = "center",
main.title.size = 1.2,
legend.height = 0.45,
legend.width = 0.35,
frame = TRUE) +
tm_borders(alpha = 0.5) +
tm_compass(type = "8star", size = 2) +
tm_scale_bar(width = 0.15) +
tm_grid(lwd = 0.5, alpha = 0.5 )
tm2 <- tm_shape(sf_mpsz3414pop2019) + tm_bubbles(size = "PreSch Count")
tmap_arrange(tm1, tm2, ncol=2)
Description: According to the pre-school count that we have generated, it can be assumed that pre-schools supply in individual subzone seems to be proportional with the number of children aged below 7, where subzones with higher number of children under the age of 7 contain higher number of pre-school facilities.
2) Analytics mapping
Work with projection Transforming the projection from wgs81 to svy21
Check the projection of sf_preschool_attr
st_crs(sf_kindergartens_attr)## Coordinate Reference System:
## User input: WGS 84
## wkt:
## GEOGCRS["WGS 84",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["geodetic latitude (Lat)",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["geodetic longitude (Lon)",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4326]]st_crs(sf_childcare_services_attr)## Coordinate Reference System:
## User input: WGS 84
## wkt:
## GEOGCRS["WGS 84",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["geodetic latitude (Lat)",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["geodetic longitude (Lon)",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4326]]Transform sf_kindergartens_attr and sf_childcare_services_attr simple feature dataframes onto svy21 projected coordinate system (i.e. EPSG 3414)
sf_kindergartens_attr3414 <- st_transform(sf_kindergartens_attr, 3414)
st_crs(sf_kindergartens_attr3414)## Coordinate Reference System:
## User input: EPSG:3414
## wkt:
## PROJCRS["SVY21 / Singapore TM",
## BASEGEOGCRS["SVY21",
## DATUM["SVY21",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4757]],
## CONVERSION["Singapore Transverse Mercator",
## METHOD["Transverse Mercator",
## ID["EPSG",9807]],
## PARAMETER["Latitude of natural origin",1.36666666666667,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8801]],
## PARAMETER["Longitude of natural origin",103.833333333333,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8802]],
## PARAMETER["Scale factor at natural origin",1,
## SCALEUNIT["unity",1],
## ID["EPSG",8805]],
## PARAMETER["False easting",28001.642,
## LENGTHUNIT["metre",1],
## ID["EPSG",8806]],
## PARAMETER["False northing",38744.572,
## LENGTHUNIT["metre",1],
## ID["EPSG",8807]]],
## CS[Cartesian,2],
## AXIS["northing (N)",north,
## ORDER[1],
## LENGTHUNIT["metre",1]],
## AXIS["easting (E)",east,
## ORDER[2],
## LENGTHUNIT["metre",1]],
## USAGE[
## SCOPE["unknown"],
## AREA["Singapore"],
## BBOX[1.13,103.59,1.47,104.07]],
## ID["EPSG",3414]]sf_childcare_services_attr3414 <- st_transform(sf_childcare_services_attr, 3414)
st_crs(sf_childcare_services_attr3414)## Coordinate Reference System:
## User input: EPSG:3414
## wkt:
## PROJCRS["SVY21 / Singapore TM",
## BASEGEOGCRS["SVY21",
## DATUM["SVY21",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4757]],
## CONVERSION["Singapore Transverse Mercator",
## METHOD["Transverse Mercator",
## ID["EPSG",9807]],
## PARAMETER["Latitude of natural origin",1.36666666666667,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8801]],
## PARAMETER["Longitude of natural origin",103.833333333333,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8802]],
## PARAMETER["Scale factor at natural origin",1,
## SCALEUNIT["unity",1],
## ID["EPSG",8805]],
## PARAMETER["False easting",28001.642,
## LENGTHUNIT["metre",1],
## ID["EPSG",8806]],
## PARAMETER["False northing",38744.572,
## LENGTHUNIT["metre",1],
## ID["EPSG",8807]]],
## CS[Cartesian,2],
## AXIS["northing (N)",north,
## ORDER[1],
## LENGTHUNIT["metre",1]],
## AXIS["easting (E)",east,
## ORDER[2],
## LENGTHUNIT["metre",1]],
## USAGE[
## SCOPE["unknown"],
## AREA["Singapore"],
## BBOX[1.13,103.59,1.47,104.07]],
## ID["EPSG",3414]]Point-in-polygon count
Finding out numbers of childcare and kindergarten services in each Planning Subzone using st_intersects(). Then, the length() is used to calculate numbers of pre-school fall inside each planning subzone.
sf_mpsz3414$`Childcare Count`<- lengths(st_intersects(sf_mpsz3414, sf_childcare_services_attr3414))
sf_mpsz3414$`Kindergarten Count`<- lengths(st_intersects(sf_mpsz3414, sf_kindergartens_attr3414))Summary statistics of the newly derived PreSch Count field by using summary() are as shown in the code chunk below.
summary(sf_mpsz3414$`Childcare Count`)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 3.000 4.783 7.500 42.000summary(sf_mpsz3414$`Kindergarten Count`)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 1.000 1.387 2.000 17.000To list the planning subzone with the most number of of pre-school, the top_n() of dplyr package is used as shown in the code chunk below.
