Data Cleaning and Visualisation

news
Author

Group 9

Published

April 5, 2023

Modified

April 16, 2023

R Packages

pacman::p_load(readr, sf, tmap, spatstat, sfdep, tidyverse, maptools, raster, SpatialAcc, ggstatsplot, reshape2, rgdal, spNetwork)

Import Data

Aspatial Data

General Practitioner Clinics

gp_data <- read_csv("data/aspatial/gp_data_geocoded.csv")[,-1]
Rows: 1221 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): clinic, postal_code
dbl (5): index, X, Y, Lat, Long

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(gp_data, 5)
# A tibble: 5 × 6
  clinic                 postal_code      X      Y   Lat  Long
  <chr>                  <chr>        <dbl>  <dbl> <dbl> <dbl>
1 RafflesMedical         159953      24849. 30114.  1.29  104.
2 Shenton Medical Group  119963      24433. 28496.  1.27  104.
3 Town Hall Clinic       119967      24375. 28917.  1.28  104.
4 Chuah Clinic & Surgery 380113      33881. 33643.  1.32  104.
5 ECM Clinic & Surgery   387604      33518. 33021.  1.31  104.

Hospitals

hospital_data <- read_csv("data/aspatial/hospital_data_geocoded.csv")
Rows: 27 Columns: 11
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (6): HOSPITAL_NAME, PRIVATE, TYPE, MANAGED_BY, ADDRESS, NUM_OF_BEDS
dbl (5): POSTAL_CODE, X, Y, Lat, Long

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(hospital_data, 5)
# A tibble: 5 × 11
  HOSPITAL_N…¹ PRIVATE TYPE  MANAG…² ADDRESS POSTA…³ NUM_O…⁴      X      Y   Lat
  <chr>        <chr>   <chr> <chr>   <chr>     <dbl> <chr>    <dbl>  <dbl> <dbl>
1 ALEXANDRA H… Y       GENE… <NA>    ALEXAN…  159964 176     24230. 30060.  1.29
2 BRIGHT VISI… N       COMM… SINGHE… 5 LORO…  547530 317     32976. 39338.  1.37
3 CHANGI GENE… N       GENE… SINGHE… 2 SIME…  529889 1,000   40784. 35942.  1.34
4 CONCORD INT… Y       SPEC… <NA>    19 Ada…  289891 34      25774. 34337.  1.33
5 FARRER PARK… Y       GENE… <NA>    1 Farr…  217562 121     30284. 32783.  1.31
# … with 1 more variable: Long <dbl>, and abbreviated variable names
#   ¹​HOSPITAL_NAME, ²​MANAGED_BY, ³​POSTAL_CODE, ⁴​NUM_OF_BEDS

Polyclinics

poly_data <- read_csv("data/aspatial/polyclinic_data_geocoded.csv")
Rows: 20 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): POLYCLINIC, MANAGED_BY, ADDRESS
dbl (5): POSTAL_CODE, X, Y, Lat, Long

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(poly_data, 5)
# A tibble: 5 × 8
  POLYCLINIC               MANAGED_BY ADDRESS  POSTA…¹      X      Y   Lat  Long
  <chr>                    <chr>      <chr>      <dbl>  <dbl>  <dbl> <dbl> <dbl>
1 ANG MO KIO POLYCLINIC    NHG        21 ANG …  569666 29375. 39591.  1.37  104.
2 BEDOK POLYCLINIC         Singhealth HEARTBE…  469662 38997. 34344.  1.33  104.
3 BUKIT BATOK POLYCLINIC   NUHS       50 BUKI…  659164 18485. 37125.  1.35  104.
4 BUKIT MERAH POLYCLINIC   Singhealth 163 BUK…  150163 26192. 29566.  1.28  104.
5 CHOA CHU KANG POLYCLINIC NUHS       2 TECK …  688846 18814. 40478.  1.38  104.
# … with abbreviated variable name ¹​POSTAL_CODE

Nursing Homes

nursing_data <- read_csv("data/aspatial/nursing_home_data_geocoded.csv")
Rows: 76 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): NURSING_HOME_NAME, ADDRESS
dbl (5): POSTAL_CODE, X, Y, Lat, Long

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(nursing_data, 5)
# A tibble: 5 × 7
  NURSING_HOME_NAME             ADDRESS        POSTA…¹      X      Y   Lat  Long
  <chr>                         <chr>            <dbl>  <dbl>  <dbl> <dbl> <dbl>
1 ALL SAINTS HOME (HOUGANG)     5 Poh Huat Ro…  546703 33636. 38608.  1.37  104.
2 ALL SAINTS HOME (JURONG EAST) 20 JURONG EAS…  609792 17702. 35990.  1.34  104.
3 ALL SAINTS HOME (TAMPINES)    11 TAMPINES S…  529123 41443. 38141.  1.36  104.
4 ALL SAINTS HOME (YISHUN)      551 YISHUN RI…  768681 27662. 46348.  1.44  104.
5 APEX HARMONY LODGE            10 PASIR RIS …  518240 42573. 39170.  1.37  104.
# … with abbreviated variable name ¹​POSTAL_CODE

Primary Care Networks

pcn_data <- read_csv("data/aspatial/PCN Clinic Listing (by PCN) With Postal Code.csv")
New names:
Rows: 828 Columns: 19
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(13): PCN, Clinic Name, Address, Operating Hours (Please call the clinic... dbl
(6): S/N, results.POSTAL...15, results.X, results.Y, results.LATITUDE, ...
ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
Specify the column types or set `show_col_types = FALSE` to quiet this message.
• `results.POSTAL` -> `results.POSTAL...9`
• `results.POSTAL` -> `results.POSTAL...15`
head(pcn_data, 5)
# A tibble: 5 × 19
  `S/N` PCN      Clini…¹ Address Opera…² Conta…³ Addre…⁴ Posta…⁵ resul…⁶ resul…⁷
  <dbl> <chr>    <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>  
1     1 ASSURAN… 57 MED… BLK 32… "Monda… 651892… BLK 32… 390032  390032  32 CAS…
2     2 ASSURAN… ACUMED… 301 BO… "Monda… 65,159… 301 BO… 649846  649846  BOON L…
3     3 ASSURAN… ACUMED… BLK 21… "Monda… 64,438… BLK 21… 460214  460214  BEDOK …
4     4 ASSURAN… ACUMED… 1 JOO … "Monda… 68,615… 1 JOO … 629117  629117  DBS NT…
5     5 ASSURAN… ACUMED… 1 JURO… "Monda… 67,923… 1 JURO… 648886  648886  OCBC J…
# … with 9 more variables: results.BLK_NO <chr>, results.ROAD_NAME <chr>,
#   results.BUILDING <chr>, results.ADDRESS <chr>, results.POSTAL...15 <dbl>,
#   results.X <dbl>, results.Y <dbl>, results.LATITUDE <dbl>,
#   results.LONGITUDE <dbl>, and abbreviated variable names ¹​`Clinic Name`,
#   ²​`Operating Hours (Please call the clinic before visiting)`, ³​`Contact No`,
#   ⁴​`Address Check`, ⁵​`Postal Code`, ⁶​results.POSTAL...9, ⁷​results.SEARCHVAL

