Utilities for Fude Polygon • fude

The fude package provides utilities to facilitate the handling of the Fude Polygon data downloadable from the Ministry of Agriculture, Forestry and Fisheries (MAFF) website. The word “fude” is a Japanese counter suffix used to denote land parcels.

Obtaining Data

Download the Fude Polygon data from the following MAFF release site (available only in Japanese):

https://open.fude.maff.go.jp

Installation

You can install the released version of fude from CRAN with:

install.packages("fude")

Or the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("takeshinishimura/fude")

Usage

Reading Fude Polygon Data

You can allow R to read the downloaded ZIP file directly without unzipping it.

library(fude)
d <- read_fude("~/2022_38.zip")

Renaming the Local Government Code

Convert local government codes into Japanese municipality names for easier management.

d2 <- rename_fude(d)
names(d2)
#>  [1] "2022_松山市"     "2022_今治市"     "2022_宇和島市"   "2022_八幡浜市"  
#>  [5] "2022_新居浜市"   "2022_西条市"     "2022_大洲市"     "2022_伊予市"    
#>  [9] "2022_四国中央市" "2022_西予市"     "2022_東温市"     "2022_上島町"    
#> [13] "2022_久万高原町" "2022_松前町"     "2022_砥部町"     "2022_内子町"    
#> [17] "2022_伊方町"     "2022_松野町"     "2022_鬼北町"     "2022_愛南町"

You can also rename the columns to Romaji instead of Japanese.

d2 <- d |> rename_fude(suffix = TRUE, romaji = "title")
names(d2)
#>  [1] "2022_Matsuyama-shi"   "2022_Imabari-shi"     "2022_Uwajima-shi"    
#>  [4] "2022_Yawatahama-shi"  "2022_Niihama-shi"     "2022_Saijo-shi"      
#>  [7] "2022_Ozu-shi"         "2022_Iyo-shi"         "2022_Shikokuchuo-shi"
#> [10] "2022_Seiyo-shi"       "2022_Toon-shi"        "2022_Kamijima-cho"   
#> [13] "2022_Kumakogen-cho"   "2022_Matsumae-cho"    "2022_Tobe-cho"       
#> [16] "2022_Uchiko-cho"      "2022_Ikata-cho"       "2022_Matsuno-cho"    
#> [19] "2022_Kihoku-cho"      "2022_Ainan-cho"

Getting Agricultural Community Boundary Data

Download the agricultural community boundary data, which corresponds to the Fude Polygon data, from the MAFF website: https://www.maff.go.jp/j/tokei/census/shuraku_data/2020/ma/ (available only in Japanese).

b <- get_boundary(d)

You can easily create a map that combines Fude Polygons with agricultural community boundaries.

db <- combine_fude(d, b, city = "松山市", community = "由良|北浦|鷲ケ巣|門田|馬磯|泊|御手洗|船越")

library(ggplot2)

ggplot() +
  geom_sf(data = db$fude, aes(fill = RCOM_NAME), alpha = .8) +
  guides(fill = guide_legend(reverse = TRUE, title = "興居島の集落別耕地")) +
  theme_void() +
  theme(legend.position = "bottom") +
  theme(text = element_text(family = "Hiragino Sans"))

出典：農林水産省「筆ポリゴンデータ（2022年度公開）」および「農業集落境界データ（2020年度）」を加工して作成。

Data Assignment

db$fude: Automatically assigns polygons on the boundaries to a community.
db$fude_split: Provides cleaner boundaries, but polygon data near community borders may be divided.

library(patchwork)

fude <- ggplot() +
  geom_sf(data = db$fude, aes(fill = RCOM_NAME), alpha = .8) +
  theme_void() +
  theme(legend.position = "none") +
  coord_sf(xlim = c(132.658, 132.678), ylim = c(33.887, 33.902))

fude_split <- ggplot() +
  geom_sf(data = db$fude_split, aes(fill = RCOM_NAME), alpha = .8) +
  theme_void() +
  theme(legend.position = "none") +
  coord_sf(xlim = c(132.658, 132.678), ylim = c(33.887, 33.902))

fude + fude_split

If you need to adjust this automatic assignment, you will need to write custom code. The rows that require attention can be identified with the following command.

# head(sf::st_drop_geometry(db$fude[db$fude$polygon_uuid %in% db$fude_split$polygon_uuid[duplicated(db$fude_split$polygon_uuid)], c("polygon_uuid", "PREF_NAME", "CITY_NAME", "KCITY_NAME", "RCOM_NAME", "RCOM_KANA", "RCOM_ROMAJI")]))
library(dplyr)
library(sf)

db$fude |>
  filter(polygon_uuid %in% (db$fude_split |> filter(duplicated(polygon_uuid)) |> pull(polygon_uuid))) |>
  st_drop_geometry() |>
  select(polygon_uuid, KCITY_NAME, RCOM_NAME, RCOM_KANA, RCOM_ROMAJI) |>
  head()
#>                           polygon_uuid KCITY_NAME RCOM_NAME RCOM_KANA
#> 1 8085bc47-9af5-440f-89e9-f188d3b95746   興居島村        泊    とまり
#> 2 26920da0-b63e-4994-a9eb-175e2982fe21   興居島村      門田    かどた
#> 3 ac2e7293-6c2f-4feb-a95f-4729dc8d0aec   興居島村      由良      ゆら
#> 4 ea130038-7035-4cf3-b71c-091783090d74   興居島村      船越  ふなこし
#> 5 4aba8229-1b14-4eab-8a91-e10d9e841180   興居島村      船越  ふなこし
#> 6 156a3459-25cb-494c-824f-9ba6b0fb6f23   興居島村      由良      ゆら
#>   RCOM_ROMAJI
#> 1      Tomari
#> 2      Kadota
#> 3        Yura
#> 4   Funakoshi
#> 5   Funakoshi
#> 6        Yura

Review Fude Polygon Data

You can review Fude Polygon data in detail.

library(shiny)

s <- shiny_fude(db, community = TRUE)
# shiny::shinyApp(ui = s$ui, server = s$server)

This feature was heavily inspired by the following website: https://brendenmsmith.com/blog/shiny_map_filter/.

Using `mapview` package

If you want to use mapview(), do the following.

db1 <- combine_fude(d, b, city = "伊方町")
db2 <- combine_fude(d, b, city = "八幡浜市")
db3 <- combine_fude(d, b, city = "西予市", kcity = "三瓶|二木生|三島|双岩")

db <- bind_fude(db1, db2, db3)

library(mapview)

mapview::mapview(db$fude, zcol = "RCOM_NAME", layer.name = "農業集落名")

fude