Structure of combined Fude Polygon data with agricultural community boundary data

Structure of combined Fude Polygon data with agricultural community boundary data

There are 7 types of objects obtained by combine_fude(), as follows:

names(db)

## [1] "fude"       "fude_split" "rcom"       "rcom_union" "kcity"     
## [6] "city"       "pref"

Possible values for rcom in combine_fude() and extract_boundary()

library(dplyr)
library(data.tree)

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()

tree$Do(\(node) {node$n <- if (node$isLeaf) NA_integer_ else node$count})
data.tree::SetFormat(tree, "n", \(x) if (is.na(x)) "-" else x)
print(tree, "n", limit = 30)

##                             levelName   n
## 1  愛媛県                               1
## 2   °--松山市                          1
## 3       °--松山市                    108
## 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          ¦--藤原第２                -
## 11          ¦--竹原東                  -
## 12          ¦--竹原西                  -
## 13          ¦--生石南                  -
## 14          ¦--生石北                  -
## 15          ¦--八代                    -
## 16          ¦--南味酒                  -
## 17          ¦--南江戸                  -
## 18          ¦--朝美１                  -
## 19          ¦--朝美２                  -
## 20          ¦--朝美３                  -
## 21          ¦--宮西                    -
## 22          ¦--六軒家                  -
## 23          ¦--衣山                    -
## 24          ¦--萱町９                  -
## 25          ¦--山越                    -
## 26          ¦--姫原                    -
## 27          ¦--御幸寺                  -
## 28          ¦--本町９                  -
## 29          ¦--本町８                  -
## 30          °--... 82 nodes w/ 0 sub   -

library(ggplot2)

ggplot(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()

出典：農林水産省「農業集落境界データ（2020年度）」を加工して作成。

library(collapsibleTree)

b[[1]] |>
  filter(grepl("松山", city_name)) |>
  distinct(pref_name, city_name, kcity_name, rcom_name) |>
  (\(x) collapsibleTree(
    x,
    hierarchy = names(x),
    root = "・"
  ))()

Possible values for kcity in combine_fude() and extract_boundary()

library(paletteer)

ggplot(b[[1]] |> filter(city_name == "松山市")) +
  geom_sf(aes(fill = kcity_name), alpha = .8) +
  theme_void() +
  theme(text = element_text(family = "Hiragino Sans")) +
  paletteer::scale_fill_paletteer_d("Polychrome::kelly")

出典：農林水産省「農業集落境界データ（2020年度）」を加工して作成。