The fude package provides utilities to facilitate handling of 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 release site of MAFF (available only in Japanese).
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
You can allow R to read the downloaded ZIP file directly without the need to unzip it.
For those who prefer using a mouse or trackpad to select files, a method particularly popular among R beginners, the following approach can be taken.
d <- read_fude(file.choose())You can convert the local government codes into Japanese municipality names for more convenient management.
d2 <- rename_fude(d)
#> 2022_382019 -> 2022_松山市
#> 2022_382027 -> 2022_今治市
#> 2022_382035 -> 2022_宇和島市
#> 2022_382043 -> 2022_八幡浜市
#> 2022_382051 -> 2022_新居浜市
#> 2022_382060 -> 2022_西条市
#> 2022_382078 -> 2022_大洲市
#> 2022_382108 -> 2022_伊予市
#> 2022_382132 -> 2022_四国中央市
#> 2022_382141 -> 2022_西予市
#> 2022_382159 -> 2022_東温市
#> 2022_383562 -> 2022_上島町
#> 2022_383864 -> 2022_久万高原町
#> 2022_384011 -> 2022_松前町
#> 2022_384020 -> 2022_砥部町
#> 2022_384224 -> 2022_内子町
#> 2022_384429 -> 2022_伊方町
#> 2022_384844 -> 2022_松野町
#> 2022_384887 -> 2022_鬼北町
#> 2022_385069 -> 2022_愛南町It can also be renamed to romaji instead of Japanese.
d3 <- d |> rename_fude(suffix = TRUE, romaji = "title", quiet = TRUE)
names(d3)
#> [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"You can download the agricultural community boundary data, which corresponds to the Fude Polygon data, from the MAFF website at https://www.maff.go.jp/j/tokei/census/shuraku_data/2020/ma/ (onlyavailable only in Japanese).
b <- get_boundary(d)You can effortlessly create a map that integrates Fude Polygons with agricultural community boundaries.
library(ggplot2)
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")
ggplot() +
geom_sf(data = db$fude_split, aes(fill = RCOM_NAME)) +
guides(fill = guide_legend(reverse = TRUE, title = "興居島の集落別耕地")) +
theme_void()
出典：農林水産省「筆ポリゴンデータ（2022年度公開）」および「農業集落境界データ（2020年度）」を加工して作成。
Polygon data close to community borders may be divided. To avoid this, utilize db$fude.
library(ggforce)
ggplot() +
geom_sf(data = db$community, fill = NA) +
geom_sf(data = db$fude, aes(fill = RCOM_NAME)) +
geom_mark_hull(data = db$fude,
aes(x = point_lng, y = point_lat,
fill = RCOM_NAME,
label = RCOM_NAME),
colour = NA,
expand = unit(1, "mm"),
radius = unit(1, "mm"),
label.fontsize = 9,
label.family = "Hiragino Sans",
label.fill = NA,
label.colour = "black",
label.buffer = unit(4, "mm"),
con.colour = "gray70") +
theme_no_axes() +
theme(legend.position = "none")
出典：農林水産省「筆ポリゴンデータ（2022年度公開）」および「農業集落境界データ（2020年度）」を加工して作成。
Polygons situated on community boundaries are not divided but are allocated to one of the communities. Should there be a need to adjust this automatic assignment, custom coding will be necessary. The rows that require consideration can be extracted with the following command.
