Hands-on Exercise 7B

Author

Janet Toa

Published

February 26, 2024

Modified

February 26, 2024

22 Visualising Geospatial Point Data

22.1 Overview and Learning Outcomes

This hands-on exercise is based on Chapter 22 of the R for Visual Analytics book.

Proportional symbol maps (also known as graduate symbol maps) are a class of maps that use the visual variable of size to represent differences in the magnitude of a discrete, abruptly changing phenomenon, e.g. counts of people. Like choropleth maps, you can create classed or unclassed versions of these maps. The classed ones are known as range-graded or graduated symbols, and the unclassed are called proportional symbols, where the area of the symbols are proportional to the values of the attribute being mapped. In this hands-on exercise, you will learn how to create a proportional symbol map showing the number of wins by Singapore Pools’ outlets using an R package called tmap.

By the end of this hands-on exercise, you will acquire the following skills by using appropriate R packages:

To import an aspatial data file into R.
To convert it into simple point feature data frame and at the same time, to assign an appropriate projection reference to the newly create simple point feature data frame.
To plot interactive proportional symbol maps.

22.2 Getting Started

22.2.1 Installing and Loading Required Libraries

In this hands-on exercise, the following R packages are used:

tidyverse (i.e. readr, tidyr, dplyr) for performing data science tasks such as importing, tidying, and wrangling data;
sf for handling geospatial data; and
tmap for thematic mapping.

The code chunk below uses the p_load() function in the pacman package to check if the packages are installed. If yes, they are then loaded into the R environment. If no, they are installed, then loaded into the R environment.

pacman::p_load(tidyverse, sf, tmap)

22.2.2 Importing and Preparing Data

The dataset for this hands-on exercise, SGPools_svy21, which is in the csv file format, is imported into the R environment using the read_csv() function in the readr package and stored as the R object, sgpools.

sgpools = read_csv("data/aspatial/SGPools_svy21.csv")

The tibble data frame, sgpools, has 7 columns and 306 rows. The “XCOORD” and “YCOORD” variables are the x-coordinates and y-coordinates of Singapore Pools outlets/branches. They are in the Singapore SVY21 Projected Coordinates System.

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

The sgpools data frame is then converted into a simple feature data frame using the st_as_sf() function in the sf package.

The “coords” argument requires the column name of the x-coordinates first followed by the column name of the y-coordinates.

The “crs” argument requires the coordinates system in epsg format. EPSG: 3414 is the Singapore SVY21 Projected Coordinate System.

Figure below shows the data table of sgpools_sf. Notice that

sgpools_sf = st_as_sf(sgpools, 
                       coords = c("XCOORD", "YCOORD"),
                       crs= 3414)

A new column called geometry has been added into the sf data frame, sgpools_sf, which is a point feature class.

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]>

22.3 Drawing Proportional Symbol Map

The “view” mode is turned on via the tmap_mode() function to create an interactive proportional symbol map.

tmap_mode("view")

22.3.1 Starting with Interactive Point Symbol Map

An interactive point symbol map is created below to show where the Singapore Pools outlets/branches are.

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

22.3.2 Making Map Proportional

A numerical variable is assigned to the “size” attribute to draw a proportional symbol map. The variable “Gp1Gp2Winnings” is assigned in the plot below.

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

22.3.3 Using Different Colours

The proportional symbol map can be further improved by using the “col” attribute. The “OUTLET_TYPE“ variable is used as the colour attribute variable in the plot below.

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

22.3.4 Plotting Facet Plots

An impressive and little-know feature of tmap’s view mode is that it also works with faceted plots. The “sync” argument the in tm_facets() function can be used 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)

Before ending the session, the tmap_mode() function is used to switch the mode back to “plot”.

tmap_mode("plot")