top_n(sf_mpsz3414, 5, `Childcare Count`)## Simple feature collection with 6 features and 18 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 23449.05 ymin: 33973.71 xmax: 42940.57 ymax: 47996.47
## projected CRS: SVY21 / Singapore TM
## OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND PLN_AREA_N
## 1 142 2 TAMPINES EAST TMSZ02 N TAMPINES
## 2 167 3 TAMPINES WEST TMSZ03 N TAMPINES
## 3 173 4 BEDOK NORTH BDSZ04 N BEDOK
## 4 285 3 WOODLANDS EAST WDSZ03 N WOODLANDS
## 5 292 2 RIVERVALE SESZ02 N SENGKANG
## 6 314 3 SENGKANG TOWN CENTRE SESZ03 N SENGKANG
## PLN_AREA_C REGION_N REGION_C INC_CRC FMEL_UPD_D X_ADDR
## 1 TM EAST REGION ER 21658EAAF84F4D8D 2016-05-11 41122.55
## 2 TM EAST REGION ER 2E3EA3D1BBF9A601 2016-05-11 39421.66
## 3 BD EAST REGION ER A2254301F85C1EDF 2016-05-11 39429.21
## 4 WD NORTH REGION NR C90769E43EE6B0F2 2016-05-11 24506.64
## 5 SE NORTH-EAST REGION NER 986666487FF7CF78 2016-05-11 35977.61
## 6 SE NORTH-EAST REGION NER 5A2D0E9E6B285069 2016-05-11 35163.81
## Y_ADDR SHAPE_Leng SHAPE_Area geometry PreSch Count
## 1 37392.39 10180.624 4339824 MULTIPOLYGON (((42196.76 38... 58
## 2 36739.21 8058.336 3475210 MULTIPOLYGON (((39918.43 35... 30
## 3 34737.62 8414.962 3203663 MULTIPOLYGON (((40284.24 35... 31
## 4 46991.63 6603.608 2553464 MULTIPOLYGON (((24786.75 46... 47
## 5 41060.80 6315.954 1569035 MULTIPOLYGON (((37015.07 40... 26
## 6 41501.14 5216.401 1455508 MULTIPOLYGON (((35615.75 40... 30
## Childcare Count Kindergarten Count
## 1 42 17
## 2 23 9
## 3 25 7
## 4 42 5
## 5 23 4
## 6 27 3top_n(sf_mpsz3414, 5, `Kindergarten Count`)## Simple feature collection with 5 features and 18 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 32605.74 ymin: 31587.44 xmax: 42940.57 ymax: 40973.79
## projected CRS: SVY21 / Singapore TM
## OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND PLN_AREA_N PLN_AREA_C
## 1 93 2 KATONG MPSZ02 N MARINE PARADE MP
## 2 117 4 ALJUNIED GLSZ04 N GEYLANG GL
## 3 121 5 FRANKEL BDSZ05 N BEDOK BD
## 4 142 2 TAMPINES EAST TMSZ02 N TAMPINES TM
## 5 233 2 PASIR RIS WEST PRSZ02 N PASIR RIS PR
## REGION_N REGION_C INC_CRC FMEL_UPD_D X_ADDR Y_ADDR
## 1 CENTRAL REGION CR 55705659E2A91D11 2016-05-11 35680.90 32176.35
## 2 CENTRAL REGION CR 83AFAB768B6B2B66 2016-05-11 33592.58 32970.83
## 3 EAST REGION ER B34F041CC4B050EC 2016-05-11 37694.55 33007.27
## 4 EAST REGION ER 21658EAAF84F4D8D 2016-05-11 41122.55 37392.39
## 5 EAST REGION ER 504681216FE2A24B 2016-05-11 39851.41 40140.22
## SHAPE_Leng SHAPE_Area geometry PreSch Count
## 1 5062.815 1078992 MULTIPOLYGON (((36317.74 32... 15
## 2 7100.699 2959368 MULTIPOLYGON (((34449.13 33... 30
## 3 8750.386 4297141 MULTIPOLYGON (((36551.53 31... 27
## 4 10180.624 4339824 MULTIPOLYGON (((42196.76 38... 58
## 5 5128.495 1583440 MULTIPOLYGON (((40016.02 39... 18
## Childcare Count Kindergarten Count
## 1 9 11
## 2 22 10
## 3 18 10
## 4 42 17
## 5 11 10tmap_mode("plot")## tmap mode set to plottingtm_child <- tm_shape(sf_mpsz3414)+
tm_bubbles(size = "Childcare Count",
col = "red",
#size = 1,
border.col = "black",
border.lwd = 1)
tm_kindergarten <- tm_shape(sf_mpsz3414)+
tm_bubbles( size = "Kindergarten Count",
col = "blue",
#size = 1,
border.col = "black",
border.lwd = 1)
tmap_arrange(tm_child, tm_kindergarten)
3) Geocommunication
Tampines East has the highest number of Chilcare and Kindergarten count as compared to any other subzones. However, preliminary analysis on the number of child below 6 years old shows that Tiong Bahru and Tiong Bahru Station subzones has the highest population of children below 6 years old. This could mean that either the parents of the child actually sends their children to other subzones to acquire childcare and kindergarten services or the land scarce Tiong Bahru subzone which is near the Central Business District do not have enough land space for such facilities to be built.