db$fude[duplicated(db$fude_split$polygon_uuid), ]
#> Simple feature collection with 80 features and 26 fields
#> Active geometry column: geometry (with 4 geometries empty)
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: 132.6574 ymin: 33.87414 xmax: 132.6946 ymax: 33.92217
#> Geodetic CRS: WGS 84
#> First 10 features:
#> polygon_uuid land_type issue_year edit_year
#> 179 95d68fa9-02c8-41c9-a273-ee30465605ff 200 2022 2020
#> 180 17a1301c-1edf-4217-a349-ff152d13cc73 200 2022 2020
#> 187 36c62d0d-444c-412b-b171-b9b6a6a274bb 200 2022 2020
#> 191 2c5e218b-bf8b-444d-85e4-d30de3e951ba 200 2022 2020
#> 204 696bd14d-a0cc-4fb5-94f6-ce73328502ed 200 2022 2020
#> 229 a422c19e-da70-47ff-848f-2d5e30f3fed9 200 2022 2020
#> 237 befbd449-9f38-40c2-8db2-32236ff368b4 200 2022 2020
#> 303 1ca420b2-6563-40ca-aae3-512e59b034de 200 2022 2020
#> 418 55bc275e-9361-4a34-8fdc-e07fa7dcac87 200 2022 2020
#> 527 81d95fa7-a821-407f-aa38-0a5b8a47b8d2 200 2022 2020
#> history
#> 179 [{"筆ポリゴンID":"58eb5fcb-ea9c-49f8-8d38-ae48e573f096","更新年度":2021,"前年同一":true},{"筆ポリゴンID":"58eb5fcb-ea9c-49f8-8d38-ae48e573f096","発生年度":2020}]
#> 180 [{"筆ポリゴンID":"8d856d0a-b4cc-471b-bb0d-7a386aee263a","更新年度":2021,"前年同一":true},{"筆ポリゴンID":"8d856d0a-b4cc-471b-bb0d-7a386aee263a","発生年度":2020}]
#> 187 [{"筆ポリゴンID":"17c4d96b-b7ae-4058-9c97-750961e04191","更新年度":2021,"前年同一":true},{"筆ポリゴンID":"17c4d96b-b7ae-4058-9c97-750961e04191","発生年度":2020}]
#> 191 [{"筆ポリゴンID":"9fbf0d62-5287-4036-8e5e-39448edd7d39","更新年度":2021,"前年同一":true},{"筆ポリゴンID":"9fbf0d62-5287-4036-8e5e-39448edd7d39","発生年度":2020}]
#> 204 [{"筆ポリゴンID":"2c35b35a-1a17-4631-8b65-9c3d5bd567d0","更新年度":2021,"前年同一":true},{"筆ポリゴンID":"2c35b35a-1a17-4631-8b65-9c3d5bd567d0","発生年度":2020}]
#> 229 [{"筆ポリゴンID":"3910f090-033e-4e70-b940-572916e5ada3","更新年度":2021,"前年同一":true},{"筆ポリゴンID":"3910f090-033e-4e70-b940-572916e5ada3","発生年度":2020}]
#> 237 [{"筆ポリゴンID":"079911a5-f6cd-4ea3-ac83-40f85c216a73","更新年度":2021,"前年同一":true},{"筆ポリゴンID":"079911a5-f6cd-4ea3-ac83-40f85c216a73","発生年度":2020}]
#> 303 [{"筆ポリゴンID":"ce292372-6417-432c-8350-81cd0efcae00","更新年度":2021,"前年同一":true},{"筆ポリゴンID":"ce292372-6417-432c-8350-81cd0efcae00","発生年度":2020}]
#> 418 [{"筆ポリゴンID":"91c37533-c2e4-4688-8f20-8b456fea7fef","更新年度":2021,"前年同一":true},{"筆ポリゴンID":"91c37533-c2e4-4688-8f20-8b456fea7fef","発生年度":2020}]
#> 527 [{"筆ポリゴンID":"1359d49c-7df2-4941-bbd0-789a5e7daacc","更新年度":2021,"前年同一":true},{"筆ポリゴンID":"1359d49c-7df2-4941-bbd0-789a5e7daacc","発生年度":2020}]
#> last_polygon_uuid prev_last_polygon_uuid
#> 179 58eb5fcb-ea9c-49f8-8d38-ae48e573f096 <NA>
#> 180 8d856d0a-b4cc-471b-bb0d-7a386aee263a <NA>
#> 187 17c4d96b-b7ae-4058-9c97-750961e04191 <NA>
#> 191 9fbf0d62-5287-4036-8e5e-39448edd7d39 <NA>
#> 204 2c35b35a-1a17-4631-8b65-9c3d5bd567d0 <NA>
#> 229 3910f090-033e-4e70-b940-572916e5ada3 <NA>
#> 237 079911a5-f6cd-4ea3-ac83-40f85c216a73 <NA>