Resident Population by Planning Area/Subzone, Age Group and Sex

Only total population, not separated by genders

pop_data <- read_csv("data/aspatial/Resident Population 2015.csv", skip=11)[1:379,1:58]
New names:
Rows: 401 Columns: 59
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(58): ...1, Total...2, 0 - 4...3, 5 - 9...4, 10 - 14...5, 15 - 19...6, 2... lgl
(1): ...59
ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
Specify the column types or set `show_col_types = FALSE` to quiet this message.
• `` -> `...1`
• `Total` -> `Total...2`
• `0 - 4` -> `0 - 4...3`
• `5 - 9` -> `5 - 9...4`
• `10 - 14` -> `10 - 14...5`
• `15 - 19` -> `15 - 19...6`
• `20 - 24` -> `20 - 24...7`
• `25 - 29` -> `25 - 29...8`
• `30 - 34` -> `30 - 34...9`
• `35 - 39` -> `35 - 39...10`
• `40 - 44` -> `40 - 44...11`
• `45 - 49` -> `45 - 49...12`
• `50 - 54` -> `50 - 54...13`
• `55 - 59` -> `55 - 59...14`
• `60 - 64` -> `60 - 64...15`
• `65 - 69` -> `65 - 69...16`
• `70 - 74` -> `70 - 74...17`
• `75 - 79` -> `75 - 79...18`
• `80 - 84` -> `80 - 84...19`
• `85 & Over` -> `85 & Over...20`
• `Total` -> `Total...21`
• `0 - 4` -> `0 - 4...22`
• `5 - 9` -> `5 - 9...23`
• `10 - 14` -> `10 - 14...24`
• `15 - 19` -> `15 - 19...25`
• `20 - 24` -> `20 - 24...26`
• `25 - 29` -> `25 - 29...27`
• `30 - 34` -> `30 - 34...28`
• `35 - 39` -> `35 - 39...29`
• `40 - 44` -> `40 - 44...30`
• `45 - 49` -> `45 - 49...31`
• `50 - 54` -> `50 - 54...32`
• `55 - 59` -> `55 - 59...33`
• `60 - 64` -> `60 - 64...34`
• `65 - 69` -> `65 - 69...35`
• `70 - 74` -> `70 - 74...36`
• `75 - 79` -> `75 - 79...37`
• `80 - 84` -> `80 - 84...38`
• `85 & Over` -> `85 & Over...39`
• `Total` -> `Total...40`
• `0 - 4` -> `0 - 4...41`
• `5 - 9` -> `5 - 9...42`
• `10 - 14` -> `10 - 14...43`
• `15 - 19` -> `15 - 19...44`
• `20 - 24` -> `20 - 24...45`
• `25 - 29` -> `25 - 29...46`
• `30 - 34` -> `30 - 34...47`
• `35 - 39` -> `35 - 39...48`
• `40 - 44` -> `40 - 44...49`
• `45 - 49` -> `45 - 49...50`
• `50 - 54` -> `50 - 54...51`
• `55 - 59` -> `55 - 59...52`
• `60 - 64` -> `60 - 64...53`
• `65 - 69` -> `65 - 69...54`
• `70 - 74` -> `70 - 74...55`
• `75 - 79` -> `75 - 79...56`
• `80 - 84` -> `80 - 84...57`
• `85 & Over` -> `85 & Over...58`
• `` -> `...59`
head(pop_data, 5)
# A tibble: 5 × 58
  ...1   Total…¹ 0 - 4…² 5 - 9…³ 10 - …⁴ 15 - …⁵ 20 - …⁶ 25 - …⁷ 30 - …⁸ 35 - …⁹
  <chr>  <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>  
1 Total  3902690 183580  204450  214390  242900  264130  271030  290620  301070 
2 Ang M… 174770  6790    7660    8290    9320    10310   11170   12250   13070  
3 Ang M… 5020    260     280     320     280     260     310     370     420    
4 Cheng… 29770   1290    1180    1290    1400    1570    1830    2490    2490   
5 Chong… 27900   910     1100    1180    1370    1520    1800    1980    2100   
# … with 48 more variables: `40 - 44...11` <chr>, `45 - 49...12` <chr>,
#   `50 - 54...13` <chr>, `55 - 59...14` <chr>, `60 - 64...15` <chr>,
#   `65 - 69...16` <chr>, `70 - 74...17` <chr>, `75 - 79...18` <chr>,
#   `80 - 84...19` <chr>, `85 & Over...20` <chr>, Total...21 <chr>,
#   `0 - 4...22` <chr>, `5 - 9...23` <chr>, `10 - 14...24` <chr>,
#   `15 - 19...25` <chr>, `20 - 24...26` <chr>, `25 - 29...27` <chr>,
#   `30 - 34...28` <chr>, `35 - 39...29` <chr>, `40 - 44...30` <chr>, …

PCN

pcn_data <- readr::read_csv("data/aspatial/PCN Clinic Listing (by PCN) With Postal Code.csv")
New names:
Rows: 828 Columns: 19
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(13): PCN, Clinic Name, Address, Operating Hours (Please call the clinic... dbl
(6): S/N, results.POSTAL...15, results.X, results.Y, results.LATITUDE, ...
ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
Specify the column types or set `show_col_types = FALSE` to quiet this message.
• `results.POSTAL` -> `results.POSTAL...9`
• `results.POSTAL` -> `results.POSTAL...15`

Geospatial Data

Master Plan Subzone 2019

mpsz <- st_read(dsn = "data/geospatial/MPSZ-2019", 
                            layer = "MPSZ-2019") %>% 
  st_transform(crs = 3414)
Reading layer `MPSZ-2019' from data source 
  `C:\deadline2359\IS415-Project\posts\data_cleaning\data\geospatial\MPSZ-2019' 
  using driver `ESRI Shapefile'
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS:  WGS 84
write_rds(mpsz, "data/models/mpsz_original.rds")
head(mpsz, 5)
Simple feature collection with 5 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 8012.578 ymin: 22108.68 xmax: 33316.59 ymax: 31087.61
Projected CRS: SVY21 / Singapore TM
                SUBZONE_N SUBZONE_C      PLN_AREA_N PLN_AREA_C       REGION_N
1             MARINA EAST    MESZ01     MARINA EAST         ME CENTRAL REGION
2        INSTITUTION HILL    RVSZ05    RIVER VALLEY         RV CENTRAL REGION
3          ROBERTSON QUAY    SRSZ01 SINGAPORE RIVER         SR CENTRAL REGION
4 JURONG ISLAND AND BUKOM    WISZ01 WESTERN ISLANDS         WI    WEST REGION
5            FORT CANNING    MUSZ02          MUSEUM         MU CENTRAL REGION
  REGION_C                       geometry
1       CR MULTIPOLYGON (((33222.98 29...
2       CR MULTIPOLYGON (((28481.45 30...
3       CR MULTIPOLYGON (((28087.34 30...
4       WR MULTIPOLYGON (((14557.7 304...
5       CR MULTIPOLYGON (((29542.53 31...

