Swisscom grid coordinates & IDs

Tile definitions were pulled from API using query_swisscom_heatmaps_api.py.

read_tiles <- function(filename) {
  
  data <- jsonlite::fromJSON(filename)
  data <- jsonlite::flatten(data$tiles) %>% 
    dplyr::as_tibble()
  
  data$plz <- gsub("grid_|.json", "", filename)
  data$plz <- gsub("data/swisscom/", "", data$plz)
  
  return( data )
}

doFuture::registerDoFuture()
future::plan("multisession", workers = 8)

grid <- plyr::ldply(.data = fs::dir_ls("data/swisscom/", 
                                       regexp = "[0-9][.]json$"),
                    .fun = read_tiles,
                    .id = NULL,
                    .parallel = TRUE) %>% 
  as_tibble() %>% 
  distinct()

Focusing on test area of Bern city centre and selected suburbs, including postal codes:

plz <character> 
# total N=9916 valid N=9916 mean=3063.57 sd=56.61

Value |    N | Raw % | Valid % | Cum. %
---------------------------------------
 3005 |  198 |  2.00 |    2.00 |   2.00
 3006 |  604 |  6.09 |    6.09 |   8.09
 3007 |  254 |  2.56 |    2.56 |  10.65
 3008 |  445 |  4.49 |    4.49 |  15.14
 3010 |   28 |  0.28 |    0.28 |  15.42
 3011 |  138 |  1.39 |    1.39 |  16.81
 3012 |  581 |  5.86 |    5.86 |  22.67
 3013 |  176 |  1.77 |    1.77 |  24.45
 3014 |  366 |  3.69 |    3.69 |  28.14
 3018 |  590 |  5.95 |    5.95 |  34.09
 3027 |  720 |  7.26 |    7.26 |  41.35
 3037 |  226 |  2.28 |    2.28 |  43.63
 3047 |  237 |  2.39 |    2.39 |  46.02
 3066 |  795 |  8.02 |    8.02 |  54.03
 3072 |  696 |  7.02 |    7.02 |  61.05
 3073 |  509 |  5.13 |    5.13 |  66.19
 3074 |  389 |  3.92 |    3.92 |  70.11
 3084 |  526 |  5.30 |    5.30 |  75.41
 3095 |  152 |  1.53 |    1.53 |  76.95
 3097 |  182 |  1.84 |    1.84 |  78.78
 3098 | 1107 | 11.16 |   11.16 |  89.95
 3202 |  997 | 10.05 |   10.05 | 100.00
 <NA> |    0 |  0.00 |    <NA> |   <NA>

Points of grid were defined using lower left corner coordinates. They were also shifted by 50m east and north to better align with grids.

grid_sf <- grid %>% 
  st_as_sf(coords = c("ll.x", "ll.y"), 
           crs = 4326,
           remove = TRUE) %>% 
  st_transform(21781) %>% 
  mutate(x = st_coordinates(.)[, 1],
         y = st_coordinates(.)[, 2]) %>% 
  select(-ur.x, -ur.y)

# shifting by 50m to the centre
grid_sf_50 <- grid_sf %>% 
  st_drop_geometry() %>% 
  mutate(x = as.integer(as.integer(x) + 51), # why on earth 1?
         y = as.integer(as.integer(y) + 50)) %>% 
  st_as_sf(coords = c("x", "y"), 
           crs = 21781,
           remove = FALSE) 

Grid derived with swisscom offset

swisscom points were linked to country grid derived in file 01.Rmd providing access to crucial tile ID variable needed to link to the Heatmap API outputs.

bern_plz <- 
  read_rds("data/grid/country.Rds") %>% 
  st_join(grid_sf_50,
          left = FALSE)

write_rds(bern_plz, "data/grid/bern_plz.Rds")

x <lgl> 
# total N=9916 valid N=9916 mean=0.10 sd=0.29

Value |    N | Raw % | Valid % | Cum. %
---------------------------------------
FALSE | 8968 | 90.44 |   90.44 |  90.44
TRUE  |  948 |  9.56 |    9.56 | 100.00
<NA>  |    0 |  0.00 |    <NA> |   <NA>

Environment

Analyses were conducted using the R Statistical language (version 4.2.0; R Core Team, 2022) on Windows 10 x64 (build 18363), using the packages ggplot2 (version 3.3.6; Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.), jsonlite (version 1.8.0; Jeroen Ooms, 2014), sf (version 1.0.7; Pebesma, 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10, 1), pacman (version 0.5.1; Rinker et al., 2017), tidyverse (version 1.3.1; Wickham et al., 2019), dplyr (version 1.0.9; NA), forcats (version 0.5.1; NA), magrittr (version 2.0.3; NA), purrr (version 0.3.4; NA), readr (version 2.1.2; NA), scales (version 1.2.0; NA), stringr (version 1.4.0; NA), tibble (version 3.1.7; NA), tidyr (version 1.2.0; NA) and tmap (version 3.3.3; NA).