fude

R-CMD-check CRAN status

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.

Obtain data

Fude Polygon data can now be downloaded from two different MAFF websites (both available only in Japanese):

  1. GeoJSON format:
    https://open.fude.maff.go.jp

  2. FlatGeobuf format:
    https://www.maff.go.jp/j/tokei/census/shuraku_data/2020/mb/

Install the package

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

Read Fude Polygon data

There are two ways to load Fude Polygon data, depending on how the data was obtained:

  1. From a locally saved ZIP file:
    This method works for both GeoJSON (from Obtaining Data #1) and FlatGeobuf (from Obtaining Data #2) formats. You can load a ZIP file saved on your computer without unzipping it.
library(fude)
d <- read_fude("~/2022_38.zip")
  1. By specifying a prefecture name or code:
    This method is available only for FlatGeobuf data (from Obtaining Data #2). Provide the name of a prefecture (e.g., “愛媛”) or its corresponding prefecture code (e.g., “38”), and the required FlatGeobuf format ZIP file will be automatically downloaded and loaded.
d2 <- read_fude(pref = "愛媛")

Inspect the structure of Fude Polygon data

ls_fude(d)
#>           name issue_year local_government_cd     n pref_name  city_name
#> 1  2022_382019       2022              382019 72045    愛媛県     松山市
#> 2  2022_382027       2022              382027 43396    愛媛県     今治市
#> 3  2022_382035       2022              382035 61683    愛媛県   宇和島市
#> 4  2022_382043       2022              382043 37753    愛媛県   八幡浜市
#> 5  2022_382051       2022              382051 15734    愛媛県   新居浜市
#> 6  2022_382060       2022              382060 63244    愛媛県     西条市
#> 7  2022_382078       2022              382078 37570    愛媛県     大洲市
#> 8  2022_382108       2022              382108 33302    愛媛県     伊予市
#> 9  2022_382132       2022              382132 34781    愛媛県 四国中央市
#> 10 2022_382141       2022              382141 73676    愛媛県     西予市
#> 11 2022_382159       2022              382159 24235    愛媛県     東温市
#> 12 2022_383562       2022              383562  2195    愛媛県     上島町
#> 13 2022_383864       2022              383864 22823    愛媛県 久万高原町
#> 14 2022_384011       2022              384011  8634    愛媛県     松前町
#> 15 2022_384020       2022              384020  7042    愛媛県     砥部町
#> 16 2022_384224       2022              384224 27131    愛媛県     内子町
#> 17 2022_384429       2022              384429 23429    愛媛県     伊方町
#> 18 2022_384844       2022              384844  9089    愛媛県     松野町
#> 19 2022_384887       2022              384887 16550    愛媛県     鬼北町
#> 20 2022_385069       2022              385069 22931    愛媛県     愛南町
#>        city_romaji
#> 1    Matsuyama-shi
#> 2      Imabari-shi
#> 3      Uwajima-shi
#> 4   Yawatahama-shi
#> 5      Niihama-shi
#> 6        Saijo-shi
#> 7          Ozu-shi
#> 8          Iyo-shi
#> 9  Shikokuchuo-shi
#> 10       Seiyo-shi
#> 11        Toon-shi
#> 12    Kamijima-cho
#> 13   Kumakogen-cho
#> 14    Matsumae-cho
#> 15        Tobe-cho
#> 16      Uchiko-cho
#> 17       Ikata-cho
#> 18     Matsuno-cho
#> 19      Kihoku-cho
#> 20       Ainan-cho

Rename the local government code

Note: This feature is available only for data obtained from GeoJSON (Obtaining Data #1).

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

dren <- rename_fude(d)
names(dren)
#>  [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.

dren <- d |> rename_fude(suffix = TRUE, romaji = "title")
names(dren)
#>  [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"

Get 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)

Combine Fude Polygons with agricultural community boundaries

You can easily combine Fude Polygons with agricultural community boundaries to create enriched spatial analyses or maps.

GeoJSON data characteristics (obtain data #1)

db <- combine_fude(d, b, city = "松山市", rcom = "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
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.