Eldercare

eldercare_sf <- st_read(dsn = "data/geospatial/eldercare", 
                            layer = "ELDERCARE") %>% 
  st_transform(crs = 3414)
Reading layer `ELDERCARE' from data source 
  `C:\deadline2359\IS415-Project\posts\data_cleaning\data\geospatial\eldercare' 
  using driver `ESRI Shapefile'
Simple feature collection with 120 features and 19 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 14481.92 ymin: 28218.43 xmax: 41665.14 ymax: 46804.9
Projected CRS: SVY21 / Singapore TM
head(eldercare_sf, 5)
Simple feature collection with 5 features and 19 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 14481.92 ymin: 30135.49 xmax: 38803.81 ymax: 36639.12
Projected CRS: SVY21 / Singapore TM
  fid OBJECTID ADDRESSBLO ADDRESSBUI ADDRESSPOS
1   1        1       <NA>       <NA>     601318
2   2        2       <NA>       <NA>     462509
3   3        3       <NA>       <NA>     640190
4   4        4       <NA>       <NA>     190005
5   5        5       <NA>       <NA>     160044
                             ADDRESSSTR ADDRESSTYP DESCRIPTIO HYPERLINK
1     318A Jurong East Avenue 1 #02-308       <NA>       <NA>      <NA>
2 Blk 509B Bedok North Street 3 #02-157       <NA>       <NA>      <NA>
3        Blk 190 Boon Lay Drive #01-242       <NA>       <NA>      <NA>
4                   5 Beach Rd #02-4943       <NA>       <NA>      <NA>
5            Blk 44 Beo Crescent #01-67       <NA>       <NA>      <NA>
  LANDXADDRE LANDYADDRE                                   NAME PHOTOURL
1          0          0           Yuhua Senior Activity Centre     <NA>
2          0          0                   THK SAC @ Kaki Bukit     <NA>
3          0          0                     THK SAC @ Boon Lay     <NA>
4          0          0 PEACE-Connect Senior Activity Centre@5     <NA>
5          0          0                 THK SAC @ Beo Crescent     <NA>
  ADDRESSFLO          INC_CRC FMEL_UPD_D ADDRESSUNI   X_ADDR   Y_ADDR
1       <NA> 2B0DB92FDD914FFC 2016-07-28       <NA> 16614.08 36639.12
2       <NA> 82728FA30612F3FD 2016-07-28       <NA> 38803.81 35098.78
3       <NA> DE7A8D4EA0BD1D9B 2016-07-28       <NA> 14481.92 36357.61
4       <NA> A2C058FC5785F7FE 2016-07-28       <NA> 31505.35 31853.52
5       <NA> 9DBFD51E056AEE70 2016-07-28       <NA> 27218.35 30135.49
                   geometry
1 POINT (16614.08 36639.12)
2 POINT (38803.81 35098.78)
3 POINT (14481.92 36357.61)
4 POINT (31505.35 31853.52)
5 POINT (27218.35 30135.49)

Locations of Bus Stops

busstop_sf <- st_read(dsn = "data/geospatial/BusStop_Feb2023", 
                            layer = "BusStop") %>% 
  st_transform(crs = 3414)
Reading layer `BusStop' from data source 
  `C:\deadline2359\IS415-Project\posts\data_cleaning\data\geospatial\BusStop_Feb2023' 
  using driver `ESRI Shapefile'
Simple feature collection with 5159 features and 3 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 3970.122 ymin: 26482.1 xmax: 48284.56 ymax: 52983.82
Projected CRS: SVY21
head(busstop_sf, 5)
Simple feature collection with 5 features and 3 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 13228.59 ymin: 30391.85 xmax: 41603.76 ymax: 44206.38
Projected CRS: SVY21 / Singapore TM
  BUS_STOP_N BUS_ROOF_N             LOC_DESC                  geometry
1      22069        B06   OPP CEVA LOGISTICS POINT (13576.31 32883.65)
2      32071        B23         AFT TRACK 13 POINT (13228.59 44206.38)
3      44331        B01              BLK 239  POINT (21045.1 40242.08)
4      96081        B05 GRACE INDEPENDENT CH POINT (41603.76 35413.11)
5      11561        B05              BLK 166 POINT (24568.74 30391.85)

Locations of Train Stations

trainstation_sf <- st_read(dsn = "data/geospatial/TrainStation_Feb2023", 
                            layer = "RapidTransitSystemStation")[,c(-1, -2)] %>% 
  st_transform(crs = 3414)
Reading layer `RapidTransitSystemStation' from data source 
  `C:\deadline2359\IS415-Project\posts\data_cleaning\data\geospatial\TrainStation_Feb2023' 
  using driver `ESRI Shapefile'
Warning in CPL_read_ogr(dsn, layer, query, as.character(options), quiet, : GDAL
Message 1: Non closed ring detected. To avoid accepting it, set the
OGR_GEOMETRY_ACCEPT_UNCLOSED_RING configuration option to NO
Simple feature collection with 220 features and 4 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 6068.209 ymin: 27478.44 xmax: 45377.5 ymax: 47913.58
Projected CRS: SVY21
head(trainstation_sf, 5)
Simple feature collection with 5 features and 2 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 29286.74 ymin: 30548.91 xmax: 34623.54 ymax: 33404.47
Projected CRS: SVY21 / Singapore TM
  TYP_CD_DES              STN_NAM_DE                       geometry
1        MRT   ESPLANADE MRT STATION POLYGON ((30566.07 30621.21...
2        MRT  PAYA LEBAR MRT STATION POLYGON ((34495.6 33384.44,...
3        MRT DHOBY GHAUT MRT STATION POLYGON ((29293.51 31312.53...
4        MRT      DAKOTA MRT STATION POLYGON ((34055.08 32290.62...
5        MRT    LAVENDER MRT STATION POLYGON ((31236.5 32085.76,...

