The jpmesh is a package that makes it easy to use "regional mesh (i.e. mesh code JIS X 0410 )" used in Japan from R. Regional mesh is a code given when subdividing Japanese landscape into rectangular subregions by latitude and longitude. Depending on the accuracy of the code, different regional mesh length. By using the same mesh in statistical survey etc., it will become possible to handle the survey results of a large area in the area mesh unit.
2018/3/8
jpmesh
jpmesh
In jpmesh, mesh codes and latitude and longitude coordinates are compatible with mesh codes from the first region mesh, which is the standard region mesh, to the quarter regional mesh of the divided region mesh (from 80 km to 125 m). Features include "conversion from latitude and longitude to regional mesh", "acquisition of latitude and longitude from regional mesh", "mapping on prefecture unit and leaflet".
Usage
Install
Available CRAN (version 1.1.0), and olso GitHub develop versions.
# CRAN
install.packages("jpmesh")
# the development version from GitHub:
install.packages("remotes")
remotes::install_github("uribo/jpmesh")
library(jpmesh) library(sf) library(leaflet)
Convert mesh code to coordinate and vice versa
Return the latitude and longitude for specifying the mesh range from the mesh code.
mesh_to_coords(5133) # 80km
## # A tibble: 1 x 4 ## lng_center lat_center lng_error lat_error ## <dbl> <dbl> <dbl> <dbl> ## 1 134. 34.3 0.500 0.333
mesh_to_coords(513377) # 10km # ... mesh_to_coords(51337783123) # 125m
Convert mesh code to coordinate and vice versa
Find the mesh code within the range from latitude and longitude.
coords_to_mesh(133, 34) # default as 1km meshcode
## [1] "51330000"
coords_to_mesh(133, 34, mesh_size = "80km")
## [1] "5133"
coords_to_mesh(133, 34, mesh_size = "125m")
## [1] "51330000111"
Detect fine and neighborhood mesh codes
mesh_80km <- coords_to_mesh(133, 34, "80km") # Convert to sfc_POLYGON mesh_polygon <- mesh_80km %>% export_mesh() mesh_polygon
## Geometry set for 1 feature ## geometry type: POLYGON ## dimension: XY ## bbox: xmin: 133 ymin: 34 xmax: 134 ymax: 34.66667 ## epsg (SRID): 4326 ## proj4string: +proj=longlat +datum=WGS84 +no_defs
## POLYGON ((133 34, 134 34, 134 34.66667, 133 34....
Detect fine and neighborhood mesh codes
mesh_polygon %>% st_geometry() %>% plot()
Detect fine and neighborhood mesh codes
# Returns a finer mesh of the area of the mesh codes # Such as, 80km to 10km mesh codes. meshes_10km <- mesh_80km %>% fine_separate() meshes_10km
## [1] "513300" "513301" "513302" "513303" "513304" "513305" "513306" ## [8] "513307" "513310" "513311" "513312" "513313" "513314" "513315" ## [15] "513316" "513317" "513320" "513321" "513322" "513323" "513324" ## [22] "513325" "513326" "513327" "513330" "513331" "513332" "513333" ## [29] "513334" "513335" "513336" "513337" "513340" "513341" "513342" ## [36] "513343" "513344" "513345" "513346" "513347" "513350" "513351" ## [43] "513352" "513353" "513354" "513355" "513356" "513357" "513360" ## [50] "513361" "513362" "513363" "513364" "513365" "513366" "513367" ## [57] "513370" "513371" "513372" "513373" "513374" "513375" "513376" ## [64] "513377"
Detect fine and neighborhood mesh codes
meshes_10km %>% export_meshes() %>% plot()
Detect fine and neighborhood mesh codes
# the value of the adjacent mesh codes coords_to_mesh(133, 34, "80km") %>% neighbor_mesh()
## [1] "5032" "5033" "5034" "5132" "5133" "5134" "5232" "5233" "5234"
coords_to_mesh(133, 34, "500m") %>% neighbor_mesh()
## [1] "503277994" "503370903" "503370904" "513207092" "513207094" "513300003" ## [7] "513300004" "513300001"
Detect fine and neighborhood mesh codes
mesh_1km_neighbors <- coords_to_mesh(133, 34, "1km") %>% neighbor_mesh() mesh_1km_neighbors %>% export_meshes() %>% st_geometry() %>% plot()
1km neighborhood meshes
Plots and Visualize
ggplot2
remotes::install_github("hadley/ggplot2")
library(ggplot2)
ggplot() + geom_sf(data = mesh_1km_neighbors %>% export_meshes(), aes(fill = meshcode)) + theme_bw()
Administration mesh
set.seed(71) # Select prefecture or city code administration_mesh(code = 33, type = "prefecture") %>% sample(5)
## Simple feature collection with 98 features and 4 fields ## geometry type: POLYGON ## dimension: XY ## bbox: xmin: 133.25 ymin: 34.25 xmax: 134.5 ymax: 35.41667 ## epsg (SRID): 4326 ## proj4string: +proj=longlat +datum=WGS84 +no_defs ## First 10 features: ## lng_center lat_center meshcode lat_error ## 1 133.8125 34.62500 513376 0.04166667 ## 2 133.9375 34.62500 513377 0.04166667 ## 3 133.6875 34.70833 523305 0.04166667 ## 4 133.8125 34.70833 523306 0.04166667 ## 5 133.9375 34.70833 523307 0.04166667 ## 6 133.6875 34.79167 523315 0.04166667 ## 7 133.8125 34.79167 523316 0.04166667 ## 8 133.9375 34.79167 523317 0.04166667 ## 9 133.8125 34.87500 523326 0.04166667 ## 10 133.9375 34.87500 523327 0.04166667 ## geometry ## 1 POLYGON ((133.75 34.58333, ... ## 2 POLYGON ((133.875 34.58333,... ## 3 POLYGON ((133.625 34.66667,... ## 4 POLYGON ((133.75 34.66667, ... ## 5 POLYGON ((133.875 34.66667,... ## 6 POLYGON ((133.625 34.75, 13... ## 7 POLYGON ((133.75 34.75, 133... ## 8 POLYGON ((133.875 34.75, 13... ## 9 POLYGON ((133.75 34.83333, ... ## 10 POLYGON ((133.875 34.83333,...
Mapping on Leaflet
leaflet() %>%
addTiles() %>%
addProviderTiles("OpenStreetMap.BlackAndWhite") %>%
addPolygons(data = administration_mesh(code = 33101, type = "city"))