#> 303 ce292372-6417-432c-8350-81cd0efcae00 <NA>
#> 418 91c37533-c2e4-4688-8f20-8b456fea7fef <NA>
#> 527 1359d49c-7df2-4941-bbd0-789a5e7daacc <NA>
#> local_government_cd point_lng point_lat land_type_jp KEY PREF CITY
#> 179 382019 132.6890 33.91362 畑 3820102008 38 201
#> 180 382019 132.6729 33.87807 畑 3820102002 38 201
#> 187 382019 132.6761 33.88398 畑 3820102002 38 201
#> 191 382019 132.6859 33.91254 畑 3820102008 38 201
#> 204 382019 132.6831 33.90642 畑 3820102008 38 201
#> 229 382019 132.6667 33.88325 畑 3820102002 38 201
#> 237 382019 132.6686 33.89156 畑 3820102003 38 201
#> 303 382019 132.6886 33.90552 畑 3820102008 38 201
#> 418 382019 132.6585 33.90284 畑 3820102005 38 201
#> 527 382019 132.6875 33.90904 畑 3820102008 38 201
#> KCITY RCOM PREF_NAME CITY_NAME KCITY_NAME RCOM_NAME RCOM_KANA HININTEI
#> 179 02 008 愛媛県 松山市 興居島村 門田 かどた 0
#> 180 02 002 愛媛県 松山市 興居島村 泊 とまり 0
#> 187 02 002 愛媛県 松山市 興居島村 泊 とまり 0
#> 191 02 008 愛媛県 松山市 興居島村 門田 かどた 0
#> 204 02 008 愛媛県 松山市 興居島村 門田 かどた 0
#> 229 02 002 愛媛県 松山市 興居島村 泊 とまり 0
#> 237 02 003 愛媛県 松山市 興居島村 船越 ふなこし 0
#> 303 02 008 愛媛県 松山市 興居島村 門田 かどた 0
#> 418 02 005 愛媛県 松山市 興居島村 鷲ケ巣 わしがす 0
#> 527 02 008 愛媛県 松山市 興居島村 門田 かどた 0
#> edit_year.1 RCOM_romaji x y geometry
#> 179 2020 Kadota 132.6865 33.91249 POLYGON ((132.6889 33.91384...
#> 180 2020 Tomari 132.6745 33.88193 POLYGON ((132.6726 33.87832...
#> 187 2020 Tomari 132.6745 33.88193 POLYGON ((132.676 33.88372,...
#> 191 2020 Kadota 132.6865 33.91249 POLYGON ((132.6856 33.91279...
#> 204 2020 Kadota 132.6865 33.91249 POLYGON ((132.6829 33.90642...
#> 229 2020 Tomari 132.6745 33.88193 POLYGON ((132.6667 33.88354...
#> 237 2020 Funakoshi 132.6706 33.89536 POLYGON ((132.6687 33.89186...
#> 303 2020 Kadota 132.6865 33.91249 POLYGON ((132.6887 33.90569...
#> 418 2020 Washigasu 132.6637 33.90494 POLYGON ((132.6577 33.90273...
#> 527 2020 Kadota 132.6865 33.91249 POLYGON ((132.6879 33.90909...
#> centroid
#> 179 POINT (132.6865 33.91249)
#> 180 POINT (132.6745 33.88193)
#> 187 POINT (132.6745 33.88193)
#> 191 POINT (132.6865 33.91249)
#> 204 POINT (132.6865 33.91249)
#> 229 POINT (132.6745 33.88193)
#> 237 POINT (132.6706 33.89536)
#> 303 POINT (132.6865 33.91249)
#> 418 POINT (132.6637 33.90494)
#> 527 POINT (132.6865 33.91249)The gghighlight package allows for a wider range of expression.
library(gghighlight)
ggplot() +
geom_sf(data = db$community, aes(fill = RCOM_NAME), alpha = 0) +
geom_sf(data = db$fude, aes(fill = RCOM_NAME), linewidth = 0) +
gghighlight() +
facet_wrap(vars(RCOM_NAME)) +
theme_void() +
theme(legend.position = "none",
text = element_text(family = "Hiragino Sans"))
Source: Created by processing the Ministry of Agriculture, Forestry and Fisheries, Fude Polygon Data (released in FY2022) and Agricultural Community Boundary Data (FY2020).