Section B: Spatial Point Pattern Analysis
4) Exploratory Spatial Data Analtysis
Changing projection of SG coastal outline data
sg <- spTransform(sg, CRS("+init=EPSG:3414"))## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO"): Discarded
## datum SVY21 in CRS definitionConverting point feature data.frame to SpatialPointsDataFrame
sp_preschool <- as_Spatial(sf_preschool_attr3414)## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO"): Discarded datum Unknown based on WGS84 ellipsoid in CRS definition,
## but +towgs84= values preservedsp_chilcare <-as_Spatial(sf_childcare_services_attr3414)## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO"): Discarded datum Unknown based on WGS84 ellipsoid in CRS definition,
## but +towgs84= values preservedsp_kindergarten <- as_Spatial(sf_kindergartens_attr3414)## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO"): Discarded datum Unknown based on WGS84 ellipsoid in CRS definition,
## but +towgs84= values preservedsp_mpsz <- as_Spatial(sf_mpsz3414)## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO"): Discarded datum Unknown based on WGS84 ellipsoid in CRS definition,
## but +towgs84= values preservedsummary(sp_preschool)## Object of class SpatialPointsDataFrame
## Coordinates:
## min max
## coords.x1 11203.01 45404.24
## coords.x2 25596.33 49300.88
## coords.x3 0.00 0.00
## Is projected: TRUE
## proj4string :
## [+proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1
## +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0
## +units=m +no_defs]
## Number of points: 1925
## Data attributes:
## Name CENTRE_NAME CENTRE_CODE ADDRESS
## Length:1925 Length:1925 Length:1925 Length:1925
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## POSTAL_CODE INC_CRC FMEL_UPD_D
## Length:1925 Length:1925 Length:1925
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :charactersummary(sp_chilcare)## Object of class SpatialPointsDataFrame
## Coordinates:
## min max
## coords.x1 11203.01 45404.24
## coords.x2 25667.60 49300.88
## coords.x3 0.00 0.00
## Is projected: TRUE
## proj4string :
## [+proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1
## +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0
## +units=m +no_defs]
## Number of points: 1545
## Data attributes:
## Name ADDRESSBLOCKHOUSENUMBER ADDRESSBUILDINGNAME
## Length:1545 Length:1545 Length:1545
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
## ADDRESSPOSTALCODE ADDRESSSTREETNAME ADDRESSTYPE DESCRIPTION
## Length:1545 Length:1545 Length:1545 Length:1545
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## HYPERLINK LANDXADDRESSPOINT LANDYADDRESSPOINT NAME
## Length:1545 Length:1545 Length:1545 Length:1545
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## PHOTOURL ADDRESSFLOORNUMBER INC_CRC FMEL_UPD_D
## Length:1545 Length:1545 Length:1545 Length:1545
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## ADDRESSUNITNUMBER
## Length:1545
## Class :character
## Mode :charactersummary(sp_kindergarten)## Object of class SpatialPointsDataFrame
## Coordinates:
## min max
## coords.x1 11909.70 43395.47
## coords.x2 25596.33 48562.06
## coords.x3 0.00 0.00
## Is projected: TRUE
## proj4string :
## [+proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1
## +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0
## +units=m +no_defs]
## Number of points: 448
## Data attributes:
## Name ADDRESSBLOCKHOUSENUMBER ADDRESSBUILDINGNAME
## Length:448 Length:448 Length:448
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
## ADDRESSFLOORNUMBER ADDRESSPOSTALCODE ADDRESSSTREETNAME ADDRESSTYPE
## Length:448 Length:448 Length:448 Length:448
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## DESCRIPTION HYPERLINK LANDXADDRESSPOINT LANDYADDRESSPOINT
## Length:448 Length:448 Length:448 Length:448
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## NAME PHOTOURL INC_CRC FMEL_UPD_D
## Length:448 Length:448 Length:448 Length:448
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## ADDRESSUNITNUMBER