CHAS

chas_sf <- st_read(dsn = "data/geospatial/CHAS Clinics Shapefile", 
                            layer = "CHAS Clinics") %>% 
  st_transform(crs = 3414)
Reading layer `CHAS Clinics' from data source 
  `C:\deadline2359\IS415-Project\posts\data_cleaning\data\geospatial\CHAS Clinics Shapefile' 
  using driver `ESRI Shapefile'
replacing null geometries with empty geometries
Simple feature collection with 1917 features and 19 fields (with 5 geometries empty)
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 4985.536 ymin: 26424.19 xmax: 45457.14 ymax: 48626.7
Projected CRS: SVY21 / Singapore TM
head(chas_sf, 5)
Simple feature collection with 5 features and 19 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 17595.29 ymin: 35041.76 xmax: 33064.94 ymax: 39158.18
Projected CRS: SVY21 / Singapore TM
  fid                              Name
1   1                1 Aljunied Medical
2   2                  1 BISHAN MEDICAL
3   3               1 MEDICAL TECK GHEE
4   4 1728 Dental Practice (Ang Mo Kio)
5   5     1728 Dental Practice (Jurong)
                                                                   Address
1                                                         Singapore 367874
2                          283, Bishan Street, #01- 191, Singapore\n570283
3 410, ANG MO KIO AVENUE 10, TECK GHEE SQUARE, #01- 837, Singapore\n560410
4                       704, Ang Mo Kio Ave 8, #01- 2559, Singapore 560704
5                    135, Jurong Gateway Road, #01- 319,\nSingapore 600135
  Telephone                                                               Type
1      <NA>                         Medical, Public Health Preparedness Clinic
2  64561600 Medical, Cervical Cancer Screen, Public Health Preparedness Clinic
3  62517030                                                            Medical
4  96311728                                                             Dental
5  97701728                                                             Dental
  Website Pap.Test.S Postal.Cod results.PO
1    <NA>          0     367874     367874
2    <NA>          1     570283     570283
3    <NA>          0     560410     560410
4    <NA>          0     560704     560704
5    <NA>          0     600135     600135
                                 results.SE results.BL           results.RO
1  389 UPPER ALJUNIED ROAD SINGAPORE 367874        389  UPPER ALJUNIED ROAD
2     283 BISHAN STREET 22 SINGAPORE 570283        283     BISHAN STREET 22
3 410 ANG MO KIO AVENUE 10 SINGAPORE 560410        410 ANG MO KIO AVENUE 10
4  704 ANG MO KIO AVENUE 8 SINGAPORE 560704        704  ANG MO KIO AVENUE 8
5      OCBC JURONG GATEWAY ROAD - FAIRPRICE        135  JURONG GATEWAY ROAD
                            results.BU
1                                  NIL
2                                  NIL
3                                  NIL
4                                  NIL
5 OCBC JURONG GATEWAY ROAD - FAIRPRICE
                                                                     results.AD
1                                      389 UPPER ALJUNIED ROAD SINGAPORE 367874
2                                         283 BISHAN STREET 22 SINGAPORE 570283
3                                     410 ANG MO KIO AVENUE 10 SINGAPORE 560410
4                                      704 ANG MO KIO AVENUE 8 SINGAPORE 560704
5 135 JURONG GATEWAY ROAD OCBC JURONG GATEWAY ROAD - FAIRPRICE SINGAPORE 600135
  results._1 results.X results.Y results.LA results.LO
1     367874  33064.94  35041.76   1.333179   103.8788
2     570283  29272.03  37887.89   1.358919   103.8447
3     560410  30373.38  38307.67   1.362715   103.8546
4     560704  29548.25  39158.18   1.370407   103.8472
5     600135  17595.29  35116.25   1.333852   103.7398
                   geometry
1 POINT (33064.94 35041.76)
2 POINT (29272.03 37887.89)
3 POINT (30373.38 38307.67)
4 POINT (29548.25 39158.18)
5 POINT (17595.29 35116.25)
chas_sf <- chas_sf[rowSums(is.na(chas_sf)) == 0, ]

Data Preparation

#pcn_sf <- st_as_sf(pcn_data, coords=c("results.LONGITUDE", "results.LATITUDE"), crs=4326) %>% st_transform(crs = 3414)
#pcn_data$results.LATITUDE
#pcn_data[is.na(pcn_data$results.LATITUDE),]
chas_sf <- chas_sf[rowSums(is.na(chas_sf)) == 0, ]
pcn_data <- pcn_data[rowSums(is.na(pcn_data)) == 0, ]

Retrieve Geospatial Data

General Practitioner Clinics

gp_sf <- st_as_sf(gp_data, coords=c("Long", "Lat"), crs=4326) %>% st_transform(crs = 3414)
gp_sf <- gp_sf[, c(1,2)]
head(gp_sf, 5)
Simple feature collection with 5 features and 2 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 24374.75 ymin: 28496.49 xmax: 33880.65 ymax: 33642.63
Projected CRS: SVY21 / Singapore TM
# A tibble: 5 × 3
  clinic                 postal_code            geometry
  <chr>                  <chr>               <POINT [m]>
1 RafflesMedical         159953         (24849.49 30114)
2 Shenton Medical Group  119963      (24432.59 28496.49)
3 Town Hall Clinic       119967      (24374.75 28916.52)
4 Chuah Clinic & Surgery 380113      (33880.65 33642.63)
5 ECM Clinic & Surgery   387604      (33518.21 33021.09)

Hospitals

hospital_sf <- st_as_sf(hospital_data, coords=c("Long", "Lat"), crs=4326) %>% st_transform(crs = 3414)
hospital_sf <- hospital_sf[, c(1:7)]
head(hospital_sf, 5)
Simple feature collection with 5 features and 7 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 24230.14 ymin: 30059.55 xmax: 40784.33 ymax: 39338.44
Projected CRS: SVY21 / Singapore TM
# A tibble: 5 × 8
  HOSPITAL_NAME                  PRIVATE TYPE    MANAG…¹ ADDRESS POSTA…² NUM_O…³
  <chr>                          <chr>   <chr>   <chr>   <chr>     <dbl> <chr>  
1 ALEXANDRA HOSPITAL             Y       GENERAL <NA>    ALEXAN…  159964 176    
2 BRIGHT VISION HOSPITAL         N       COMMUN… SINGHE… 5 LORO…  547530 317    
3 CHANGI GENERAL HOSPITAL        N       GENERAL SINGHE… 2 SIME…  529889 1,000  
4 CONCORD INTERNATIONAL HOSPITAL Y       SPECIA… <NA>    19 Ada…  289891 34     
5 FARRER PARK HOSPITAL           Y       GENERAL <NA>    1 Farr…  217562 121    
# … with 1 more variable: geometry <POINT [m]>, and abbreviated variable names
#   ¹​MANAGED_BY, ²​POSTAL_CODE, ³​NUM_OF_BEDS

Polyclinics

poly_sf <- st_as_sf(poly_data, coords=c("Long", "Lat"), crs=4326) %>% st_transform(crs = 3414)
poly_sf <- poly_sf[, c(1:4)]
head(poly_sf, 5)
Simple feature collection with 5 features and 4 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 18485.22 ymin: 29566.29 xmax: 38996.66 ymax: 40477.96
Projected CRS: SVY21 / Singapore TM
# A tibble: 5 × 5
  POLYCLINIC               MANAGED_BY ADDRESS  POSTA…¹            geometry
  <chr>                    <chr>      <chr>      <dbl>         <POINT [m]>
1 ANG MO KIO POLYCLINIC    NHG        21 ANG …  569666 (29375.43 39591.35)
2 BEDOK POLYCLINIC         Singhealth HEARTBE…  469662 (38996.66 34343.66)
3 BUKIT BATOK POLYCLINIC   NUHS       50 BUKI…  659164 (18485.22 37124.65)
4 BUKIT MERAH POLYCLINIC   Singhealth 163 BUK…  150163 (26191.96 29566.29)
5 CHOA CHU KANG POLYCLINIC NUHS       2 TECK …  688846 (18814.15 40477.96)
# … with abbreviated variable name ¹​POSTAL_CODE

