Definition

The term hexbin map refers to two different concepts:

The first definition is based on an unusual geospatial object where all regions of the map are represented as hexagons. Here is an example representing the US states, each represented as an hexagon. Note that here the data input provides one value per state and a specific shape file with hexagone boundaries.

# library
library(tidyverse)
library(sf)
library(RColorBrewer)

# Download the Hexagon boundaries at geojson format here: https://team.carto.com/u/andrew/tables/andrew.us_states_hexgrid/public/map.

# Load this file. (Note: I stored in a folder called DATA)
my_sf <- read_sf("us_states_hexgrid.geojson.json")

# Bit of reformatting
my_sf <- my_sf %>%
  mutate(google_name = gsub(" \\(United States\\)", "", google_name))

data <- read.table("https://raw.githubusercontent.com/holtzy/R-graph-gallery/master/DATA/State_mariage_rate.csv",
  header = T, sep = ",", na.strings = "---"
)

# Show it
my_sf_wed <- my_sf %>%
  left_join(data, by = c("google_name" = "state"))

# Prepare binning
my_sf_wed$bin <- cut(my_sf_wed$y_2015,
  breaks = c(seq(5, 10), Inf),
  labels = c("5-6", "6-7", "7-8", "8-9", "9-10", "10+"),
  include.lowest = TRUE
)

# Prepare a color scale coming from the viridis color palette
library(viridis)
my_palette <- rev(magma(8))[c(-1, -8)]

# plot
ggplot(my_sf_wed) +
  geom_sf(aes(fill = bin), linewidth = 0, alpha = 0.9) +
  geom_sf_text(aes(label = iso3166_2), color = "white", size = 3, alpha = 0.6) +
  theme_void() +
  scale_fill_manual(
    values = my_palette,
    name = "Wedding per 1000 people in 2015",
    guide = guide_legend(
      keyheight = unit(3, units = "mm"),
      keywidth = unit(12, units = "mm"),
      label.position = "bottom", title.position = "top", nrow = 1
    )
  ) +
  ggtitle("A map of marriage rates, state by state") +
  theme(
    legend.position = c(0.5, 0.9),
    text = element_text(color = "#22211d"),
    plot.background = element_rect(fill = "#f5f5f2", color = NA),
    panel.background = element_rect(fill = "#f5f5f2", color = NA),
    legend.background = element_rect(fill = "#f5f5f2", color = NA),
    plot.title = element_text(
      size = 22, hjust = 0.5, color = "#4e4d47",
      margin = margin(b = -0.1, t = 0.4, l = 2, unit = "cm")
    )
  )

The second definition refers to the utilisation of 2D density techniques. Think of a scatterplot where the X axis is the longitude and the Y axis is the latitude. The graphic area is divided in a multitude of hexagones and the number of data point in each is counted and represented using a color gradient. Note that here no map boundaries is needed. It requires only a list of latitude and longitude.

# Libraries
library(tidyverse)
library(viridis)
library(hrbrthemes)
library(kableExtra)
options(knitr.table.format = "html")
library(mapdata)

# Load dataset from github
data <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/17_ListGPSCoordinates.csv", sep=",", header=T)

# plot
data %>%
  filter(homecontinent=='Europe') %>%
  ggplot( aes(x=homelon, y=homelat)) +
    geom_hex(bins=59) +
    ggplot2::annotate("text", x = -27, y = 72, label="Where people tweet about #Surf", colour = "black", size=5, alpha=1, hjust=0) +
    ggplot2::annotate("segment", x = -27, xend = 10, y = 70, yend = 70, colour = "black", size=0.2, alpha=1) +
    theme_void() +
    xlim(-30, 70) +
    ylim(24, 72) +
    scale_fill_viridis(
      trans = "log",
      breaks = c(1,7,54,403,3000),
      name="Tweet # recorded in 8 months",
      guide = guide_legend( keyheight = unit(2.5, units = "mm"), keywidth=unit(10, units = "mm"), label.position = "bottom", title.position = 'top', nrow=1)
    )  +
    ggtitle( "" ) +
    theme(
      legend.position = c(0.8, 0.09),
      legend.title=element_text(color="black", size=8),
      text = element_text(color = "#22211d"),
      plot.background = element_rect(fill = "#f5f5f2", color = NA),
      panel.background = element_rect(fill = "#f5f5f2", color = NA),
      legend.background = element_rect(fill = "#f5f5f2", color = NA),
      plot.title = element_text(size= 13, hjust=0.1, color = "#4e4d47", margin = margin(b = -0.1, t = 0.4, l = 2, unit = "cm")),
    )

Note on the first map: You can learn more about this story here. Data comes from here. Code has been inspired from this post and that one.

Note on the second map: This map shows the geographic position of about 200k tweets containing the hashtags #surf, #windsurf or #kitesurf. Read more about it here.

What for

Hexbin or grid map has an advantage over usual choropleth maps. In choropleths, a large polygon’s data looks more emphasized just because of its size, what introduces a bias. Here with hexbin, each region is represented equally dismissing the bias.

There’s a drawback to this format though. Map readers generally recognize a geographic area by it’s shape and orientation to other areas. For instance, the geography of the US is well known and people easily identify different regions. In hexbin maps, these landmarks do not exist anymore what can confuse the audience. One solution for this is to choose a basemap that uses labels on top of your data layer.

Variation

Hexbin map uses hexagons to split the area in several parts and attribute a color to it. Note that it is possible to use square instead of hexagones, resulting in a 2D histogram map.

Common mistakes

Normalize your variable: you cannot compare raw numbers between regions of distinct size or population.
Take a huge care when choosing the continuous color palette.
Don’t forget the legend.

Build your own

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A work by Yan Holtz for data-to-viz.com