# Merging route networks

    # library(stplanr)
    devtools::load_all()
    library(dplyr)
    library(tmap)
    library(ggplot2)
    library(tmaptools)
    rnet_x = sf::read_sf("https://github.com/ropensci/stplanr/releases/download/v1.0.2/rnet_x_ed.geojson")
    rnet_y = sf::read_sf("https://github.com/ropensci/stplanr/releases/download/v1.0.2/rnet_y_ed.geojson")
    # dups = duplicated(rnet_x$geometry)
    # summary(dups)
    # rnet_x = rnet_x |>
    #   filter(!dups)
    # sf::write_sf(rnet_x, "~/github/ropensci/stplanr/rnet_x_ed.geojson", delete_dsn = TRUE)

# Target network preprocessing

We pre-processed the input simple geometry to make it even simpler as shown below.

    # tmap_mode("view")
    # nrow(rnet_x)
    # summary(sf::st_length(rnet_x))
    plot(sf::st_geometry(rnet_x))
    rnet_x = rnet_subset(rnet_x, rnet_y, dist = 20)
    # nrow(rnet_x)
    # plot(sf::st_geometry(rnet_x))
    rnet_x = rnet_subset(rnet_x, rnet_y, dist = 20, min_length = 5)
    # summary(sf::st_length(rnet_x))
    # nrow(rnet_x)
    # plot(sf::st_geometry(rnet_x))
    rnet_x = rnet_subset(rnet_x, rnet_y, dist = 20, rm_disconnected = TRUE)
    # nrow(rnet_x)
    plot(sf::st_geometry(rnet_x))

The initial merged result was as follows (original data on left)

    funs = list(value = sum, Quietness = mean)
    brks = c(0, 100, 500, 1000, 5000)
    system.time({
      rnet_merged = rnet_merge(rnet_x, rnet_y, dist = 10, segment_length = 20, funs = funs)
    })
    m1 = tm_shape(rnet_y) + tm_lines("value", palette = "viridis", lwd = 5, breaks = brks)  +
      tm_scale_bar()
    m2 = tm_shape(rnet_merged) + tm_lines("value", palette = "viridis", lwd = 5, breaks = brks)
    tmap_arrange(m1, m2, sync = TRUE, nrow = 1)

Speed-up the results by transforming to a projected coordinate system:

    rnet_x = sf::st_transform(rnet_x, 27700)
    rnet_y = sf::st_transform(rnet_y, 27700)

    rnet_y_segmented = line_segment(rnet_y, segment_length = 20, use_rsgeo = TRUE)
    system.time({
      rnet_merged2 = rnet_merge(rnet_x, rnet_y, dist = 10, segment_length = 20, funs = funs)
    })

Let’s check the results:

    names(rnet_merged)
    summary(rnet_merged$value)
    summary(rnet_y$value)
    sum(rnet_merged$value * sf::st_length(rnet_merged), na.rm = TRUE)
    sum(rnet_y$value * sf::st_length(rnet_y), na.rm = TRUE)

We can more reduce the minimum segment length to ensure fewer NA values in the outputs:

    rnet_merged = rnet_merge(rnet_x, rnet_y, dist = 20, segment_length = 10, funs = funs)
    m1 = tm_shape(rnet_y) + tm_lines("value", palette = "viridis", lwd = 5, breaks = brks)
    m2 = tm_shape(rnet_merged) + tm_lines("value", palette = "viridis", lwd = 5, breaks = brks)
    tmap_arrange(m1, m2, sync = TRUE, nrow = 1)

As shown in the results, some sideroad values have unrealistically high values:

Let’s see the results again:

    summary(rnet_merged$value)
    summary(rnet_y$value)
    sum(rnet_merged$value * sf::st_length(rnet_merged), na.rm = TRUE)
    sum(rnet_y$value * sf::st_length(rnet_y), na.rm = TRUE)

The good news: the number of NAs is down to only 21 compared with the previous 100+. Bad news: sideroads have been assigned values from the main roads.

We can fix this with the `max_angle_diff` argument:

    rnet_merged = rnet_merge(rnet_x, rnet_y, dist = 20, segment_length = 10, funs = funs, max_angle_diff = 20)
    m1 = tm_shape(rnet_y) + tm_lines("value", palette = "viridis", lwd = 5, breaks = brks)
    m2 = tm_shape(rnet_merged) + tm_lines("value", palette = "viridis", lwd = 5, breaks = brks)
    tmap_arrange(m1, m2, sync = TRUE, nrow = 1)

As shown in the results, the sideroad values are fixed:

Let’s see the results again:

    summary(rnet_merged$value)
    summary(rnet_y$value)
    sum(rnet_merged$value * sf::st_length(rnet_merged), na.rm = TRUE)
    sum(rnet_y$value * sf::st_length(rnet_y), na.rm = TRUE)

It also works with charaster strings:

    rnet_y$char = paste0("road", sample(1:3, nrow(rnet_y), replace = TRUE))
    most_common = function(x) {
      ux = unique(x)
      ux[which.max(tabulate(match(x, ux)))]
    }
    funs = list(char = most_common)
    system.time({
      rnet_merged = rnet_merge(rnet_x, rnet_y, dist = 10, segment_length = 20, funs = funs)
    })
    plot(rnet_y["char"])
    plot(rnet_merged["char"])

Now let’s testing on 3km dataset

    rnet_x = sf::read_sf("https://github.com/nptscot/networkmerge/releases/download/v0.1/os_3km.geojson")
    rnet_y = sf::read_sf("https://github.com/nptscot/npt/releases/download/rnet_3km_buffer/rnet_3km_buffer.geojson")

Read columns from rnet_y to assign functions to them

    # Extract column names from the rnet_x data frame
    name_list <- names(rnet_y)
    name_list
    # Initialize an empty list
    funs <- list()

    # Loop through each name and assign it a function based on specific conditions
    for (name in name_list) {
      if (name == "geometry") {
        next  # Skip the current iteration
      } else if (name %in% c("Gradient", "Quietness")) {
        funs[[name]] <- mean
      } else {
        funs[[name]] <- sum
      }
    }

    brks = c(0, 100, 500, 1000, 5000,10000)
    colors <- c("green", "yellow", "blue", "purple", "red")
    rnet_merged = rnet_merge(rnet_x, rnet_y, dist = 20, segment_length = 10, funs = funs, max_angle_diff = 20)
    # st_write(rnet_merged, "data-raw/3km_exmaple_merged.geojson", driver = "GeoJSON")
    rnet_merged <- st_make_valid(rnet_merged)
    m1 = tm_shape(rnet_y) + tm_lines("all_fastest_bicycle", palette = "viridis", lwd = 5, breaks = brks)
    m2 = tm_shape(rnet_merged) + tm_lines("all_fastest_bicycle", palette = "viridis", lwd = 5, breaks = brks)
    tmap_arrange(m1, m2, sync = TRUE, nrow = 1)

Read 3km_exmaple_merged from github

    exmaple_3km = sf::read_sf("https://github.com/nptscot/networkmerge/releases/download/v0.1/3km_exmaple_merged.geojson")
    names(rnet_y)

    summary(rnet_y$all_fastest_bicycle)
    summary(exmaple_3km$all_fastest_bicycle)
    sum(exmaple_3km$all_fastest_bicycle * sf::st_length(exmaple_3km), na.rm = TRUE)
    sum(rnet_y$all_fastest_bicycle * sf::st_length(rnet_y), na.rm = TRUE)