Nursing Homes

nursing_sf <- st_as_sf(nursing_data, coords=c("Long", "Lat"), crs=4326) %>% st_transform(crs = 3414)
nursing_sf <- nursing_sf[, c(1:3)]
head(nursing_sf, 5)
Simple feature collection with 5 features and 3 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 17702.24 ymin: 35989.84 xmax: 42573.31 ymax: 46347.74
Projected CRS: SVY21 / Singapore TM
# A tibble: 5 × 4
  NURSING_HOME_NAME             ADDRESS        POSTA…¹            geometry
  <chr>                         <chr>            <dbl>         <POINT [m]>
1 ALL SAINTS HOME (HOUGANG)     5 Poh Huat Ro…  546703  (33635.6 38608.27)
2 ALL SAINTS HOME (JURONG EAST) 20 JURONG EAS…  609792 (17702.24 35989.84)
3 ALL SAINTS HOME (TAMPINES)    11 TAMPINES S…  529123 (41443.41 38141.06)
4 ALL SAINTS HOME (YISHUN)      551 YISHUN RI…  768681 (27662.47 46347.74)
5 APEX HARMONY LODGE            10 PASIR RIS …  518240  (42573.31 39170.3)
# … with abbreviated variable name ¹​POSTAL_CODE

Primary Care Network

pcn_sf <- st_as_sf(pcn_data, coords=c("results.LONGITUDE", "results.LATITUDE"), crs=4326) %>% st_transform(crs = 3414)
pcn_sf <- pcn_sf[, c(2:6, 8)]

Data

head(pcn_sf, 5)
Simple feature collection with 5 features and 6 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 10725.31 ymin: 32414.15 xmax: 39037.12 ymax: 35735.68
Projected CRS: SVY21 / Singapore TM
# A tibble: 5 × 7
  PCN          Clini…¹ Address Opera…² Conta…³ Posta…⁴            geometry
  <chr>        <chr>   <chr>   <chr>   <chr>   <chr>           <POINT [m]>
1 ASSURANCE P… 57 MED… BLK 32… "Monda… 651892… 390032  (33629.25 32414.15)
2 ASSURANCE P… ACUMED… 301 BO… "Monda… 65,159… 649846  (13813.58 35629.67)
3 ASSURANCE P… ACUMED… BLK 21… "Monda… 64,438… 460214  (39037.12 34234.33)
4 ASSURANCE P… ACUMED… 1 JOO … "Monda… 68,615… 629117  (10725.31 34094.33)
5 ASSURANCE P… ACUMED… 1 JURO… "Monda… 67,923… 648886  (13907.03 35735.68)
# … with abbreviated variable names ¹​`Clinic Name`,
#   ²​`Operating Hours (Please call the clinic before visiting)`, ³​`Contact No`,
#   ⁴​`Postal Code`

:::

Merge MPSZ with Population Data

Convert Data Types

pop_is_char <- sapply(pop_data[c(2:58)], is.character)
pop_data[c(2:58)][ , pop_is_char] <- as.data.frame(apply(pop_data[c(2:58)][ , pop_is_char], 2, as.numeric))
Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion

Warning in apply(pop_data[c(2:58)][, pop_is_char], 2, as.numeric): NAs
introduced by coercion
pop_data_male <- pop_data[, c(1, 21:39)]
pop_data_female <- pop_data[, c(1, 40:58)]
pop_data <- pop_data[, c(1, 2:20)]

Population

pop_data$...1  = toupper(pop_data$...1)
mpsz <- st_make_valid(mpsz)
total_pop <- merge(x = mpsz, y = pop_data, by.x = "SUBZONE_N", by.y = "...1", all.x = TRUE)
names(total_pop)[7:25] = c("Total", "0 - 4", "5 - 9", "10 - 14", "15 - 19","20 - 24", "25 - 29", "30 - 34", "35 - 39", "40 - 44", "45 - 49", "50 - 54", "55 - 59", "60 - 64", "65 - 69", "70 - 74", "75 - 79", "80 - 84", "85 & Over")
pop_data_male$...1  = toupper(pop_data_male$...1)
mpsz <- st_make_valid(mpsz)
male_pop <- merge(x = mpsz, y = pop_data_male, by.x = "SUBZONE_N", by.y = "...1", all.x = TRUE)
names(male_pop)[7:25] = c("Total", "0 - 4", "5 - 9", "10 - 14", "15 - 19","20 - 24", "25 - 29", "30 - 34", "35 - 39", "40 - 44", "45 - 49", "50 - 54", "55 - 59", "60 - 64", "65 - 69", "70 - 74", "75 - 79", "80 - 84", "85 & Over")
pop_data_female$...1  = toupper(pop_data_female$...1)
mpsz <- st_make_valid(mpsz)
female_pop <- merge(x = mpsz, y = pop_data_female, by.x = "SUBZONE_N", by.y = "...1", all.x = TRUE)
names(female_pop)[7:25] = c("Total", "0 - 4", "5 - 9", "10 - 14", "15 - 19","20 - 24", "25 - 29", "30 - 34", "35 - 39", "40 - 44", "45 - 49", "50 - 54", "55 - 59", "60 - 64", "65 - 69", "70 - 74", "75 - 79", "80 - 84", "85 & Over")
saveRDS(total_pop, file = "data/models/total_pop.rds")
saveRDS(male_pop, file = "data/models/male_pop.rds")
saveRDS(female_pop, file = "data/models/female_pop.rds")

Validity of Geometries of Train Stations

length(which(st_is_valid(trainstation_sf) == FALSE))
[1] 2

make valid cannot do

trainstation_sf <- trainstation_sf[st_is_valid(trainstation_sf) == TRUE,]
trainstation_sf <- trainstation_sf[!st_is_empty(trainstation_sf),,drop=FALSE]
length(which(st_is_valid(trainstation_sf) == FALSE))
[1] 0
length(which(st_is_valid(mpsz) == FALSE))
[1] 0
length(which(st_is_valid(mpsz) == FALSE))
[1] 0

Excluding Unnecessary Data Points

gp_sf <- st_intersection(mpsz, gp_sf)
Warning: attribute variables are assumed to be spatially constant throughout
all geometries
hospital_sf <- st_intersection(mpsz, hospital_sf)
Warning: attribute variables are assumed to be spatially constant throughout
all geometries
poly_sf <- st_intersection(mpsz, poly_sf)
Warning: attribute variables are assumed to be spatially constant throughout
all geometries
nursing_sf <- st_intersection(mpsz, nursing_sf)
Warning: attribute variables are assumed to be spatially constant throughout
all geometries
busstop_sf <- st_intersection(mpsz, busstop_sf)
Warning: attribute variables are assumed to be spatially constant throughout
all geometries
trainstation_sf <- st_intersection(mpsz, trainstation_sf)
Warning: attribute variables are assumed to be spatially constant throughout
all geometries
chas_sf <- st_intersection(mpsz, chas_sf)
Warning: attribute variables are assumed to be spatially constant throughout
all geometries
pcn_sf <- st_intersection(mpsz, pcn_sf)
Warning: attribute variables are assumed to be spatially constant throughout
all geometries
eldercare_sf <- st_intersection(mpsz, eldercare_sf)
Warning: attribute variables are assumed to be spatially constant throughout
all geometries

Data Visualisation

General Practitioner Clinics

tmap_mode("view")
tmap mode set to interactive viewing
tm_shape(total_pop) +
  tm_polygons(
    "Total",
    style = "cont",
    alpha = 0.4) +
  tm_fill(palette = "Blues") + 
tm_shape(gp_sf) + 
  tm_symbols(shape=24,
    col  = "blue",
    size = 0.15) +
  tm_layout(main.title="General Practitioner Clinics",
            main.title.position = "center") +
tm_view(set.zoom.limits = c(11,14),
          set.view = 11,
        set.bounds = TRUE) 
Warning: One tm layer group has duplicated layer types, which are omitted. To
draw multiple layers of the same type, use multiple layer groups (i.e. specify
tm_shape prior to each of them).
Symbol shapes other than circles or icons are not supported in view mode.