There are 8 types of objects obtained by combine_fude() as follows.
names(db)
#> [1] "fude" "fude_split" "community" "community_union"
#> [5] "ov" "lg" "pref" "source"If you want to be particular about the details of the map, for example, execute the following code.
library(ggrepel)
library(cowplot)
db <- combine_fude(d, b, city = "松山市", old_village = "興居島", community = "^(?!釣島).*")
minimap <- ggplot() +
geom_sf(data = db$lg, aes(fill = fill)) +
geom_sf_text(data = db$lg, aes(label = city_kanji), family = "Hiragino Sans") +
gghighlight(fill == 1) +
geom_sf(data = db$community_union, fill = "black", linewidth = 0) +
theme_void() +
theme(panel.background = element_rect(fill = "aliceblue")) +
scale_fill_manual(values = c("white", "gray"))
mainmap <- ggplot() +
geom_sf(data = db$community, fill = "white") +
geom_sf(data = db$fude, aes(fill = RCOM_NAME)) +
geom_point(data = db$community, aes(x = x, y = y), colour = "gray") +
geom_text_repel(data = db$community,
aes(x = x, y = y, label = RCOM_NAME),
nudge_x = c(-.01, .01, -.01, -.012, .005, -.01, .01, .01),
nudge_y = c(.005, .005, 0, .01, -.005, .01, 0, -.005),
min.segment.length = .01,
segment.color = "gray",
size = 3,
family = "Hiragino Sans") +
theme_void() +
theme(legend.position = "none")
ggdraw(mainmap) +
draw_plot(
{minimap +
geom_rect(aes(xmin = 132.47, xmax = 133.0,
ymin = 33.72, ymax = 34.05),
fill = NA,
colour = "black",
size = .5) +
coord_sf(xlim = c(132.47, 133.0),
ylim = c(33.72, 34.05),
expand = FALSE) +
theme(legend.position = "none")
},
x = .7,
y = 0,
width = .3,
height = .3)This package may be beneficial, especially for R beginners, when simply wanting to draw agricultural community boundaries.
library(dplyr)
library(ggrepel)
db <- combine_fude(d, b, city = "西予市", old_village = "遊子川")
ggplot() +
geom_sf(data = db$pref, fill = NA) +
geom_sf(data = db$lg, fill = "gray") +
gghighlight(fill == 1,
unhighlighted_params = list(
alpha = .05
)) +
geom_sf(data = db$ov |> filter(fill == 1), colour = NA, fill = "black") +
geom_sf_text(data = db$lg |> filter(fill == 1),
aes(label = city_kanji),
size = 3,
nudge_x = -.025, nudge_y = -.025,
family = "Hiragino Sans") +
geom_point(data = db$community_union, aes(x = x, y = y), colour = "black") +
geom_text_repel(data = db$community_union,
aes(x = x, y = y),
label = "遊子川地区",
nudge_x = .3, nudge_y = -.025,
segment.color = "black",
size = 3,
family = "Hiragino Sans") +
theme_void()
出典：農林水産省「筆ポリゴンデータ（2022年度公開）」および「農業集落境界データ（2020年度）」を加工して作成。
If you want to use mapview(), do the following.
library(mapview)
db1 <- combine_fude(d, b, city = "伊方町")
db2 <- combine_fude(d, b, city = "八幡浜市")
db3 <- combine_fude(d, b, city = "西予市", old_village = "三瓶|二木生|三島|双岩")
db <- bind_fude(db1, db2, db3)
mapview::mapview(db$fude, zcol = "RCOM_NAME", layer.name = "農業集落名")