Before we do anything, we need to download the OneMap API.

library(onemapsgapi)

In order to use OneMap, we need to generate a token.

token <- get_token("user@example.com", "password")

Remember to install and load the R packages.

packages = c('maptools', 'sf', 'raster','spatstat', 'tmap', 'tidyverse')
for (p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p,character.only = T)
}

Step 1: Importing Geospatial Data

Let’s first import the CoastalOutline and Subzone shapefiles.

sg_sf <- st_read(dsn = "data/shapefile", 
                 layer= "CostalOutline")
mpsz_sf <- st_read(dsn = "data/shapefile", 
                   layer= "MP14_SUBZONE_WEB_PL")

Step 2: Importing Aspatial Data

Let’s import the Childcare data next.

childcare <- read_rds("data/rds/childcare.rds")
CHAS <- read_rds("data/rds/CHAS.rds")

After importing all the necessary data, ensure that you check each dataset; make this a good habit to have!

Step 3: Converting aspatial to geospatial

Before converting, notice that CHAS is not in sf yet. Therefore, we must use the function: st_as_sf and change the geographic coordinates to 3414 (This is the standard for Singapore data).

CHAS_sf <- st_as_sf(CHAS,
                    coords = c("X_COORDINATE",
                               "Y_COORDINATE"),
                    crs = 3414)

We also need to set the coordinates and CRS for childcare dataset.

childcare$Lat <- as.numeric(childcare$Lat)
childcare$Lng <- as.numeric(childcare$Lng)
childcare_sf <- st_as_sf(childcare,
                          coords = c("Lng",
                                     "Lat"),
                          crs = 4326) %>% # here is assigned as 4326 because the data was originally 4326 
  # (if it has decimal points in the dataset, its most likely 4326)
  st_transform(crs = 3414) # here is changing crs 4326 to crs 3414 because we want to keep it equal and 
# within the Singapore geographic coordinate system

Fun Fact:

This function chains code lines together:

%>%

It helps the user perform multiple operations at one go!

To view the dataset, you can use this function:

view(childcare_sf)

If not, you could also double click on the dataset under Environment.

Step 4: Geospatial Data Wrangling

Converting from sf to Spatial class

childcare_spatial <- as_Spatial(childcare_sf)
CHAS_spatial <- as_Spatial(CHAS_sf)
sg <- as_Spatial(sg_sf)

Converting from Spatial class to sp format

childcare_sp <- as(childcare_spatial, "SpatialPOints")
CHAS_sp <- as(CHAS_spatial, "SpatialPoints")
sg_sp <- as(sg, "SpatialPoints")

Converting from sp to spatstat ppp format

childcare_ppp <- as(childcare_sp, "ppp")
CHAS_ppp <- as(CHAS_sp, "ppp")
sg_ppp <- as(sg_sp, "ppp")

Step 5: Plotting the data

tmap_mode('view')
tm_shape(childcare_sf) +
  tm_dots(alpha=0.4,
          col="blue",
          size=0.05) +
tm_shape(CHAS_sf) +
  tm_dots(alpha=0.4,
          col="red",
          size=0.05)

The more intense the colour of the data point, the more childcare centers there are in the area.

Take note to not put too many interactive maps! It might cause your blog to overload.

In-Class Exercise 4