Hospitals

general_hospital = hospital_sf[hospital_sf$TYPE == "GENERAL",]
specialised_hospital = hospital_sf[hospital_sf$TYPE == "SPECIALISED",]
community_hospital = hospital_sf[hospital_sf$TYPE == "COMMUNITY",]


tm_shape(total_pop) +
  tm_polygons(
    "Total",
    style = "cont",
    alpha = 0.4) +
tm_shape(general_hospital) + 
  tm_symbols(
    shape=23,
    col  = "red",
    size = 0.15) +
tm_shape(specialised_hospital) + 
  tm_symbols(
    shape=23,
    col  = "blue",
    size = 0.15) +
tm_shape(community_hospital) + 
  tm_symbols(
    shape=23,
    col  = "green",
    size = 0.15) +
  tm_layout(main.title="Hospitals",
            main.title.position = "center")
Symbol shapes other than circles or icons are not supported in view mode.

Polyclinics

tmap_mode("plot")
tmap mode set to plotting
tm_shape(total_pop) +
  tm_polygons(
    "Total",
    style = "cont",
    alpha = 0.4) +
tm_shape(poly_sf) + 
  tm_symbols(shape=22,
    col  = "orange",
    size = 0.15) +
  tm_layout(main.title="Polyclinics",
            main.title.position = "center")

Nursing Homes

tmap_mode("plot")
tmap mode set to plotting
tm_shape(total_pop) +
  tm_polygons(
    "Total",
    style = "cont",
    alpha = 0.4) +
tm_shape(nursing_sf) + 
  tm_symbols(shape=21,
    col  = "green",
    size = 0.15) +
  tm_layout(main.title="Nursing Homes",
            main.title.position = "center")

Eldercare

tmap_mode("plot")
tmap mode set to plotting
tm_shape(total_pop) +
  tm_polygons(
    "Total",
    style = "cont",
    alpha = 0.4) +
tm_shape(eldercare_sf) + 
  tm_symbols(shape=21,
    col  = "green",
    size = 0.15) +
  tm_layout(main.title="Eldercare",
            main.title.position = "center")

Bus Stops

tmap_mode("plot")
tmap mode set to plotting
tm_shape(total_pop) +
  tm_polygons(
    "Total",
    style = "cont",
    alpha = 0.4) +
tm_shape(busstop_sf) + 
  tm_symbols(shape=20,
    col  = "darkblue",
    size = 0.15) +
  tm_layout(main.title="Bus Stops",
            main.title.position = "center")

MRT Stations

mrt <- trainstation_sf[trainstation_sf$TYP_CD_DES == "MRT",]
lrt <- trainstation_sf[trainstation_sf$TYP_CD_DES == "LRT",]

tmap_mode("plot")
tmap mode set to plotting
tm_shape(total_pop) +
  tm_polygons(
    "Total",
    style = "cont",
    alpha = 0.4) +
tm_shape(mrt) + 
  tm_symbols(shape=20,
    col  = "green",
    size = 0.15) +
tm_shape(lrt) + 
  tm_symbols(shape=20,
    col  = "darkgreen",
    size = 0.15) +
  tm_layout(main.title="Train Stations",
            main.title.position = "center")

CHAS

tmap_mode("plot")
tmap mode set to plotting
tm_shape(total_pop) +
  tm_polygons(
    "Total",
    style = "cont",
    alpha = 0.4,) +
  tm_fill(col="oranges") + 
tm_shape(chas_sf) + 
  tm_symbols(shape=24,
    col  = "blue",
    size = 0.15) +
  tm_layout(main.title="CHAS Clinics",
            main.title.position = "center")
Warning: One tm layer group has duplicated layer types, which are omitted. To
draw multiple layers of the same type, use multiple layer groups (i.e. specify
tm_shape prior to each of them).

First-order Spatial Point Patterns Analysis

Conversion of Datatypes

idk why train stations converts into spatial polygon

Converting sf data frames to sp’s Spatial class

mpsz_spatial <- as_Spatial(mpsz)
gp_spatial <- as_Spatial(gp_sf)
hospital_spatial <- as_Spatial(hospital_sf)
poly_spatial <- as_Spatial(poly_sf)
nursing_spatial <- as_Spatial(nursing_sf)
busstop_spatial <- as_Spatial(busstop_sf)
trainstation_spatial <- as_Spatial(trainstation_sf)
chas_spatial <- as_Spatial(chas_sf)
pcn_spatial <- as_Spatial(pcn_sf)
eldercare_spatial <- as_Spatial(eldercare_sf)

Converting sp’s *Spatial** Class into Generic sp Format

mpsz_sp <- as(mpsz_spatial, "SpatialPolygons")
gp_sp <- as(gp_spatial, "SpatialPoints")
hospital_sp <- as(hospital_spatial, "SpatialPoints")
poly_sp <- as(poly_spatial, "SpatialPoints")
nursing_sp <- as(nursing_spatial, "SpatialPoints")
busstop_sp <- as(busstop_spatial, "SpatialPoints")
chas_sp <- as(chas_spatial, "SpatialPoints")
pcn_sp <- as(pcn_spatial, "SpatialPoints")
eldercare_sp <- as(eldercare_spatial, "SpatialPoints")
trainstation_sp <- as(trainstation_spatial, "SpatialPolygons")

Converting Generic sp Format into spatstat’s ppp Format

gp_ppp <- as(gp_sp, "ppp")
hospital_ppp <- as(hospital_sp, "ppp")
poly_ppp <- as(poly_sp, "ppp")
nursing_ppp <- as(nursing_sp, "ppp")
busstop_ppp <- as(busstop_sp, "ppp")
chas_ppp <- as(chas_sp, "ppp")
pcn_ppp <- as(pcn_sp, "ppp")
eldercare_ppp <- as(eldercare_sp, "ppp")

Data Visualisation

plot(mpsz_spatial, main="General Practitioner Clinics")

plot(gp_ppp, main="General Practitioner Clinics")

plot(hospital_ppp, main="Hospitals")

plot(poly_ppp, main="Polyclinics")

plot(nursing_ppp, main="Nursing Homes")

plot(eldercare_ppp, main="Eldercares")

plot(busstop_ppp, main="Bus Stops")

plot(trainstation_spatial, main="Mrt Stations")

plot(chas_ppp, main="CHAS Clinics")

