Relationship Between the Residences of Farmers and Their Farmland

You can also visualize the relationship between the residences of farmers and their farmland.

library(dplyr)
library(sf)
library(osmdata)
library(ggplot2)
library(ggmapinset)
library(ggrepel)

db <- combine_fude(d, b, city = "松山", community = "和気|安城寺|長戸|久万ノ台")

set.seed(200)
probabilities <- c(0.97, 0.01, 0.005, 0.005, 0.005, 0.005)
names(probabilities) <- LETTERS[1:length(probabilities)]
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)

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()

inset1 <- configure_inset(
    centre = sf::st_geometry(farm)[farm$farmer == "F"],
    scale = 3,
    translation = c(-4, 1),
    radius = 1, units = "km"
  )
inset2 <- configure_inset(
    centre = sf::st_geometry(farm)[farm$farmer == "E"],
    scale = 3,
    translation = c(4, -3),
    radius = 1, units = "km"
  )

farm$x <- sf::st_coordinates(farm)[, 1]
farm$y <- sf::st_coordinates(farm)[, 2]

ggplot(data = db$fude) +
  geom_sf(data = streets$osm_lines, colour = "gray") +
  geom_sf(data = river$osm_lines, colour = "skyblue") +
  geom_sf(aes(fill = farmer, colour = farmer), alpha = .5) +
  geom_sf(data = farm, aes(colour = farmer)) +
  geom_text_repel(data = farm,
                  aes(x = x, y = y, label = farmer),
                  nudge_x = c(.02, .02, .02, -.01, .02, -.012),
                  nudge_y = c(.01, 0, -.005, -.005, .01, -.005),
                  min.segment.length = 0,
                  segment.color = "black",
                  size = 3,
                  family = "Helvetica") +
  geom_sf_inset(data = streets$osm_lines, colour = "gray", map_base = "none", inset = inset1) +
  geom_sf_inset(data = river$osm_lines, colour = "skyblue", map_base = "none", inset = inset1) +
  geom_sf_inset(aes(fill = farmer, colour = farmer), alpha = .5, map_base = "none", inset = inset1) +
  geom_inset_frame(inset = inset1) +
  geom_sf_inset(data = streets$osm_lines, colour = "gray", map_base = "none", inset = inset2) +
  geom_sf_inset(data = river$osm_lines, colour = "skyblue", map_base = "none", inset = inset2) +
  geom_sf_inset(aes(fill = farmer, colour = farmer), alpha = .5, map_base = "none", inset = inset2) +
  geom_inset_frame(inset = inset2) +
  theme_void() +
  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。