## Length:448
## Class :character
## Mode :charactersummary(sp_mpsz)## Object of class SpatialPolygonsDataFrame
## Coordinates:
## min max
## x 2667.538 56396.44
## y 15748.721 50256.33
## Is projected: TRUE
## proj4string :
## [+proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1
## +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0
## +units=m +no_defs]
## Data attributes:
## OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C
## Min. : 1.0 Min. : 1.000 Length:323 Length:323
## 1st Qu.: 81.5 1st Qu.: 2.000 Class :character Class :character
## Median :162.0 Median : 4.000 Mode :character Mode :character
## Mean :162.0 Mean : 4.625
## 3rd Qu.:242.5 3rd Qu.: 6.500
## Max. :323.0 Max. :17.000
## CA_IND PLN_AREA_N PLN_AREA_C REGION_N
## Length:323 Length:323 Length:323 Length:323
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## REGION_C INC_CRC FMEL_UPD_D X_ADDR
## Length:323 Length:323 Min. :2016-05-11 Min. : 5093
## Class :character Class :character 1st Qu.:2016-05-11 1st Qu.:21864
## Mode :character Mode :character Median :2016-05-11 Median :28465
## Mean :2016-05-11 Mean :27257
## 3rd Qu.:2016-05-11 3rd Qu.:31674
## Max. :2016-05-11 Max. :50425
## Y_ADDR SHAPE_Leng SHAPE_Area PreSch.Count
## Min. :19579 Min. : 871.5 Min. : 39438 Min. : 0.00
## 1st Qu.:31776 1st Qu.: 3709.6 1st Qu.: 628261 1st Qu.: 0.00
## Median :35113 Median : 5211.9 Median : 1229894 Median : 3.00
## Mean :36106 Mean : 6524.4 Mean : 2420882 Mean : 5.96
## 3rd Qu.:39869 3rd Qu.: 6942.6 3rd Qu.: 2106483 3rd Qu.: 9.00
## Max. :49553 Max. :68083.9 Max. :69748299 Max. :58.00
## Childcare.Count Kindergarten.Count
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 3.000 Median : 1.000
## Mean : 4.783 Mean : 1.387
## 3rd Qu.: 7.500 3rd Qu.: 2.000
## Max. :42.000 Max. :17.000sp_chilcare## class : SpatialPointsDataFrame
## features : 1545
## extent : 11203.01, 45404.24, 25667.6, 49300.88 (xmin, xmax, ymin, ymax)
## crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
## variables : 16
## names : Name, ADDRESSBLOCKHOUSENUMBER, ADDRESSBUILDINGNAME, ADDRESSPOSTALCODE, ADDRESSSTREETNAME, ADDRESSTYPE, DESCRIPTION, HYPERLINK, LANDXADDRESSPOINT, LANDYADDRESSPOINT, NAME, PHOTOURL, ADDRESSFLOORNUMBER, INC_CRC, FMEL_UPD_D, ...
## min values : kml_1, , , 018989, 1 & 3, Stratton Road, SINGAPORE 806787, , , , 0, 0, 3-IN-1 FAMILY CENTRE, , , 00A958622500BF89, 20200812221033, ...
## max values : kml_999, , , 829646, UPPER BASEMENT LEVEL, WEST WING, TERMINAL 1, SINGAPORE CHANGI AIRPORT, SINGAPORE 819642, , Child Care Services, , 0, 0, ZEE SCHOOLHOUSE PTE LTD, , , FFCFA88A8CE5665A, 20200826094036, ...Convert Spatial Point DataFrame to Generic sp format
preschool_sp <- as(sp_preschool, "SpatialPoints")
childcare_sp <- as(sp_chilcare, "SpatialPoints")
kindergarten_sp <- as(sp_kindergarten, 'SpatialPoints')
sg_sp <- as(sg, "SpatialPolygons")Converting the generic sp format into spatstat’s ppp format
preschool_ppp <- as(preschool_sp, "ppp")
childcare_ppp <- as(childcare_sp, "ppp")
kindergarten_ppp <- as(kindergarten_sp, "ppp")Examining the summary of the ppp objects
summary(preschool_ppp)## Planar point pattern: 1925 points
## Average intensity 2.374419e-06 points per square unit
##
## *Pattern contains duplicated points*
##
## Coordinates are given to 3 decimal places
## i.e. rounded to the nearest multiple of 0.001 units
##
## Window: rectangle = [11203.01, 45404.24] x [25596.33, 49300.88] units
## (34200 x 23700 units)
## Window area = 810725000 square unitssummary(childcare_ppp)## Planar point pattern: 1545 points
## Average intensity 1.91145e-06 points per square unit
##
## *Pattern contains duplicated points*
##
## Coordinates are given to 3 decimal places
## i.e. rounded to the nearest multiple of 0.001 units
##
## Window: rectangle = [11203.01, 45404.24] x [25667.6, 49300.88] units
## (34200 x 23630 units)
## Window area = 808287000 square unitssummary(kindergarten_ppp)## Planar point pattern: 448 points
## Average intensity 6.195602e-07 points per square unit
##
## *Pattern contains duplicated points*
##
## Coordinates are given to 3 decimal places
## i.e. rounded to the nearest multiple of 0.001 units
##
## Window: rectangle = [11909.7, 43395.47] x [25596.33, 48562.06] units
## (31490 x 22970 units)
## Window area = 723094000 square unitsSince all 3 ppp objects has error message of duplicated points, we will perform duplication point handling.
