4. Making a glyph map
In cubble: A Vector Spatio-Temporal Data Structure for Data Analysis

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  message = FALSE, 
  warning = FALSE,
  out.width = "100%"
)

library(dplyr)
library(cubble)
library(ggplot2)
library(tsibble)

Sometimes, we wish to communicate spatial and temporal information collectively through visualisation. This can be achieved through several graphical displays: one can make faceted maps across time, creating map animations, or constructing interactive graphics to link between map and time series plot. While interactive graphics will be the main focus of vignette 6. Interactive graphics, this vignette will introduce a specific type of spatio-temporal plot called glyph maps.

Understanding glyph maps

The concept of glyph maps was initially proposed in @Wickham2012-yr. The underlying idea is to transform the temporal coordinates into spatial coordinates so that time series plot can be displayed on the map. The diagram below illustrates how the coordinate transformation:

knitr::include_graphics("cluster-diagram/glyph-steps.png")

Subplot (1) show the spatial location of a weather station and subplot (2) displays its associated maximum temperature as time series in 2020. In subplot (3), the temporal coordinates are transformed into the spatial coordinates using linear algebra with a defined height and width (Equation 1 in @Wickham2012-yr), while the time series glyph remains unchanged. The transformed time series can then be plotted as a layer on the map in (4).

The package GGally initially implement the glyph map. It uses glyphs() to calculate the axis transformation and then uses geom_polygon() to draw the map. In cubble, a ggproto implementation geom_glyph() performs the linear algebra internally as data transformation . The geom_glyph() requires four aesthetics: x_major, y_major, x_minor, and y_minor. The major axes are the outer spatial coordinates and the minor axes are the inner/ temporal coordinates:

data |> 
  ggplot() +
  geom_glyph(aes(x_major = ..., x_minor = ..., y_major = ..., y_minor = ...))

Reference line and box can be added by separate geoms (geom_glyph_box(), geom_glyph_line()) with the same aesthetics (x_major, x_minor, y_major, y_minor). To avoid repetition, you may want specify the aesthetics collectively inside ggplot():

data |> 
  ggplot(aes(x_major = ..., x_minor = ..., y_major = ..., y_minor = ...)) +
  geom_glyph_box() + 
  geom_glyph_line() + 
  geom_glyph()

If you want add an undelying map which does not use the four glyph map aesthetics, the argument inherit.aes = FALSE is handy:

data |> 
  ggplot(aes(x_major = ..., x_minor = ..., y_major = ..., y_minor = ...)) +
  geom_sf(data = MAP_DATA, inherit.aes = FALSE)
  geom_glyph_box() + 
  geom_glyph_line() + 
  geom_glyph()

Monthly average maximum temperature in Victoria, Australia

Global Historical Climatology Network (GHCN) provides daily climate measures from stations across the world. The dataset climate_aus stores climate variables (precipitation, maximum and minimum temperature) for r nrow(climate_aus) Australian stations in 2020. This is a lot of stations to work with and we will start with a randomly sample 80 stations (since not all the stations have the full year record, we will only consider those that have 366 days for 2020):

set.seed(12345)
(tmax <- climate_aus %>% 
    rowwise() %>% 
    filter(nrow(ts) == 366) %>% 
    slice_sample(n = 80))

Next, we would like to summarise the daily maximum temperature into monthly. This can be done with the dplyr group_by + summarise:

(tmax <- tmax %>%
  face_temporal() %>%
  group_by(month = tsibble::yearmonth(date)) %>%
  summarise(tmax = mean(tmax, na.rm = TRUE)))

One requirement for the data to be plot with ggplot2 is that all the variables mapped to aesthetics need to be store in the same table. In cubble, you can move the spatial variables (e.g. long and lat) into the temporal cubble with unfold():

(tmax <- tmax %>% unfold(long, lat))

Using the glyph map syntax introduced in this vignette, we can then create a glyph map:

tmax %>% 
  ggplot(aes(x_major = long, y_major = lat, 
             x_minor = month, y_minor = tmax))  + 
  geom_sf(data = ozmaps::abs_ste, 
          fill = "grey95", color = "white",
          inherit.aes = FALSE) + 
  geom_glyph_box(width = 1, height = 0.5) + 
  geom_glyph(width = 1,  height = 0.5) + 
  coord_sf(xlim = c(110, 155)) + 
  theme_void() + 
  theme(legend.position = "bottom") + 
  labs(x = "Longitude", y = "Latitude")

# script for diagram 
library(tidyverse)
library(ggsvg)
library(patchwork)
nsw <- ozmaps::abs_ste %>% 
  filter(NAME %in% c("New South Wales")) %>% 
  sf::st_simplify(dTolerance = 4000)

single <- climate_aus %>% filter(id == "ASN00076031")
glyph_dt <- single %>% face_temporal() %>% unfold(long, lat)
p1 <- ggplot() + 
  geom_sf(data = nsw,fill = "transparent", linetype = "dotted")+ 
  geom_point(data = single, aes(x = long, y = lat), color = "#443750") + 
  theme_bw() + 
  coord_sf(xlim = c(141, 143), ylim = c(-35, -33.5)) + 
  scale_x_continuous(breaks = seq(140, 143, 1)) + 
  scale_y_continuous(breaks = seq(-35, -33, 1)) + 
  ggtitle("(1)")

p2 <- single %>% 
  face_temporal() %>% 
  ggplot(aes(x = date, y = tmax)) + 
  geom_line(color = "#443750") + 
  theme_bw() + 
  theme() + 
  ggtitle("(2)")

glyph <- glyph_dt %>% 
  ggplot(aes(x_major = long, x_minor = as.numeric(date),
             y_major = lat, y_minor = tmax)) + 
  geom_glyph(width = 1, height = 0.3)

p3 <- layer_data(glyph) %>% 
  ggplot(aes(x = x, y = y)) + 
  geom_line(color = "#443750") +
  theme_bw() + 
  theme(axis.line = element_line(color = "#840032"),
        axis.text = element_text(color = "#840032", size = 10),
  ) + 
  ggtitle("(3)") + xlab("long") + ylab("lat")

p4 <- glyph_dt %>% 
  ggplot(aes(x_major = long, x_minor = as.numeric(date),
             y_major = lat, y_minor = tmax)) + 
  geom_sf(data = nsw, fill = "transparent", 
          linetype = "dotted", inherit.aes = FALSE) + 
  geom_glyph_box(width = 1, height = 0.3, color= "#840032", size = 1.2) + 
  geom_glyph(color = "#443750", width = 1, height = 0.3) + 
  geom_point(data = single, aes(x = long, y = lat), 
             color = "#443750", inherit.aes = FALSE) + 
  theme_bw() + 
  coord_sf(xlim = c(141, 143), ylim = c(-35, -33.5)) + 
  scale_x_continuous(breaks = seq(140, 143, 1)) + 
  scale_y_continuous(breaks = seq(-35, -33, 1)) + 
  ggtitle("(4)") + xlab("long") + ylab("lat")

g2 <- (p1 | p2) / (p4 | p3) + plot_layout(guides='collect') &
  theme(legend.position='none')

ggsave(g2, 
       filename = here::here("vignettes/cluster-diagram/glyph-steps.png"), 
       height = 4)