osmtraces

The goal of osmtraces is to help with downloading publicly available traces from OpenStreetMaps

Installation

You can install the development version of osmtraces like so:

remotes::install_github("heike/osmtraces")

Example

This is a basic example which shows you how to solve a common problem:

library(osmtraces)
## basic example code
library(dplyr)

OSM Traces are made publicly available GPX files. Foremost, these traces help with making adjustments to OSM when roads are in slightly different locations than indicated on the map.

The link below show a highway intersection of I-30 and I-35 close to Ames IA:

https://www.openstreetmap.org/#map=16/42.00739/-93.57140&layers=PG

Based on this link, we can download all traces from the geographic latitude and longitude, using the zoom as a way to define the size of the bounding box:

# download traces into a predefined folder:
download_osm_tracks(lon = -93.57140, lat =42.00739, zoom = 15,
 folder="ames")

By default up to 20 pages with 5000 track points are downloaded. Further traces can be downloaded by re-running the command with an increased setting of first_page.

Pass the folder into the command to read all traces:

folder <- system.file("ames", package="osmtraces")
ames_tracks <- read_osm_traces(folder)

data(ames_tracks)

For each track, derive gps heading, speed and time between consecutive locations:

library(dplyr)
ames_tracks <- ames_tracks |> 
  group_by(name) |> 
  mutate(
  data = data |> purrr::map(.f = function(d) {
    d |> mutate(
      time = lubridate::ymd_hms(time),
    ) |> arrange(time) |>
      mutate(
        segment = segmentize(time, seconds = 10)
    ) 
  })) |> tidyr::unnest(data)

# now split by segment    
ames_tracks <- ames_trackpoints |> 
  group_by(name, desc, url, segment) |>
  tidyr::nest()

ames_tracks <- ames_tracks |> mutate(
  data = data |> purrr::map(.f = function(d) {
    d |> arrange(time) |>
      mutate(
        gps_heading = gps_heading(lon = lon, lat = lat),
        gps_speed = gps_speed_mph(lon = lon, lat = lat, time = time),
        dtime = c(diff(as.numeric(time)), NA)
      )
  })
)

library(ggplot2)
ames_trackpoints <- ames_tracks |> 
  ungroup() |>
  tidyr::unnest(data)

ames_trackpoints |> ggplot(aes(x = lon, y = lat, colour = gps_heading)) +
  geom_path(aes(group=name), alpha = 0.7, linewidth=1) +
  scale_colour_gradientn(
    colours = grDevices::hcl(
      h = seq(0, 360, length.out = 361),
      c = 100,
      l = 65
    ),
    limits = c(0, 360),
    breaks = seq(0, 360, by = 45)
  ) + ggthemes::theme_map()

ames_trackpoints |> ggplot(aes(x = lon, y = lat, colour = gps_speed)) +
  geom_path(aes(group=name))