Handling ppp object duplicated points
any(duplicated(childcare_ppp))## [1] TRUETo count the number of coindicence point, we will use the multiplicity() function as shown in the code chunk below.
multiplicity(childcare_ppp)## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## 1 1 1 3 1 1 1 1 2 1 1 1 1 1 1 1
## 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
## 1 1 1 1 1 1 1 1 1 1 9 1 1 1 1 1
## 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
## 1 1 1 1 1 1 2 1 1 3 1 1 1 1 1 1
## 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
## 1 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1
## 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
## 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1
## 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
## 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1
## 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
## 1 1 1 1 1 1 2 1 1 1 3 1 1 1 2 1
## 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## 1 1 1 1 1 2 1 1 1 1 1 1 1 1 3 2
## 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
## 1 2 1 1 1 2 2 3 1 5 1 5 1 1 1 2
## 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
## 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1
## 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
## 1 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
## 1 1 1 1 1 2 2 1 1 1 1 2 1 4 1 1
## 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
## 2 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1
## 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
## 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1
## 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
## 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 3
## 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
## 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1
## 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
## 1 1 1 1 1 1 1 9 1 1 2 1 1 1 1 1
## 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
## 1 1 1 5 1 1 1 1 1 2 1 1 2 2 1 1
## 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
## 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1
## 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
## 1 1 1 1 9 1 1 1 1 1 1 1 1 1 1 1
## 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
## 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
## 1 1 2 1 1 1 1 1 1 1 2 1 1 1 1 1
## 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
## 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1
## 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
## 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1
## 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
## 1 1 9 9 1 1 1 1 1 1 1 1 1 1 2 1
## 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
## 2 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1
## 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
## 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2
## 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
## 1 1 1 1 1 1 1 1 1 1 1 2 1 1 3 1
## 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
## 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1
## 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
## 2 2 2 1 1 1 1 2 1 1 2 1 1 1 2 1
## 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
## 1 2 1 1 1 1 1 9 1 4 1 2 1 1 1 1
## 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
## 2 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1
## 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
## 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1
## 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4
## 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
## 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1
## 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
## 1 1 1 1 1 4 1 1 1 1 1 4 1 1 1 1
## 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
## 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1
## 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
## 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
## 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1
## 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
## 1 1 1 1 1 1 1 1 1 4 1 1 1 1 1 1
## 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
## 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1
## 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2
## 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
## 3 1 1 1 2 1 1 1 3 1 1 3 1 1 1 1
## 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2
## 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
## 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1
## 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
## 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1
## 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
## 1 1 1 1 1 1 1 1 2 2 1 1 1 5 1 1
## 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
## 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1
## 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
## 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1
## 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
## 1 9 1 2 2 1 1 1 2 1 1 1 1 1 1 1
## 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
## 1 1 1 1 2 1 1 1 3 1 1 1 1 1 1 1
## 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
## 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
## 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1
## 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2
## 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
## 1 1 1 2 1 2 1 1 1 2 2 2 1 1 1 1
## 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
## 1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1
## 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
## 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1
## 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
## 1 1 1 1 1 1 1 1 4 1 1 1 1 1 2 1
## 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
## 1 1 1 1 1 1 1 1 1 9 1 1 1 1 1 1
## 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1
## 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
## 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
## 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1
## 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
## 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1
## 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
## 1 1 1 1 1 1 1 1 1 1 5 1 1 1 1 1
## 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
## 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 3
## 1537 1538 1539 1540 1541 1542 1543 1544 1545
## 1 1 1 1 1 1 2 1 1sum(multiplicity(preschool_ppp) > 1)## [1] 302sum(multiplicity(childcare_ppp) > 1)## [1] 128sum(multiplicity(kindergarten_ppp) > 1)## [1] 100Handle duplicated points using jittering approach. Jittering, which will add a small perturbation to the duplicate points so that they do not occupy the exact same space.
childcare_ppp_jit <- rjitter(childcare_ppp, retry=TRUE, nsim=1, drop=TRUE)
plot(childcare_ppp_jit)
preschool_ppp_jit <- rjitter(preschool_ppp, retry=TRUE, nsim=1, drop=TRUE)
plot(preschool_ppp_jit)
kindergarten_ppp_jit <- rjitter(kindergarten_ppp, retry=TRUE, nsim=1, drop=TRUE)
plot(kindergarten_ppp_jit)
### Creating owin objects Confining analysis within Singapore boundaries using sg_sp data
sg_owin <- as(sg_sp, 'owin')
plot(sg_owin)
Combining chilcare and kindergarten services with the study area
preschoolSG_ppp = preschool_ppp_jit[sg_owin]
childcareSG_ppp = childcare_ppp_jit[sg_owin]
kindergartenSG_ppp = kindergarten_ppp_jit[sg_owin]Spatial Point Pattern Analysis - 1st Order Analysis
Quadrat Analysis
Perform a test of Complete Spatial Randomness for a given point pattern, based on quadrat counts by using quadrat.test() of statspat.
The test hypotheses are:
Ho = The distribution of childcare/kindergarten services are randomly distributed in space.
H1= The distribution of childcare/kindergarten services are not randomly distributed in space.
The 95% confident interval will be used.
Distribution of Childcare centre
childcare_qt <- quadrat.test(childcareSG_ppp, nx=20, ny=15, method='M', nsim=999)
childcare_qt##
## Conditional Monte Carlo test of CSR using quadrat counts
## Test statistic: Pearson X2 statistic
##
## data: childcareSG_ppp
## X2 = 2409.2, p-value = 0.004
## alternative hypothesis: two.sided
##
## Quadrats: 185 tiles (irregular windows)Since the p-value is lesser than the pre-defined alpha value of 0.05 (confidence interval 95%), we will reject the null hypothesis. Thus we can conclude with 95% confidence that childcare services are not randomly distributed.
