This visualisation is based on Niwako Yamawaki’s academic article [1] and Masaki Kubota’s R code [2].

Collectivism is defined in the Oxford dictionary as “the practice or principle of giving a group priority over each individual in it.”
Yamawaki measured collectivism based on divorce to marriage rate, percentage of households with three generations living together, percentage of elderly people (aged 65+ years) living alone (reverse scored), percentage of nuclear family households (reverse scored), and percentage of people living alone (reverse scored). Then, a scale was created and each Japanese prefecture got a score. See details in the original article.

Here I got these scores of collectivism, combined with Kubora’s code, and made two tile maps of Japan.

# Packages
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
library(readr)
# Data with collectivism scores
jp_collectivism<- read_delim("jp_collectivism.csv", 
                             ";", escape_double = FALSE, trim_ws = TRUE)
## 
## -- Column specification --------------------------------------------------------
## cols(
##   Rank = col_double(),
##   Prefecture = col_character(),
##   Score = col_double()
## )

Data necessary to plot the tile maps

JP_plot <- tribble(
  ~Prefecture_jp,~Prefecture ,~Code, ~x, ~y,
  "北海道", "Hokkaido","HK", 16, 14,
  "青森","Aomori"　,"AO", 15, 11,
  "岩手","Iwate"　,"IT", 15, 10,
  "宮城","Miyagi"　,"MG", 15, 9,
  "秋田","Akita"　,"AK", 14, 10,
  "山形","Yamagata"　,"YG", 14, 9,
  "福島","Fukushima"　,"FS", 15, 8,
  "茨城","Ibaraki"　,"IB", 15, 6,
  "栃木","Tochigi"　,"TC", 15, 7,
  "群馬","Gunma"　,"GU", 14, 7,
  "埼玉","Saitama"　,"ST", 14, 6,
  "千葉","Chiba"　,"CB", 15, 5,
  "東京",　"Tokyo","TY", 14, 5,
  "神奈川","Kanagawa",　"KN", 14, 4,
  "新潟","Niigata",　"NI", 14, 8,
  "富山","Toyama",　"TM", 13, 7,
  "石川","Ishikawa",　"IS", 12, 7,
  "福井","Fukui",　"FI", 12, 6,
  "山梨","Yamanashi",　"YN", 13, 5,
  "長野","Nagano",　"NA", 13, 6,
  "岐阜","Gifu",　"GI", 12, 5,
  "静岡","Shizuoka",　"SZ", 13, 4,
  "愛知","Aichi",　"AI", 12, 4,
  "三重","Mie",　"ME", 11, 4,
  "滋賀","Shiga",　"SI", 11, 5,
  "京都","Kyoto",　"KY", 10, 5,
  "大阪","Osaka",　"OS", 9, 5,
  "兵庫","Hyogo",　"HG", 8, 5,
  "奈良","Nara",　"NR", 10, 4,
  "和歌山","Wakayama",　"WA", 9, 4,
  "鳥取","Tottori",　"TT", 7, 5,
  "島根","Shimane",　"SM", 6, 5,
  "岡山","Okayama",　"OY", 7, 4,
  "広島","Hiroshima",　"HS", 6, 4,
  "山口","Yamaguchi",　"YA", 5, 5,
  "徳島","Tokushima"　,"TK", 7, 1,
  "香川","Kagawa"　,"KA", 7, 2,
  "愛媛","Ehime"　,"EH", 6, 2,
  "高知","Kochi",　"KO", 6, 1,
  "福岡","Fukuoka"　,"FO", 4, 3,
  "佐賀","Saga"　,"SG", 3, 3,
  "長崎","Nagasaki",　"NS", 2, 3,
  "熊本","Kumamoto"　,"KU", 3, 2,
  "大分","Oita"　,"OI", 4, 2,
  "宮崎","Miyazaki"　,"MZ", 4, 1,
  "鹿児島","Kagoshima" ,"KG", 3, 1,
  "沖縄","Okinawa"　,"OK", 1, 1)

# Define Map sizes
JP_plot<- JP_plot %>% mutate(scale = c(3,rep(1,46)))# because Hokkaido is larger than the other prefectures

Combining the data

jp_collectivism<- jp_collectivism %>%  left_join(JP_plot,by= "Prefecture")

Creating the graphs

# Graph with latin alphabet labels
graph<- jp_collectivism %>% # data itself
  ggplot(aes(x = x, # x axis
             y = y, # y axis
             width = scale, # scale of width
             height = scale)) +  #scale of height
  geom_tile(aes(fill = Score), color = "black") + # variable used to calculate the colour filling of the tiles
 geom_text(aes(label = Code), size = 2.7, color = "red" ) +   # label and its size and colour
  coord_fixed(ratio = 1) +  # to keep the ratio between the representation of data and axis
  theme( # no background, axis, title, ticks or legend
    panel.background = element_blank(),
    panel.grid = element_blank(),
    axis.title = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    legend.position = "none")  +

  scale_fill_gradient2(low="black", # gradient colours
                     mid="#DDDDDD",
                     high="grey50",
                     midpoint = 50) # gradient midpoint

# Graph with Japanese labels
graph1<- jp_collectivism %>% # data itself
  ggplot(aes(x = x, # x axis
             y = y, # y axis
             width = scale, # scale of width
             height = scale)) +  #scale of height
  geom_tile(aes(fill = Score), color = "black") + # variable used to calculate the colour filling of the tiles
 geom_text(aes(label = Prefecture_jp), size = 2.7, color = "black" ) +   # label and its size and colour
  coord_fixed(ratio = 1) +  # to keep the ratio between the representation of data and axis
  theme( # no background, axis, title, ticks or legend
    panel.background = element_blank(),
    panel.grid = element_blank(),
    axis.title = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    legend.position = "none")  +

  scale_fill_gradient2(low="grey90", # gradient colours
                     mid="steelblue",
                     high="blue",
                     midpoint = 50
                     ) # gradient midpoint

Plotting the graphs

The first graph has the prefecture names written in Latin Alphabet, prefectures scoring high on collectivism are coloured light grey, and prefectures scoring low on collectivism are coloured black.
The second graph has the prefecture names written in Japanese kanji, prefectures scoring high on collectivism are coloured blue, and prefectures scoring low on collectivism are coloured light grey. There is a midpoint coloured steel blue.

print(graph)

print(graph1)

[1] Yamawaki, N. (2012). Within-culture variations of collectivism in Japan. Journal of Cross-Cultural Psychology, 43(8), 1191-1204.