Hands-on Exercise 8 Part II - Visualising Geospatial Point Data

Author

You Ting QUEK

Published

June 7, 2024

Modified

June 8, 2024

Learning Objectives:

Getting Started

Installing and loading the required libraries

The following R packages will be used:

  • Tidyverse:

    • readr for importing delimited text file,

    • tidyr for tidying data,

    • dplyr for wrangling data and

  • sf for handling geospatial data

  • tmap for plotting choropleth maps

Code chunk below will be used to check if these packages have been installed and also will load them into the working R environment.

pacman::p_load(sf, tmap, tidyverse)

Geospatial Data Wrangling

The Data

The data set used is called SGPools_svy21. The data is in csv file format.

Figure below shows the first 15 records of SGPools_svy21.csv. It consists of seven columns. The XCOORD and YCOORD columns are the x-coordinates and y-coordinates of SingPools outlets and branches. They are in Singapore SVY21 Projected Coordinates System.

Data Import and Preparation

The code chunk below uses read_csv() function of readr package to import SGPools_svy21.csv into R as a tibble data frame called sgpools.

sgpools <- read_csv("data/aspatial/SGPools_svy21.csv")

Examining the data content

list(sgpools) 
[[1]]
# A tibble: 306 × 7
   NAME           ADDRESS POSTCODE XCOORD YCOORD `OUTLET TYPE` `Gp1Gp2 Winnings`
   <chr>          <chr>      <dbl>  <dbl>  <dbl> <chr>                     <dbl>
 1 Livewire (Mar… 2 Bayf…    18972 30842. 29599. Branch                        5
 2 Livewire (Res… 26 Sen…    98138 26704. 26526. Branch                       11
 3 SportsBuzz (K… Lotus …   738078 20118. 44888. Branch                        0
 4 SportsBuzz (P… 1 Sele…   188306 29777. 31382. Branch                       44
 5 Prime Serango… Blk 54…   552542 32239. 39519. Branch                        0
 6 Singapore Poo… 1A Woo…   731001 21012. 46987. Branch                        3
 7 Singapore Poo… Blk 64…   370064 33990. 34356. Branch                       17
 8 Singapore Poo… Blk 88…   370088 33847. 33976. Branch                       16
 9 Singapore Poo… Blk 30…   540308 33910. 41275. Branch                       21
10 Singapore Poo… Blk 20…   560202 29246. 38943. Branch                       25
# ℹ 296 more rows
Note

Notice that the sgpools data in tibble data frame and not the common R data frame.

Creating a sf data frame from an aspatial data frame

The code chunk below converts sgpools data frame into a simple feature data frame by using st_as_sf() of sf packages

sgpools_sf <- st_as_sf(sgpools, 
                       coords = c("XCOORD", "YCOORD"),
                       crs= 3414)
Note
  • The coords argument requires the column name of the x-coordinates to be provided first, followed by the column name of the y-coordinates.

  • The crs argument requires the coordinates system in epsg format. EPSG: 3414 is Singapore SVY21 Projected Coordinate System to be provided. Country epsg codes can be found at epsg.io.

Figure below shows the data table of sgpools_sf. Notice that a new column called geometry has been added into the data frame.

Display the basic information of the newly created sgpools_sf 

list(sgpools_sf)
[[1]]
Simple feature collection with 306 features and 5 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 7844.194 ymin: 26525.7 xmax: 45176.57 ymax: 47987.13
Projected CRS: SVY21 / Singapore TM
# A tibble: 306 × 6
   NAME                         ADDRESS POSTCODE `OUTLET TYPE` `Gp1Gp2 Winnings`
 * <chr>                        <chr>      <dbl> <chr>                     <dbl>
 1 Livewire (Marina Bay Sands)  2 Bayf…    18972 Branch                        5
 2 Livewire (Resorts World Sen… 26 Sen…    98138 Branch                       11
 3 SportsBuzz (Kranji)          Lotus …   738078 Branch                        0
 4 SportsBuzz (PoMo)            1 Sele…   188306 Branch                       44
 5 Prime Serangoon North        Blk 54…   552542 Branch                        0
 6 Singapore Pools Woodlands C… 1A Woo…   731001 Branch                        3
 7 Singapore Pools 64 Circuit … Blk 64…   370064 Branch                       17
 8 Singapore Pools 88 Circuit … Blk 88…   370088 Branch                       16
 9 Singapore Pools Anchorvale … Blk 30…   540308 Branch                       21
10 Singapore Pools Ang Mo Kio … Blk 20…   560202 Branch                       25
# ℹ 296 more rows
# ℹ 1 more variable: geometry <POINT [m]>
Note

The output shows that sgppols_sf is in point feature class. It’s epsg ID is 3414. The bbox provides information of the extend of the geospatial data.

Drawing Proportional Symbol Map

To create an interactive proportional symbol map in R, the view mode of tmap will be used.

tmap_mode("view")

Interactive point symbol map

The code chunks below are used to create an interactive point symbol map.

tm_shape(sgpools_sf)+
tm_bubbles(col = "red",
           size = 1,
           border.col = "black",
           border.lwd = 1)

Proportional Symbol Map

To draw a proportional symbol map, the numerical variable needs to be assigned to the size visual attribute. The code chunk below shows the variable Gp1Gp2Winnings is assigned to size visual attribute.

tm_shape(sgpools_sf)+
tm_bubbles(col = "red",
           size = "Gp1Gp2 Winnings",
           border.col = "black",
           border.lwd = 1)

Colour Visual Attribute

The proportional symbol map can be further improved by using the colour visual attribute. In the code chunk below, OUTLET_TYPE variable is used as the colour attribute variable.

tm_shape(sgpools_sf)+
tm_bubbles(col = "OUTLET TYPE", 
          size = "Gp1Gp2 Winnings",
          border.col = "black",
          border.lwd = 1)

Faceted Plots

tmap’s view mode also works with faceted plots. The argument sync in tm_facets() can be used in this case to produce multiple maps with synchronised zoom and pan settings.

tm_shape(sgpools_sf) +
  tm_bubbles(col = "OUTLET TYPE", 
          size = "Gp1Gp2 Winnings",
          border.col = "black",
          border.lwd = 1) +
  tm_facets(by= "OUTLET TYPE",
            nrow = 1,
            sync = TRUE)

Switch tmap’s viewer back to plot mode

tmap_mode("plot")