Distribution of Kindergarten
kindergarten_qt <- quadrat.test(kindergartenSG_ppp, nx=20, ny=15, method='M', nsim=999)
kindergarten_qt##
## Conditional Monte Carlo test of CSR using quadrat counts
## Test statistic: Pearson X2 statistic
##
## data: kindergartenSG_ppp
## X2 = 770.75, p-value = 0.002
## alternative hypothesis: two.sided
##
## Quadrats: 185 tiles (irregular windows)Since the p-value is lesser than the pre-defined alpha value of 0.05 (confidence interval 95%), we will reject the null hypothesis. Thus we can conclude with 95% confidence that kindergarten services are not randomly distributed.
Compare distribution of childcare and kindergarten services
par(mfrow=c(1, 2))
plot(childcareSG_ppp, main = 'Distribution of Childcare Services')
plot(kindergartenSG_ppp, main = 'Distribution of Kindergartens')
5) Statistical interpretations on Section B (4)
2nd order spatial point patterns analysis
Focus on the following four planning areas instead of the entire country. They are: Sengkang, Bedok, Bukit Batok and Hougang.
Import subzone data
mpsz <- readOGR(dsn = "Data/shapefile", layer="MP14_SUBZONE_NO_SEA_PL")## OGR data source with driver: ESRI Shapefile
## Source: "D:\Users\John_Ng\Year 3 Term 1\(Monday) IS415 Geospatial Analytics & Applns (SMU-X)\Take home exercise\IS415_Take_Home_Exercise_01_NG_XUN_JIE\Data\shapefile", layer: "MP14_SUBZONE_NO_SEA_PL"
## with 323 features
## It has 15 fieldsExtract subzone of interest
sk = mpsz[mpsz@data$PLN_AREA_N == "SENGKANG",]
bk = mpsz[mpsz@data$PLN_AREA_N == "BEDOK",]
bb = mpsz[mpsz@data$PLN_AREA_N == "BUKIT BATOK",]
hg = mpsz[mpsz@data$PLN_AREA_N == "HOUGANG",]Convert SpatialPolygonDataFrame into Spatial Polygon
sk_sp = as(sk, "SpatialPolygons")
bk_sp = as(bk, "SpatialPolygons")
bb_sp = as(bb, "SpatialPolygons")
hg_sp = as(hg, "SpatialPolygons")Create owin object
sk_owin = as(sk_sp, "owin")
bk_owin = as(bk_sp, "owin")
bb_owin = as(bb_sp, "owin")
hg_owin = as(hg_sp, "owin")Combining childcare points and the study area Extract childcare that is within the specific region to do our analysis later on.
childcare_sk_ppp = childcare_ppp_jit[sk_owin]
childcare_bk_ppp = childcare_ppp_jit[bk_owin]
childcare_bb_ppp = childcare_ppp_jit[bb_owin]
childcare_hg_ppp = childcare_ppp_jit[hg_owin]plot(childcare_sk_ppp)
plot(childcare_bk_ppp)
plot(childcare_bb_ppp)
plot(childcare_hg_ppp)
Extract kindergarten that is within the specific region to do our analysis later on.
kindergarten_sk_ppp = kindergarten_ppp_jit[sk_owin]
kindergarten_bk_ppp = kindergarten_ppp_jit[bk_owin]
kindergarten_bb_ppp = kindergarten_ppp_jit[bb_owin]
kindergarten_hg_ppp = kindergarten_ppp_jit[hg_owin]plot(kindergarten_sk_ppp)
plot(kindergarten_bk_ppp)
plot(kindergarten_bb_ppp)
plot(kindergarten_hg_ppp)
#####################################################################################################
Analysing Spatial Point Process Using G-Function ### Sengkang planning area Compute G-function estimation Chilcare Distribution
G_SK_CC = Gest(childcare_sk_ppp, correction = "border")
plot(G_SK_CC)
Compute G-function estimation Kindergarten Distribution
G_SK_KG = Gest(kindergarten_sk_ppp, correction = "border")
plot(G_SK_KG)
Performing Complete Spatial Randomness Test To confirm the observed spatial patterns above, a hypothesis test will be conducted. The hypothesis and test are as follows:
Ho = The distribution of childcare/kindergarten services at SengKang are randomly distributed.
H1= The distribution of childcare/kindergarten services at SengKang are not randomly distributed.
The null hypothesis will be rejected if p-value is smaller than alpha value of 0.001.
Monte Carlo test with G-FUNCTION
For childcare
G_SK_CC.csr <- envelope(childcare_sk_ppp, Gest, nsim = 999)## Generating 999 simulations of CSR ...
## 1, 2, 3, ......10.........20.........30.........40.........50.........60........
## .70.........80.........90.........100.........110.........120.........130......
## ...140.........150.........160.........170.........180.........190.........200....
## .....210.........220.........230.........240.........250.........260.........270..
## .......280.........290.........300.........310.........320.........330.........340
## .........350.........360.........370.........380.........390.........400........
## .410.........420.........430.........440.........450.........460.........470......
## ...480.........490.........500.........510.........520.........530.........540....
## .....550.........560.........570.........580.........590.........600.........610..
## .......620.........630.........640.........650.........660.........670.........680
## .........690.........700.........710.........720.........730.........740........
## .750.........760.........770.........780.........790.........800.........810......
## ...820.........830.........840.........850.........860.........870.........880....
## .....890.........900.........910.........920.........930.........940.........950..
## .......960.........970.........980.........990........ 999.
##
## Done.plot(G_SK_CC.csr)
For Kindergarten
G_SK_KG.csr <- envelope(kindergarten_sk_ppp, Gest, nsim = 999)## Generating 999 simulations of CSR ...