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_romaji) |>
  head()
#>                           polygon_uuid kcity_name rcom_name rcom_romaji
#> 1 8085bc47-9af5-440f-89e9-f188d3b95746   興居島村        泊      Tomari
#> 2 26920da0-b63e-4994-a9eb-175e2982fe21   興居島村      門田      Kadota
#> 3 ac2e7293-6c2f-4feb-a95f-4729dc8d0aec   興居島村      由良        Yura
#> 4 ea130038-7035-4cf3-b71c-091783090d74   興居島村      船越   Funakoshi
#> 5 4aba8229-1b14-4eab-8a91-e10d9e841180   興居島村      船越   Funakoshi
#> 6 156a3459-25cb-494c-824f-9ba6b0fb6f23   興居島村      由良        Yura

FlatGeobuf data characteristics (obtain data #2)

The FlatGeobuf format offers a more efficient alternative to GeoJSON. A notable feature of this format is that each record already includes an accurately assigned agricultural community code.

db2 <- combine_fude(d2, b, city = "松山市", rcom = "由良|北浦|鷲ケ巣|門田|馬磯|泊|御手洗|船越")

ggplot() +
  geom_sf(data = db2$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（2025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 enables extraction based on city names, former city names, and agricultural community names.

Note: This feature is available only for data obtained from FlatGeobuf (Obtaining Data #2).

extract_fude(d2, city = "松山市", kcity = "興居島")
#> Simple feature collection with 1691 features and 6 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 132.6373 ymin: 33.87055 xmax: 132.6991 ymax: 33.92544
#> Geodetic CRS:  WGS 84
#> First 10 features:
#>                            polygon_uuid land_type issue_year point_lng
#> 1  5a72b4ef-b5f4-465e-9948-e93142819676       200       2025  132.6446
#> 2  c69d86d5-1fb2-4528-a87b-155d88260964       200       2025  132.6447
#> 3  627134ea-919c-4769-bd94-be16cb549c5b       200       2025  132.6445
#> 4  f2631019-d16e-42f9-8501-75f26515ca9a       200       2025  132.6441
#> 5  8fedb70d-4bb9-4447-879b-a0eeabc22915       200       2025  132.6437
#> 6  cd235cdf-da51-4ead-ad50-efc6ea1c84cf       200       2025  132.6434
#> 7  5853b7a1-62c3-4973-9e79-cabd3da6cdc7       200       2025  132.6436
#> 8  5e090780-6d16-4b9e-aca9-c56229851bfc       200       2025  132.6420
#> 9  90de4abf-e972-4031-987f-f3391b04b03c       200       2025  132.6421
#> 10 e5ade914-c803-42d1-9fa8-0921b98d69b8       200       2025  132.6423
#>    point_lat        key                       geometry
#> 1   33.88813 3820102004 MULTIPOLYGON (((132.6446 33...
#> 2   33.88768 3820102004 MULTIPOLYGON (((132.6444 33...
#> 3   33.88746 3820102004 MULTIPOLYGON (((132.6448 33...
#> 4   33.88755 3820102004 MULTIPOLYGON (((132.6442 33...
#> 5   33.88740 3820102004 MULTIPOLYGON (((132.6437 33...
#> 6   33.88729 3820102004 MULTIPOLYGON (((132.6434 33...
#> 7   33.88770 3820102004 MULTIPOLYGON (((132.6435 33...
#> 8   33.88782 3820102004 MULTIPOLYGON (((132.6418 33...
#> 9   33.88792 3820102004 MULTIPOLYGON (((132.6422 33...
#> 10  33.88765 3820102004 MULTIPOLYGON (((132.6422 33...

Explore Fude Polygon data

You can explore Fude Polygon data interactively.

library(shiny)

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

Read data from the MAFF database

You can read data from the MAFF database (地域の農業を見て・知って・活かすDB).

b1 <- get_boundary(d2, path = "~", boundary_type = 1, quiet = TRUE)
b2 <- get_boundary(d2, path = "~", boundary_type = 2, quiet = TRUE)
b3 <- get_boundary(d2, path = "~", boundary_type = 3, quiet = TRUE)

m3 <- read_ikasudb(b3, "~/IA0001_2023_2020_38.xlsx")

m1 <- b1 |> 
  read_ikasudb("~/SA1009_2020_2020_38.xlsx") |>
  read_ikasudb("~/GC0001_2019_2020_38.xlsx")

Use the 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")