Check for Duplicate Data Points

any(duplicated(gp_ppp))
[1] TRUE
any(duplicated(hospital_ppp))
[1] FALSE
any(duplicated(poly_ppp))
[1] FALSE
any(duplicated(nursing_ppp))
[1] TRUE
any(duplicated(busstop_ppp))
[1] TRUE
any(duplicated(chas_ppp))
[1] TRUE
any(duplicated(eldercare_ppp))
[1] FALSE

Handle Duplicated Points

If we want to know how many locations have more than one point event, we can use the code chunk below.

sum(multiplicity(gp_ppp) > 1)
[1] 554
sum(multiplicity(nursing_ppp) > 1)
[1] 4
sum(multiplicity(busstop_ppp) > 1)
[1] 2
sum(multiplicity(pcn_ppp) > 1)
[1] 268

The second solution is use jittering, which will add a small perturbation to the duplicate points so that they do not occupy the exact same space.

gp_ppp_jit <- rjitter(gp_ppp,retry = TRUE,
                             nsim = 1, 
                             drop = TRUE)
any(duplicated(gp_ppp_jit))
[1] FALSE
nursing_ppp_jit <- rjitter(nursing_ppp,retry = TRUE,
                             nsim = 1, 
                             drop = TRUE)
any(duplicated(nursing_ppp_jit))
[1] FALSE
busstop_ppp_jit <- rjitter(busstop_ppp,retry = TRUE,
                             nsim = 1, 
                             drop = TRUE)
any(duplicated(busstop_ppp_jit))
[1] FALSE
chas_ppp_jit <- rjitter(chas_ppp,retry = TRUE,
                             nsim = 1, 
                             drop = TRUE)
any(duplicated(chas_ppp_jit))
[1] FALSE
pcn_ppp_jit <- rjitter(pcn_ppp,retry = TRUE,
                             nsim = 1, 
                             drop = TRUE)
any(duplicated(pcn_ppp_jit))
[1] FALSE

Creating owin Object

mpsz_owin <- as(mpsz_sp, "owin")
gp_ppp = gp_ppp_jit[mpsz_owin]
hospital_ppp = hospital_ppp[mpsz_owin]
poly_ppp = poly_ppp[mpsz_owin]
nursing_ppp = nursing_ppp_jit[mpsz_owin]
busstop_ppp = busstop_ppp_jit[mpsz_owin]
chas_ppp = chas_ppp_jit[mpsz_owin]
pcn_ppp = pcn_ppp_jit[mpsz_owin]
eldercare_ppp = eldercare_ppp[mpsz_owin]
mpsz_owin
window: polygonal boundary
enclosing rectangle: [2667.54, 56396.44] x [15748.72, 50256.33] units

Kernel Density Estimation (KDE)

rescale

gp_ppp.km <- rescale(gp_ppp, 1000, "km")
hospital_ppp.km <- rescale(hospital_ppp, 1000, "km")
poly_ppp.km <- rescale(poly_ppp, 1000, "km")
nursing_ppp.km <- rescale(nursing_ppp, 1000, "km")
busstop_ppp.km <- rescale(busstop_ppp, 1000, "km")
chas_ppp.km <- rescale(chas_ppp, 1000, "km")
pcn_ppp.km <- rescale(pcn_ppp, 1000, "km")
eldercare_ppp.km <- rescale(eldercare_ppp, 1000, "km")

saveRDS(chas_ppp.km, file = "data/models/chas_ppp_km.rds")

General Practitioner Clinics

gp_bw <- density(gp_ppp.km,
             sigma = bw.diggle,
             edge = TRUE,
             kernel = "gaussian")


plot(gp_bw, main = "General Practitioner Clinics")

Hospitals

hospital_bw <- density(hospital_ppp.km,
             sigma = bw.diggle,
             edge = TRUE,
             kernel = "gaussian")

plot(hospital_bw, main = "Hospitals")

Polyclinics

poly_bw <- density(poly_ppp.km,
             sigma = bw.diggle,
             edge = TRUE,
             kernel = "gaussian")

plot(poly_bw, main = "Polyclinics")

Nursing Homes

nursing_bw <- density(nursing_ppp.km,
             sigma = bw.diggle,
             edge = TRUE,
             kernel = "gaussian")

plot(nursing_bw, main = "Nursing Homes")

CHAS Clinics

chas_bw <- density(chas_ppp.km,
             sigma = bw.diggle,
             edge = TRUE,
             kernel = "gaussian")

plot(chas_bw, main = "CHAS Clinics")

PCN

pcn_bw <- density(pcn_ppp.km,
             sigma = bw.diggle,
             edge = TRUE,
             kernel = "gaussian")

plot(pcn_bw, main = "Primary Care Networks (PCN)")

Bus Stops

busstop_bw <- density(busstop_ppp.km,
             sigma = bw.diggle,
             edge = TRUE,
             kernel = "gaussian")

plot(busstop_bw, main = "Bus Stops")

Converting KDE Output into Grid Object

General Practitioner Clinics

gridded_gp_bw <- as.SpatialGridDataFrame.im(gp_bw)
spplot(gridded_gp_bw, main = "General Practitioners")

Hospitals

gridded_hospital_bw <- as.SpatialGridDataFrame.im(hospital_bw)
spplot(gridded_hospital_bw)

Polyclinics

gridded_poly_bw <- as.SpatialGridDataFrame.im(poly_bw)
spplot(gridded_poly_bw)

Nursing Homes

gridded_nursing_bw <- as.SpatialGridDataFrame.im(nursing_bw)
spplot(gridded_nursing_bw)

Bus Stops

gridded_busstop_bw <- as.SpatialGridDataFrame.im(busstop_bw)
spplot(gridded_busstop_bw)

CHAS

# gridded_chas_bw <- as.SpatialGridDataFrame.im(chas_bw)
# spplot(gridded_chas_bw)

Converting Gridded Output into Raster

General Practitioner Clinics

kde_gp_bw_raster <- raster(gridded_gp_bw)
projection(kde_gp_bw_raster) <- CRS("+init=EPSG:3414")
kde_gp_bw_raster
class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.419757, 0.2695907  (x, y)
extent     : 2.667538, 56.39644, 15.74872, 50.25633  (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 +units=m +no_defs 
source     : memory
names      : v 
values     : -1.971092e-14, 97.19965  (min, max)

Hospitals

kde_hospital_bw_raster <- raster(gridded_hospital_bw)
projection(kde_hospital_bw_raster) <- CRS("+init=EPSG:3414")
kde_hospital_bw_raster
class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.419757, 0.2695907  (x, y)
extent     : 2.667538, 56.39644, 15.74872, 50.25633  (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 +units=m +no_defs 
source     : memory
names      : v 
values     : -6.617214e-17, 0.6530283  (min, max)