## 1, 2, 3, ......10.........20.........30.........40.........50.........60........
## .70.........80.........90.........100.........110.........120.........130......
## ...140.........150.........160.........170.........180.........190.........200....
## .....210.........220.........230.........240.........250.........260.........270..
## .......280.........290.........300.........310.........320.........330.........340
## .........350.........360.........370.........380.........390.........400........
## .410.........420.........430.........440.........450.........460.........470......
## ...480.........490.........500.........510.........520.........530.........540....
## .....550.........560.........570.........580.........590.........600.........610..
## .......620.........630.........640.........650.........660.........670.........680
## .........690.........700.........710.........720.........730.........740........
## .750.........760.........770.........780.........790.........800.........810......
## ...820.........830.........840.........850.........860.........870.........880....
## .....890.........900.........910.........920.........930.........940.........950..
## .......960.........970.........980.........990........ 999.
##
## Done.plot(G_SK_KG.csr)
############## ### Bedok planning area Compute G-function estimation Chilcare Distribution
G_BK_CC = Gest(childcare_bk_ppp, correction = "border")
plot(G_BK_CC)
Compute G-function estimation Kindergarten Distribution
G_BK_KG = Gest(kindergarten_bk_ppp, correction = "border")
plot(G_BK_KG)
Performing Complete Spatial Randomness Test To confirm the observed spatial patterns above, a hypothesis test will be conducted. The hypothesis and test are as follows:
Ho = The distribution of childcare/kindergarten services at Bedok are randomly distributed.
H1= The distribution of childcare/kindergarten services at Bedok are not randomly distributed.
The null hypothesis will be rejected if p-value is smaller than alpha value of 0.001.
Monte Carlo test with G-FUNCTION
For childcare
G_BK_CC.csr <- envelope(childcare_bk_ppp, Gest, nsim = 999)## Generating 999 simulations of CSR ...
## 1, 2, 3, ......10.........20.........30.........40.........50.........60........
## .70.........80.........90.........100.........110.........120.........130......
## ...140.........150.........160.........170.........180.........190.........200....
## .....210.........220.........230.........240.........250.........260.........270..
## .......280.........290.........300.........310.........320.........330.........340
## .........350.........360.........370.........380.........390.........400........
## .410.........420.........430.........440.........450.........460.........470......
## ...480.........490.........500.........510.........520.........530.........540....
## .....550.........560.........570.........580.........590.........600.........610..
## .......620.........630.........640.........650.........660.........670.........680
## .........690.........700.........710.........720.........730.........740........
## .750.........760.........770.........780.........790.........800.........810......
## ...820.........830.........840.........850.........860.........870.........880....
## .....890.........900.........910.........920.........930.........940.........950..
## .......960.........970.........980.........990........ 999.
##
## Done.plot(G_BK_CC.csr)
For Kindergarten
G_BK_KG.csr <- envelope(kindergarten_bk_ppp, Gest, nsim = 999)## Generating 999 simulations of CSR ...
## 1, 2, 3, ......10.........20.........30.........40.........50.........60........
## .70.........80.........90.........100.........110.........120.........130......
## ...140.........150.........160.........170.........180.........190.........200....
## .....210.........220.........230.........240.........250.........260.........270..
## .......280.........290.........300.........310.........320.........330.........340
## .........350.........360.........370.........380.........390.........400........
## .410.........420.........430.........440.........450.........460.........470......
## ...480.........490.........500.........510.........520.........530.........540....
## .....550.........560.........570.........580.........590.........600.........610..
## .......620.........630.........640.........650.........660.........670.........680
## .........690.........700.........710.........720.........730.........740........
## .750.........760.........770.........780.........790.........800.........810......
## ...820.........830.........840.........850.........860.........870.........880....
## .....890.........900.........910.........920.........930.........940.........950..
## .......960.........970.........980.........990........ 999.
##
## Done.plot(G_BK_KG.csr)
#################################################################
BUKIT BATOK planning area
Compute G-function estimation Chilcare Distribution
G_BB_CC = Gest(childcare_bb_ppp, correction = "border")
plot(G_BB_CC)
Compute G-function estimation Kindergarten Distribution
G_BB_KG = Gest(kindergarten_bb_ppp, correction = "border")
plot(G_BB_KG)
Performing Complete Spatial Randomness Test To confirm the observed spatial patterns above, a hypothesis test will be conducted. The hypothesis and test are as follows:
Ho = The distribution of childcare/kindergarten services at BUKIT BATOK are randomly distributed.
H1= The distribution of childcare/kindergarten services at BUKIT BATOK are not randomly distributed.
The null hypothesis will be rejected if p-value is smaller than alpha value of 0.001.
