The goal of sheffield_cycle_data is to get started with key datasets on cycling, infrastructure, behaviour and maybe more.
There are a number of packages that can help get transport (including cycling) data. Install them as follows:
install.packages("remotes")
#> Installing package into '/home/robin/R/x86_64-pc-linux-gnu-library/4.1'
#> (as 'lib' is unspecified)remotes::install_cran(c("pct", "osmextract", "osmdata", "stats19", "tidyverse", "tmap", "skimr"))
#> Skipping install of 'pct' from a cran remote, the SHA1 (0.9.3) has not changed since last install.
#> Use `force = TRUE` to force installation
#> Skipping install of 'osmextract' from a cran remote, the SHA1 (0.4.0) has not changed since last install.
#> Use `force = TRUE` to force installation
#> Skipping install of 'osmdata' from a cran remote, the SHA1 (0.1.9) has not changed since last install.
#> Use `force = TRUE` to force installation
#> Skipping install of 'stats19' from a cran remote, the SHA1 (2.0.0) has not changed since last install.
#> Use `force = TRUE` to force installation
#> Skipping install of 'tidyverse' from a cran remote, the SHA1 (1.3.1) has not changed since last install.
#> Use `force = TRUE` to force installation
#> Skipping install of 'tmap' from a cran remote, the SHA1 (3.3-3) has not changed since last install.
#> Use `force = TRUE` to force installation
#> Skipping install of 'skimr' from a cran remote, the SHA1 (2.1.4) has not changed since last install.
#> Use `force = TRUE` to force installationYou can load them as follows:
library(pct)
library(stats19)
library(tidyverse)
library(tmap)
# tmap_mode("view") # uncomment this for interactive maps
tmap_mode("plot")
#> tmap mode set to plotting# https://github.com/npct/pct-outputs-regional-notR/blob/master/commute/lsoa/south-yorkshire/rnet_full.geojson
cycle_trips_network_estimates = get_pct_rnet(region = "south-yorkshire")
zones = get_pct_zones("south-yorkshire")You can plot this data in a static map as follows:
zones %>%
select(lad_name) %>%
plot()
cycle_trips_network_estimates %>%
select(dutch_slc) %>%
plot()
Let’s subset the data representing Sheffield.
zones_sheffield = zones %>%
filter(lad_name == "Sheffield")rnet_sheffield = cycle_trips_network_estimates[zones_sheffield, ]rnet_sheffield %>%
tm_shape() +
tm_lines(lwd = "ebike_slc", scale = 9)
#> Legend labels were too wide. Therefore, legend.text.size has been set to 0.54. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.
extra_tags = c("maxspeed", "bicycle")
osm_data_region = osmextract::oe_get_network(place = "south yorkshire", mode = "cycling", extra_tags = extra_tags)
#> The input place was matched with: South Yorkshire
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#> File downloaded!
#> Start with the vectortranslate operations on the input file!
#> 0...10...20...30...40...50...60...70...80...90...100 - done.
#> Finished the vectortranslate operations on the input file!
#> Reading layer `lines' from data source
#> `/tmp/Rtmp0pGon9/geofabrik_south-yorkshire-latest.gpkg' using driver `GPKG'
#> Simple feature collection with 90808 features and 13 fields
#> Geometry type: LINESTRING
#> Dimension: XY
#> Bounding box: xmin: -1.857621 ymin: 53.2878 xmax: -0.8204247 ymax: 53.67845
#> Geodetic CRS: WGS 84
osm_sheffield = osm_data_region[zones_sheffield, ]osm_sheffield %>%
sf::st_drop_geometry() %>%
skimr::skim()| Name | Piped data |
| Number of rows | 37633 |
| Number of columns | 13 |
| _______________________ | |
| Column type frequency: | |
| character | 12 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Data summary
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| osm_id | 0 | 1.00 | 7 | 10 | 0 | 37633 | 0 |
| name | 23349 | 0.38 | 3 | 42 | 0 | 6516 | 0 |
| highway | 0 | 1.00 | 4 | 14 | 0 | 21 | 0 |
| waterway | 37633 | 0.00 | NA | NA | 0 | 0 | 0 |
| aerialway | 37633 | 0.00 | NA | NA | 0 | 0 | 0 |
| barrier | 37633 | 0.00 | NA | NA | 0 | 0 | 0 |
| man_made | 37628 | 0.00 | 4 | 10 | 0 | 3 | 0 |
| access | 37243 | 0.01 | 2 | 11 | 0 | 9 | 0 |
| bicycle | 35522 | 0.06 | 3 | 11 | 0 | 5 | 0 |
| service | 35389 | 0.06 | 5 | 16 | 0 | 8 | 0 |
| maxspeed | 31957 | 0.15 | 1 | 6 | 0 | 13 | 0 |
| other_tags | 20829 | 0.45 | 11 | 399 | 0 | 4272 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| z_order | 0 | 1 | 1.69 | 3.31 | -20 | 0 | 0 | 3 | 64 | ▁▇▁▁▁ |
osm_cycleways = osm_sheffield %>%
filter(highway == "cycleway")qtm(osm_cycleways)
To save the output you can use the st_write() function from the sf
package:
sf::st_write(obj = osm_cycleways, "osm_cycleways.geojson")You can then load the object in QGIS.
- Check out the stats19 R package, search for it and you’ll find plenty of documents!
- You can get road volume data from DfT
- Have a read and a play and let us know how you get on in the issue tracker