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.
Download the Fude Polygon data from the following MAFF release site (available only in Japanese):
You can install the released version of fude from CRAN with:
Or the development version from GitHub with:
You can allow R to read the downloaded ZIP file directly without unzipping 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.
You can convert the local government codes into Japanese municipality names for more convenient 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"It can also be renamed to romaji instead of Japanese.
d3 <- d |> rename_fude(suffix = TRUE, romaji = "title")
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/ (available only in Japanese).
You can effortlessly create a map that integrates 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$fude_split, aes(fill = RCOM_NAME)) +
guides(fill = guide_legend(reverse = TRUE, title = "興居島の集落別耕地")) +
theme_void() +
theme(text = element_text(family = "Hiragino Sans"))
出典：農林水産省「筆ポリゴンデータ（2022年度公開）」および「農業集落境界データ（2020年度）」を加工して作成。
Polygon data close to community borders may be divided. To avoid this, utilize db$fude.
library(ggforce)
ggplot() +
geom_sf(data = db$community, fill = NA) +
geom_sf(data = db$fude, aes(fill = RCOM_ROMAJI)) +
geom_mark_hull(data = db$fude,
aes(x = point_lng, y = point_lat,
fill = RCOM_ROMAJI,
label = RCOM_ROMAJI),
colour = NA,
expand = unit(1, "mm"),
radius = unit(1, "mm"),
label.fontsize = 9,
label.family = "Helvetica",
label.fill = NA,
label.colour = "black",
label.buffer = unit(1, "mm"),
con.colour = "gray70") +
theme_no_axes() +
theme(legend.position = "none")
Source: Created by processing the Ministry of Agriculture, Forestry and Fisheries, Fude Polygon Data (released in FY2022) and Agricultural Community Boundary Data (FY2020).
Polygons situated on community boundaries are not divided but are allocated to one of the communities. Should there be a need to adjust this automatic assignment, custom coding will be necessary. The rows that require consideration can be extracted with the following command.
library(dplyr)
library(sf)
# head(sf::st_drop_geometry(db$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")]))
db$fude |>
filter(polygon_uuid %in% (db$fude_split |> filter(duplicated(polygon_uuid)) |> pull(polygon_uuid))) |>
select(polygon_uuid, PREF_NAME, CITY_NAME, KCITY_NAME, RCOM_NAME, RCOM_KANA, RCOM_ROMAJI) |>
sf::st_drop_geometry() |>
head()
#> polygon_uuid PREF_NAME CITY_NAME KCITY_NAME RCOM_NAME
#> 1 8085bc47-9af5-440f-89e9-f188d3b95746 愛媛県 松山市 興居島村 泊
#> 2 26920da0-b63e-4994-a9eb-175e2982fe21 愛媛県 松山市 興居島村 門田
#> 3 ac2e7293-6c2f-4feb-a95f-4729dc8d0aec 愛媛県 松山市 興居島村 由良
#> 4 ea130038-7035-4cf3-b71c-091783090d74 愛媛県 松山市 興居島村 船越
#> 5 4aba8229-1b14-4eab-8a91-e10d9e841180 愛媛県 松山市 興居島村 船越
#> 6 156a3459-25cb-494c-824f-9ba6b0fb6f23 愛媛県 松山市 興居島村 由良
#> RCOM_KANA RCOM_ROMAJI
#> 1 とまり Tomari
#> 2 かどた Kadota
#> 3 ゆら Yura
#> 4 ふなこし Funakoshi
#> 5 ふなこし Funakoshi
#> 6 ゆら YuraThe gghighlight package enables practical and effective visualization.
library(forcats)
library(gghighlight)
db$community <- db$community %>%
mutate(across(c(RCOM_NAME, RCOM_KANA, RCOM_ROMAJI), forcats::fct_rev))
db$fude <- db$fude %>%
mutate(across(c(RCOM_NAME, RCOM_KANA, RCOM_ROMAJI), forcats::fct_rev))
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"))
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。
ggplot(data = db$fude, aes(x = as.numeric(a), fill = land_type_jp)) +
geom_histogram(position = "identity", alpha = .5) +
labs(x = "面積（a）",
y = "頻度") +
facet_wrap(vars(RCOM_NAME)) +
labs(fill = "耕地の種類") +
theme_minimal() +
theme(text = element_text(family = "Hiragino Sans"))
There are 8 types of objects obtained by combine_fude(), as follows:
If you want to be particular about the details of the map, for example, execute the following code.
db <- combine_fude(d, b, city = "松山市", old_village = "興居島", community = "^(?!釣島).*")
library(ggrepel)
library(cowplot)
minimap <- ggplot() +
geom_sf(data = db$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)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", old_village = "三瓶|二木生|三島|双岩")
db <- bind_fude(db1, db2, db3)
library(mapview)
mapview::mapview(db$fude, zcol = "RCOM_NAME", layer.name = "農業集落名")The possible values for community in combine_fude() can be listed as follows.
library(data.tree)
b[[1]] |>
filter(grepl("松山", KCITY_NAME)) |>
mutate(pathString = paste(PREF_NAME, CITY_NAME, KCITY_NAME, RCOM_NAME, sep = "/")) |>
data.tree::as.Node() |>
print(limit = 10)
#> levelName
#> 1 愛媛県
#> 2 °--松山市
#> 3 °--松山市
#> 4 ¦--土居田
#> 5 ¦--針田
#> 6 ¦--小栗第１
#> 7 ¦--小栗第２
#> 8 ¦--小栗第３
#> 9 ¦--藤原第１
#> 10 °--... 102 nodes w/ 0 subggplot(data = b[[1]] |> filter(grepl("松山", KCITY_NAME))) +
geom_sf(fill = NA) +
geom_sf_text(aes(label = RCOM_NAME), size = 2, family = "Hiragino Sans") +
theme_void()
You can also visualize the relationship between the residences of farmers and their farmland.
db <- combine_fude(d, b, city = "松山", community = "和気|安城寺|久万ノ台")
set.seed(111)
probabilities <- c("A" = 0.97, "B" = 0.01, "C" = 0.005, "D" = 0.005, "E" = 0.005, "F" = 0.005)
db$fude$farmer = factor(sample(names(probabilities),
nrow(db$fude),
replace = TRUE,
prob = probabilities))
farm <- db$fude |>
group_by(farmer) |>
summarise(geometry = sf::st_union(geometry) |> sf::st_centroid()) |>
sf::st_set_crs(4326)
farm_radius <- farm |>
sf::st_transform(crs = sp::CRS("+init=epsg:32632")) |>
sf::st_buffer(dist = units::as_units(1, "km")) |>
sf::st_transform(crs = 4326)
library(osmdata)
bbox <- sf::st_bbox(db$fude)
streets <- bbox |>
osmdata::opq() |>
osmdata::add_osm_feature(key = "highway",
value = c("motorway", "primary", "secondary", "tertiary",
"residential", "living_street",
"unclassified", "service", "footway")) |>
osmdata::osmdata_sf()
river <- bbox |>
osmdata::opq() |>
osmdata::add_osm_feature(key = "waterway", value = "river") |>
osmdata::osmdata_sf()
ggplot() +
geom_sf(data = db$community_union, fill = NA) +
geom_sf(data = streets$osm_lines, colour = "gray") +
geom_sf(data = river$osm_lines, colour = "skyblue") +
geom_sf(data = db$fude, aes(fill = farmer, colour = farmer), alpha = .5) +
geom_sf(data = farm, aes(colour = farmer)) +
geom_sf(data = farm_radius, aes(colour = farmer), linewidth = .3, fill = NA) +
theme_void()