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農林水産省が公開する農地の区画情報（筆ポリゴン）を扱うRパッケージ
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takeshinishimura/fude
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The fude package provides utilities to facilitate the handling of theFude Polygon data downloadable from the Ministry of Agriculture,Forestry and Fisheries (MAFF) website. The word “fude” is a Japanesecounter suffix used to denote land parcels.
Fude Polygon data can now be downloaded from two different MAFF websites(both available only in Japanese):
GeoJSON format:
https://open.fude.maff.go.jpFlatGeobuf format:
https://www.maff.go.jp/j/tokei/census/shuraku_data/2020/mb/
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")
There are two ways to load Fude Polygon data, depending on how the datawas obtained:
- From a locally saved ZIP file:
This method works for both GeoJSON (from Obtaining Data #1) andFlatGeobuf (from Obtaining Data #2) formats. You can load a ZIPfile saved on your computer without unzipping it.
library(fude)d<- read_fude("~/2022_38.zip")
- By specifying a prefecture name or code:
This method is available only for FlatGeobuf data (from ObtainingData #2). Provide the name of a prefecture (e.g., “愛媛”) or itscorresponding prefecture code (e.g., “38”), and the requiredFlatGeobuf format ZIP file will be automatically downloaded andloaded.
d2<- read_fude(pref="愛媛")
Note: This feature is available only for data obtained from GeoJSON(Obtaining Data #1).
Convert local government codes into Japanese municipality names foreasier 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"
Download the agricultural community boundary data, which corresponds tothe Fude Polygon data, from the MAFF website:https://www.maff.go.jp/j/tokei/census/shuraku_data/2020/ma/ (availableonly in Japanese).
b<- get_boundary(d)
You can easily combine Fude Polygons with agricultural communityboundaries to create enriched spatial analyses or maps.
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$fude, aes(fill=RCOM_NAME),alpha=.8)+ guides(fill= guide_legend(reverse=TRUE,title="興居島の集落別耕地"))+ theme_void()+ theme(legend.position="bottom")+ theme(text= element_text(family="Hiragino Sans"))
出典：農林水産省「筆ポリゴンデータ（2022年度公開）」および「農業集落境界データ（2020年度）」を加工して作成。
db$fude: Automatically assigns polygons on the boundaries to acommunity.db$fude_split: Provides cleaner boundaries, but polygon data nearcommunity borders may be divided.
library(patchwork)fude<- ggplot()+ geom_sf(data=db$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$fude_split, aes(fill=RCOM_NAME),alpha=.8)+ theme_void()+ theme(legend.position="none")+ coord_sf(xlim= c(132.658,132.678),ylim= c(33.887,33.902))fude+fude_split
If you need to adjust this automatic assignment, you will need to writecustom code. The rows that require attention can be identified with thefollowing command.
library(dplyr)library(sf)db$fude|> filter(polygon_uuid%in% (db$fude_split|> filter(duplicated(polygon_uuid))|> pull(polygon_uuid)))|> st_drop_geometry()|> select(polygon_uuid,KCITY_NAME,RCOM_NAME,RCOM_ROMAJI)|> head()#> polygon_uuid KCITY_NAME RCOM_NAME RCOM_ROMAJI#> 1 8085bc47-9af5-440f-89e9-f188d3b95746 興居島村 泊 Tomari#> 2 26920da0-b63e-4994-a9eb-175e2982fe21 興居島村 門田 Kadota#> 3 ac2e7293-6c2f-4feb-a95f-4729dc8d0aec 興居島村 由良 Yura#> 4 ea130038-7035-4cf3-b71c-091783090d74 興居島村 船越 Funakoshi#> 5 4aba8229-1b14-4eab-8a91-e10d9e841180 興居島村 船越 Funakoshi#> 6 156a3459-25cb-494c-824f-9ba6b0fb6f23 興居島村 由良 Yura
The FlatGeobuf format offers a more efficient alternative to GeoJSON. Anotable feature of this format is that each record already includes anaccurately assigned agricultural community code.
db2<- combine_fude(d2,b,city="松山市",community="由良|北浦|鷲ケ巣|門田|馬磯|泊|御手洗|船越")ggplot()+ geom_sf(data=db2$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（2024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 village names, andagricultural community names.
Note: This feature is available only for data obtained fromFlatGeobuf (Obtaining Data #2).
d2|> extract_fude(city="松山市",kcity="興居島")#> Simple feature collection with 1690 features and 7 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 87a649f2-0385-4daf-81ba-82a61d44dd1b 200 2024 132.6446#> 2 bc56286f-b6a0-48c0-826a-97ce21b50de6 200 2024 132.6447#> 3 417bda37-fd35-44be-9c15-a89ed40eb28d 200 2024 132.6445#> 4 a2823989-8451-4982-9ba4-27dca5f21a38 200 2024 132.6441#> 5 d41c3920-d3ec-4bde-b461-c207d77d9b11 200 2024 132.6437#> 6 78d5397b-1a63-4257-8b01-aa365bfb5138 200 2024 132.6434#> 7 5af7e914-38d6-4e5d-867b-c0ab2d8f904a 200 2024 132.6436#> 8 1b0126bd-6869-4986-a5bf-8c59939ed50d 200 2024 132.6420#> 9 be6c809a-2b57-4a79-b123-1dd6669e0221 200 2024 132.6421#> 10 58f4149a-273b-4b4a-95c9-1ac353580619 200 2024 132.6423#> point_lat key local_government_cd geometry#> 1 33.88813 3820102004 382019 MULTIPOLYGON (((132.6446 33...#> 2 33.88768 3820102004 382019 MULTIPOLYGON (((132.6444 33...#> 3 33.88746 3820102004 382019 MULTIPOLYGON (((132.6448 33...#> 4 33.88755 3820102004 382019 MULTIPOLYGON (((132.6442 33...#> 5 33.88740 3820102004 382019 MULTIPOLYGON (((132.6437 33...#> 6 33.88729 3820102004 382019 MULTIPOLYGON (((132.6434 33...#> 7 33.88770 3820102004 382019 MULTIPOLYGON (((132.6435 33...#> 8 33.88782 3820102004 382019 MULTIPOLYGON (((132.6418 33...#> 9 33.88792 3820102004 382019 MULTIPOLYGON (((132.6422 33...#> 10 33.88765 3820102004 382019 MULTIPOLYGON (((132.6422 33...
You can review Fude Polygon data in detail.
library(shiny)s<- shiny_fude(db,community=TRUE)# shiny::shinyApp(ui = s$ui, server = s$server)
This feature was heavily inspired by the following website:https://brendenmsmith.com/blog/shiny_map_filter/.
If you want to usemapview(), 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")
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