Polyclinics

kde_poly_bw_raster <- raster(gridded_poly_bw)
projection(kde_poly_bw_raster) <- CRS("+init=EPSG:3414")
kde_poly_bw_raster
class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.419757, 0.2695907  (x, y)
extent     : 2.667538, 56.39644, 15.74872, 50.25633  (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 +units=m +no_defs 
source     : memory
names      : v 
values     : 1.253552e-17, 0.1114663  (min, max)

Nursing Homes

kde_nursing_bw_raster <- raster(gridded_nursing_bw)
projection(kde_nursing_bw_raster) <- CRS("+init=EPSG:3414")
kde_nursing_bw_raster
class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.419757, 0.2695907  (x, y)
extent     : 2.667538, 56.39644, 15.74872, 50.25633  (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 +units=m +no_defs 
source     : memory
names      : v 
values     : -1.246191e-16, 0.87121  (min, max)

Bus Stops

kde_busstop_bw_raster <- raster(gridded_busstop_bw)
projection(kde_busstop_bw_raster) <- CRS("+init=EPSG:3414")
kde_busstop_bw_raster
class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.419757, 0.2695907  (x, y)
extent     : 2.667538, 56.39644, 15.74872, 50.25633  (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 +units=m +no_defs 
source     : memory
names      : v 
values     : -1.15015e-14, 46.68152  (min, max)

CHAS

kde_busstop_bw_raster <- raster(gridded_busstop_bw)
projection(kde_busstop_bw_raster) <- CRS("+init=EPSG:3414")
kde_busstop_bw_raster
class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.419757, 0.2695907  (x, y)
extent     : 2.667538, 56.39644, 15.74872, 50.25633  (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 +units=m +no_defs 
source     : memory
names      : v 
values     : -1.15015e-14, 46.68152  (min, max)

Visualisation

General Practitioner Clinics

tm_shape(kde_gp_bw_raster) +
  tm_raster("v") +
  tm_layout(legend.position = c("right", "bottom"), frame = FALSE)
Variable(s) "v" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.

Hospitals

tm_shape(kde_hospital_bw_raster) +
  tm_raster("v") +
  tm_layout(legend.position = c("right", "bottom"), frame = FALSE)
Variable(s) "v" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.

Polyclinics

tm_shape(kde_poly_bw_raster) +
  tm_raster("v") +
  tm_layout(legend.position = c("right", "bottom"), frame = FALSE)

Nursing Homes

tm_shape(kde_nursing_bw_raster) +
  tm_raster("v") +
  tm_layout(legend.position = c("right", "bottom"), frame = FALSE)
Variable(s) "v" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.

Bus Stops

tm_shape(kde_busstop_bw_raster) +
  tm_raster("v") +
  tm_layout(legend.position = c("right", "bottom"), frame = FALSE)
Variable(s) "v" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.

CHAS Clinics

chas_bw <- density(chas_ppp.km,
             sigma = bw.diggle,
             edge = TRUE,
             kernel = "gaussian")

plot(chas_bw, main = "CHAS Clinics")

tm_shape(kde_busstop_bw_raster) +
  tm_raster("v") +
  tm_layout(legend.position = c("right", "bottom"), frame = FALSE)
Variable(s) "v" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.

Nearest Neighbour Analysis

General Practitioner Clinics

  • Ho = The distribution of general practitioner clinics are randomly distributed.

  • H1 = The distribution of general practitioner clinics are not randomly distributed.

With the p-value lower than the alpha value of 0.05, we reject the null hypothesis and accept that general practitioner clinics are not randomly distributed.

clarkevans.test(gp_ppp.km,
                correction = "none", 
                clipregion = "mpsz_owin", 
                alternative = c("clustered"), 
                nsim = 99)

    Clark-Evans test
    No edge correction
    Monte Carlo test based on 99 simulations of CSR with fixed n

data:  gp_ppp.km
R = 0.34517, p-value = 0.01
alternative hypothesis: clustered (R < 1)

Hospitals

  • Ho = The distribution of hospitals are randomly distributed.

  • H1 = The distribution of hospitals are not randomly distributed.

With the p-value lower than the alpha value of 0.05, we reject the null hypothesis and accept that hospitals are not randomly distributed.

clarkevans.test(hospital_ppp.km,
                correction = "none", 
                clipregion = "mpsz_owin", 
                alternative = c("clustered"), 
                nsim = 99)

    Clark-Evans test
    No edge correction
    Monte Carlo test based on 99 simulations of CSR with fixed n

data:  hospital_ppp.km
R = 0.45977, p-value = 0.01
alternative hypothesis: clustered (R < 1)

Polyclinics

  • Ho = The distribution of polyclinics are randomly distributed.

  • H1 = The distribution of polyclinics are not randomly distributed.

With the p-value above than the alpha value of 0.05, we accept the null hypothesis and accept that polyclinics are randomly distributed.

clarkevans.test(poly_ppp.km,
                correction = "none", 
                clipregion = "mpsz_owin", 
                alternative = c("clustered"), 
                nsim = 99)

    Clark-Evans test
    No edge correction
    Monte Carlo test based on 99 simulations of CSR with fixed n

data:  poly_ppp.km
R = 1.0189, p-value = 0.14
alternative hypothesis: clustered (R < 1)

Nursing Homes

  • Ho = The distribution of nursing homes are randomly distributed.

  • H1 = The distribution of nursing homes are not randomly distributed.

With the p-value lower than the alpha value of 0.05, we reject the null hypothesis and accept that nursing homes are not randomly distributed.

clarkevans.test(nursing_ppp.km,
                correction = "none", 
                clipregion = "mpsz_owin", 
                alternative = c("clustered"), 
                nsim = 99)

    Clark-Evans test
    No edge correction
    Monte Carlo test based on 99 simulations of CSR with fixed n

data:  nursing_ppp.km
R = 0.71101, p-value = 0.01
alternative hypothesis: clustered (R < 1)

CHAS Clinics

  • Ho = The distribution of CHAS clinics are randomly distributed.

  • H1 = The distribution of CHAS clinics are not randomly distributed.

With the p-value lower than the alpha value of 0.05, we reject the null hypothesis and accept that CHAS clinics are not randomly distributed.

clarkevans.test(chas_ppp.km,
                correction = "none", 
                clipregion = "mpsz_owin", 
                alternative = c("clustered"), 
                nsim = 99)

    Clark-Evans test
    No edge correction
    Monte Carlo test based on 99 simulations of CSR with fixed n

data:  chas_ppp.km
R = 0.48715, p-value = 0.01
alternative hypothesis: clustered (R < 1)

Modelling Geographical Accessibility

PCN_Clinics <- st_read(dsn = "data/geospatial/PCN Network Clinics Shapefile", 
                       layer = "PCN Network Clinics") %>%
  st_transform(crs = 3414)
Reading layer `PCN Network Clinics' from data source 
  `C:\deadline2359\IS415-Project\posts\data_cleaning\data\geospatial\PCN Network Clinics Shapefile' 
  using driver `ESRI Shapefile'
replacing null geometries with empty geometries
Simple feature collection with 828 features and 20 fields (with 1 geometry empty)
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 4985.536 ymin: 26424.19 xmax: 45002.49 ymax: 48986.95
Projected CRS: SVY21 / Singapore TM