jpmesh

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Overview

The jpmesh package 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.

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”.

Installation

Fron CRAN

install.packages("jpmesh")

For developers

# the development version from GitHub:
install.packages("devtools")
devtools::install_github("uribo/jpmesh")

Usage

library(jpmesh)
library(dplyr, warn.conflicts = FALSE)

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
#> # A tibble: 1 x 4
#>   lng_center lat_center lng_error lat_error
#>        <dbl>      <dbl>     <dbl>     <dbl>
#> 1        134       34.6    0.0625    0.0417
mesh_to_coords(51337783) # 1km
#> # A tibble: 1 x 4
#>   lng_center lat_center lng_error lat_error
#>        <dbl>      <dbl>     <dbl>     <dbl>
#> 1        134       34.7   0.00625   0.00417
mesh_to_coords(513377831) # 500m
#> # A tibble: 1 x 4
#>   lng_center lat_center lng_error lat_error
#>        <dbl>      <dbl>     <dbl>     <dbl>
#> 1        134       34.7   0.00312   0.00208
mesh_to_coords(5133778312) # 250m
#> # A tibble: 1 x 4
#>   lng_center lat_center lng_error lat_error
#>        <dbl>      <dbl>     <dbl>     <dbl>
#> 1        134       34.7   0.00156   0.00104
mesh_to_coords(51337783123) # 125m
#> # A tibble: 1 x 4
#>   lng_center lat_center lng_error lat_error
#>        <dbl>      <dbl>     <dbl>     <dbl>
#> 1        134       34.7  0.000781  0.000521

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

# Returns a finer mesh of the area of the mesh codes
# Such as, 80km to 10km mesh codes.
coords_to_mesh(133, 34, "80km") %>% 
  fine_separate()
#>  [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"

# the value of the adjacent mesh codes
coords_to_mesh(133, 34, "80km") %>% 
  find_neighbor_mesh()
#> [1] 5032 5033 5034 5132 5133 5134 5232 5233 5234
coords_to_mesh(133, 34, "500m") %>% 
  find_neighbor_mesh()
#> [1] 513299894 513299903 513299904 513299992 513299994 513300001 513300002
#> [8] 513300003 513300004

Utilies

Drawing a simplified Japanese map based on the mesh code.

library(sf)
#> Linking to GEOS 3.6.1, GDAL 2.1.3, proj.4 4.9.3
plot(jpnrect["abb_name"])
library(ggplot2) # 2.2.1.9000
ggplot() +
  geom_sf(data = jpnrect)

Dataset of mesh code for prefectures.

set.seed(71)
administration_mesh(code = 33, type = "prefecture") %>% 
  dplyr::sample_n(5) %>% 
  knitr::kable()
meshcode lng_center lat_center lng_error lat_error geometry
33 523440 134.0625 35.04167 0.0625 0.0416667 134.00000, 134.12500, 134.12500, 134.00000, 134.00000, 35.00000, 35.00000, 35.08333, 35.08333, 35.00000
54 523322 133.3125 34.87500 0.0625 0.0416667 133.25000, 133.37500, 133.37500, 133.25000, 133.25000, 34.83333, 34.83333, 34.91667, 34.91667, 34.83333
32 523377 133.9375 35.29167 0.0625 0.0416667 133.87500, 134.00000, 134.00000, 133.87500, 133.87500, 35.25000, 35.25000, 35.33333, 35.33333, 35.25000
21 513355 133.6875 34.45833 0.0625 0.0416667 133.62500, 133.75000, 133.75000, 133.62500, 133.62500, 34.41667, 34.41667, 34.50000, 34.50000, 34.41667
30 523357 133.9375 35.12500 0.0625 0.0416667 133.87500, 134.00000, 134.00000, 133.87500, 133.87500, 35.08333, 35.08333, 35.16667, 35.16667, 35.08333

Example)

# For leaflet
library(leaflet)
leaflet() %>% addTiles() %>% 
  addProviderTiles("OpenStreetMap.BlackAndWhite") %>% 
  addPolygons(data = administration_mesh(code = 33101, type = "city"))
ggplot() + 
  geom_sf(data = administration_mesh(code = 33, type = "city"))