Creating a hexagon map of Tasmania
Tasmania is the southern-most state of Australia, it has one large land mass and several smaller islands.
Data
We will use the Australian Bureau of Statistics’ ESRI shape files to build our map.
The set has been filtered for only Tasmanian areas. The data set of
Tasmanian Statistical Areas at level two has been provided in the
sugarbag package.
Alternatively the data can be created from the set of Australian areas using the following code:
library(strayr)
strayr::sa22016 %>%
filter(state_name_2016 == "Tasmania") %>%
# convert to long lat
st_transform(., crs = "+proj=longlat +ellps=GRS80")Centroids
The function create_centroids finds the central points
of the polygons provided as an argument.
# Find the longitude and latitude centroid for each region or area
centroids <- create_centroids(tas_sa2, sf_id = "sa2_name_2016")Hexagon grid
To tessellate correctly, all the hexagons must be evenly spaced. This function creates a grid of possible locations for the polygons.
grid <- create_grid(centroids = centroids, hex_size = 0.2, buffer_dist = 1.2)The sugarbag package operates by creating a grid of
possible hexagons to allocate electorates. The buffer extends the grid
beyond the geographical space, this is especially useful for densely
populated coastal areas or cities, such as Brisbane and Sydney in this
case, Hobart.
Allocate areas
Each polygon centroid will be allocated to the closest available
hexagon grid point. The capital cities data set will be used to preserve
neighbourly relationships. The allocate function requires
two inputs, the centroids and the grid.
# Allocate the centroids to the hexagon grid
# We have the same amount of rows, as individual regions
hex_allocated <- allocate(centroids = centroids,
sf_id = "sa2_name_2016",
hex_grid = grid,
hex_size = 0.2, # same size used in create_grid
hex_filter = 10,
focal_points = capital_cities,
# same column used in create_centroids
width = 30, verbose = TRUE)
#> Finding closest point in focal_points data set.
#> Allocating centroids, in order of distance to closest focal point.The function fortify_hexagon assists in plotting. We now
have 6 points per region, one for each point of a hexagon. Connecting
these points will allow actual hexagons to be plotted.
The additional demographic information or data can now be added. This can be used to allow plots to be coloured by region.
For animations to move between geography and hexagons the
sf_id must match, there also needs to be an identifier to
separate the states to animate between for gganimate.
hexagons <- hex_allocated %>%
fortify_hexagon(hex_size = 0.2, sf_id = "sa2_name_2016") %>%
left_join(., tas_sa2) %>%
mutate(poly_type = "hex")
#> Joining, by = "sa2_name_2016"
polygons <- fortify_sfc(tas_sa2) %>%
mutate(poly_type = "geo")
ggplot(mapping = aes(fill = sa4_name_2016)) +
geom_polygon(data = polygons, aes(x=long, lat, group = interaction(sa2_name_2016,polygon)), alpha = 0.4) +
geom_polygon(data = hexagons, aes(x=long, lat, group = interaction(sa2_name_2016))) + scale_fill_viridis_d()
hexagon_points <- hexagons %>%
select(sa4_name_2016, sa2_name_2016,
sa2_name_2016, long, lat, poly_type) %>%
left_join(polygons %>% distinct(sa2_name_2016, polygon), by = "sa2_name_2016")
polygon_points <- polygons %>%
select(sa4_name_2016, sa2_name_2016, long, lat, polygon, poly_type)
animate_tas <- bind_rows(hexagon_points, polygon_points) %>%
left_join(homeless, by = "sa2_name_2016")
animate_tas %>%
ggplot(aes(x=long, y=lat, group = interaction(polygon, sa2_name_2016))) +
geom_polygon(aes(fill = sa4_name_2016)) +
geom_polygon(data = polygon_points %>% select(-poly_type), fill = "grey40", alpha = 0.05) +
coord_equal() +
theme_void() +
guides(fill = guide_legend(title = NULL)) +
theme(legend.position = "bottom") +
facet_wrap(~poly_type) +
scale_fill_viridis_d()
Creating an animation requires additional packages.
Consider installing: - the transformr package to tween
(go between) polygons - the gifski package for gif output -
the ava package for video output
library(gganimate)
library(transformr)
animation <- animate_tas %>%
ggplot(aes(x=long, y=lat, group = interaction(polygon, sa2_name_2016))) +
geom_polygon(aes(fill = sa4_name_2016)) +
geom_polygon(data = polygon_points %>% select(-poly_type), fill = "grey40", alpha = 0.05) +
coord_equal() +
theme_void() +
guides(fill = guide_legend(title = NULL)) +
theme(legend.position = "bottom") +
transition_states(states = poly_type) +
scale_fill_viridis_d()
animated <- animate(animation, fps = 10, duration = 15,
start_pause = 5, end_pause = 5, rewind = FALSE)
anim_save(filename = "tasmania_animation.gif", animated)