Monte Carlo test with G-FUNCTION
For childcare
G_BB_CC.csr <- envelope(childcare_bb_ppp, Gest, nsim = 999)## Generating 999 simulations of CSR ...
## 1, 2, 3, ......10.........20.........30.........40.........50.........60........
## .70.........80.........90.........100.........110.........120.........130......
## ...140.........150.........160.........170.........180.........190.........200....
## .....210.........220.........230.........240.........250.........260.........270..
## .......280.........290.........300.........310.........320.........330.........340
## .........350.........360.........370.........380.........390.........400........
## .410.........420.........430.........440.........450.........460.........470......
## ...480.........490.........500.........510.........520.........530.........540....
## .....550.........560.........570.........580.........590.........600.........610..
## .......620.........630.........640.........650.........660.........670.........680
## .........690.........700.........710.........720.........730.........740........
## .750.........760.........770.........780.........790.........800.........810......
## ...820.........830.........840.........850.........860.........870.........880....
## .....890.........900.........910.........920.........930.........940.........950..
## .......960.........970.........980.........990........ 999.
##
## Done.plot(G_BB_CC.csr)
For Kindergarten
G_BB_KG.csr <- envelope(kindergarten_bb_ppp, Gest, nsim = 999)## Generating 999 simulations of CSR ...
## 1, 2, 3, ......10.........20.........30.........40.........50.........60........
## .70.........80.........90.........100.........110.........120.........130......
## ...140.........150.........160.........170.........180.........190.........200....
## .....210.........220.........230.........240.........250.........260.........270..
## .......280.........290.........300.........310.........320.........330.........340
## .........350.........360.........370.........380.........390.........400........
## .410.........420.........430.........440.........450.........460.........470......
## ...480.........490.........500.........510.........520.........530.........540....
## .....550.........560.........570.........580.........590.........600.........610..
## .......620.........630.........640.........650.........660.........670.........680
## .........690.........700.........710.........720.........730.........740........
## .750.........760.........770.........780.........790.........800.........810......
## ...820.........830.........840.........850.........860.........870.........880....
## .....890.........900.........910.........920.........930.........940.........950..
## .......960.........970.........980.........990........ 999.
##
## Done.plot(G_BB_KG.csr)
HOUGANG planning area
Compute G-function estimation Chilcare Distribution
G_HG_CC = Gest(childcare_hg_ppp, correction = "border")
plot(G_HG_CC)
Compute G-function estimation Kindergarten Distribution
G_HG_KG = Gest(kindergarten_hg_ppp, correction = "border")
plot(G_HG_KG)
Performing Complete Spatial Randomness Test To confirm the observed spatial patterns above, a hypothesis test will be conducted. The hypothesis and test are as follows:
Ho = The distribution of childcare/kindergarten services at HOUGANG are randomly distributed.
H1= The distribution of childcare/kindergarten services at HOUGANG are not randomly distributed.
The null hypothesis will be rejected if p-value is smaller than alpha value of 0.001.
Monte Carlo test with G-FUNCTION
For childcare
G_HG_CC.csr <- envelope(childcare_hg_ppp, Gest, nsim = 999)## Generating 999 simulations of CSR ...
## 1, 2, 3, ......10.........20.........30.........40.........50.........60........
## .70.........80.........90.........100.........110.........120.........130......
## ...140.........150.........160.........170.........180.........190.........200....
## .....210.........220.........230.........240.........250.........260.........270..
## .......280.........290.........300.........310.........320.........330.........340
## .........350.........360.........370.........380.........390.........400........
## .410.........420.........430.........440.........450.........460.........470......
## ...480.........490.........500.........510.........520.........530.........540....
## .....550.........560.........570.........580.........590.........600.........610..
## .......620.........630.........640.........650.........660.........670.........680
## .........690.........700.........710.........720.........730.........740........
## .750.........760.........770.........780.........790.........800.........810......
## ...820.........830.........840.........850.........860.........870.........880....
## .....890.........900.........910.........920.........930.........940.........950..
## .......960.........970.........980.........990........ 999.
##
## Done.plot(G_HG_CC.csr)
For Kindergarten
G_HG_KG.csr <- envelope(kindergarten_hg_ppp, Gest, nsim = 999)## Generating 999 simulations of CSR ...
## 1, 2, 3, ......10.........20.........30.........40.........50.........60........
## .70.........80.........90.........100.........110.........120.........130......
## ...140.........150.........160.........170.........180.........190.........200....
## .....210.........220.........230.........240.........250.........260.........270..
## .......280.........290.........300.........310.........320.........330.........340
## .........350.........360.........370.........380.........390.........400........
## .410.........420.........430.........440.........450.........460.........470......
## ...480.........490.........500.........510.........520.........530.........540....
## .....550.........560.........570.........580.........590.........600.........610..
## .......620.........630.........640.........650.........660.........670.........680
## .........690.........700.........710.........720.........730.........740........
## .750.........760.........770.........780.........790.........800.........810......
## ...820.........830.........840.........850.........860.........870.........880....
## .....890.........900.........910.........920.........930.........940.........950..
## .......960.........970.........980.........990........ 999.
##
## Done.plot(G_HG_KG.csr)