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cid.Rmd
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---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
echo = FALSE
)
library(tidyverse)
library(tmap)
```
# CID data types
There are 102 uniquely named columns in the CID, as shown below.
```{r tfl_schema}
# Get schema
u = "https://cycling.data.tfl.gov.uk/CyclingInfrastructure/documentation/cid_database_schema.xlsx"
f = basename(u)
if (!file.exists(f)) {
download.file(u, f)
}
readxl::excel_sheets(f)
tfl_schema = readxl::read_excel(f, sheet = 3)
# summary(duplicated(tfl_schema))
write_csv(tfl_schema, "example_data/tfl_field_names.csv")
tfl_schema |>
mutate(description = gsub(pattern = "\\n", replacement = " ", x = description)) |>
knitr::kable()
```
Converted into a dataset in the Arrow format, the column names and associated data types, example values, asset types and labels are as follows, for the Cycle lane/track table, for example:
```{r get_cid_once, eval=FALSE}
library(osmdata)
remotes::install_github("PublicHealthDataGeek/CycleInfraLnd")
?CycleInfraLnd::get_cid_lines
blackfriars_bridge_road = opq(bbox = "London") |>
add_osm_feature(key = "name", value = "Blackfriars Bridge") |>
osmdata_sf() |>
pluck("osm_multilines")
blackfriars_buffer = sf::st_buffer(blackfriars_bridge_road, 500)
mapview::mapview(blackfriars_bridge_road)
cid_cycle_lane_track = CycleInfraLnd::get_cid_lines(type = "cycle_lane_track")
is_true_false = function(x) {
all(x %in% c("TRUE", "FALSE"))
}
is_true_false(cid_cycle_lane_track$CLT_CARR)
is_true_false(cid_cycle_lane_track$FEATURE_ID)
cid_cycle = cid_cycle_lane_track |>
mutate_if(.predicate = is_true_false, .funs = as.logical)
cid_cycle_lane_track_blackfriars = cid_cycle_lane_track[blackfriars_buffer, , op = sf::st_within] |>
mutate_if(.predicate = is_true_false, .funs = as.logical)
dir.create("example_data")
sf::write_sf(cid_cycle_lane_track_blackfriars, "example_data/cid_cycle_lane_track_blackfriars.geojson", delete_dsn = TRUE)
tm_shape(cid_cycle_lane_track_blackfriars) +
tm_lines()
# ASL data
cid_asl = CycleInfraLnd::get_cid_lines(type = "advanced_stop_line")
cid_asl = cid_asl |>
mutate_if(.predicate = is_true_false, .funs = as.logical)
cid_asl_blackfriars = cid_asl[blackfriars_buffer, , op = sf::st_within] |>
mutate_if(.predicate = is_true_false, .funs = as.logical)
sf::write_sf(cid_asl_blackfriars, "example_data/cid_asl_blackfriars.geojson", delete_dsn = TRUE)
# crossing data
cid_crossing = CycleInfraLnd::get_cid_lines(type = "crossing")
cid_crossing = cid_crossing |>
mutate_if(.predicate = is_true_false, .funs = as.logical)
cid_crossing_blackfriars = cid_crossing[blackfriars_buffer, , op = sf::st_within] |>
mutate_if(.predicate = is_true_false, .funs = as.logical)
sf::write_sf(cid_crossing_blackfriars, "example_data/cid_crossing_blackfriars.geojson", delete_dsn = TRUE)
plot(cid_crossing_blackfriars)
# restricted data
cid_restricted = CycleInfraLnd::get_cid_lines(type = "restricted_route")
cid_restricted = cid_restricted |>
mutate_if(.predicate = is_true_false, .funs = as.logical)
cid_restricted_blackfriars = cid_restricted[blackfriars_buffer, , op = sf::st_within] |>
mutate_if(.predicate = is_true_false, .funs = as.logical)
sf::write_sf(cid_restricted_blackfriars, "example_data/cid_restricted_blackfriars.geojson", delete_dsn = TRUE)
# cycle parking
cid_cycle_parking = CycleInfraLnd::get_cid_points(type = "cycle_parking")
cid_cycle_parking = cid_cycle_parking |>
mutate_if(.predicate = is_true_false, .funs = as.logical)
cid_cycle_parking_blackfriars = cid_cycle_parking[blackfriars_buffer, , op = sf::st_within] |>
mutate_if(.predicate = is_true_false, .funs = as.logical)
sf::write_sf(cid_cycle_parking_blackfriars, "example_data/cid_cycle_parking_blackfriars.geojson", delete_dsn = TRUE)
# restricted data
cid_restricted_point = CycleInfraLnd::get_cid_points(type = "restricted_point")
cid_restricted_point = cid_restricted_point |>
mutate_if(.predicate = is_true_false, .funs = as.logical)
cid_restricted_point_blackfriars = cid_restricted_point[blackfriars_buffer, , op = sf::st_within] |>
mutate_if(.predicate = is_true_false, .funs = as.logical)
sf::write_sf(cid_restricted_point, "example_data/cid_restricted_point.geojson", delete_dsn = TRUE)
# signaage
cid_signage = CycleInfraLnd::get_cid_points(type = "signage")
cid_signage = cid_signage |>
mutate_if(.predicate = is_true_false, .funs = as.logical)
cid_signage_blackfriars = cid_signage[blackfriars_buffer, , op = sf::st_within] |>
mutate_if(.predicate = is_true_false, .funs = as.logical)
sf::write_sf(cid_signage_blackfriars, "example_data/cid_signage_blackfriars.geojson", delete_dsn = TRUE)
# signals
cid_signal = CycleInfraLnd::get_cid_points(type = "signal")
cid_signal = cid_signal |>
mutate_if(.predicate = is_true_false, .funs = as.logical)
cid_signal_blackfriars = cid_signal[blackfriars_buffer, , op = sf::st_within] |>
mutate_if(.predicate = is_true_false, .funs = as.logical)
sf::write_sf(cid_signal, "example_data/cid_signal_blackfriars.geojson", delete_dsn = TRUE)
# traffic_calmings
cid_traffic_calming = CycleInfraLnd::get_cid_points(type = "traffic_calming")
cid_traffic_calming = cid_traffic_calming |>
mutate_if(.predicate = is_true_false, .funs = as.logical)
cid_traffic_calming_blackfriars = cid_traffic_calming[blackfriars_buffer, , op = sf::st_within] |>
mutate_if(.predicate = is_true_false, .funs = as.logical)
sf::write_sf(cid_traffic_calming, "example_data/cid_traffic_calming_blackfriars.geojson", delete_dsn = TRUE)
```
```{r blackfriars_table, message=FALSE}
tfl_schema = read_csv("example_data/tfl_field_names.csv")
tfl_schema = tfl_schema |>
rename(Name = fieldname)
cid_cycle_lane = sf::read_sf("example_data/cid_cycle_lane_track_blackfriars.geojson")
# table(cid_cycle_lane$CLT_CARR)
# cid_cycle_lane_track_blackfriars |> as_tibble() |> slice(1:3)
cid_cycle_lane_df = cid_cycle_lane |>
sf::st_drop_geometry()
cid_cycle_lane_arrow = cid_cycle_lane_df |>
arrow::as_arrow_table()
arrow::write_parquet(cid_cycle_lane_arrow, "example_data/cid_cycle_lane_track_blackfriars.parquet")
# cid_cycle_lane_schema = cid_cycle_lane_arrow$schema
# cid_cycle_lane_schema[2]
# cid_cycle_types = NULL
# i = 1
# for(i in seq(ncol(cid_cycle_lane_df))) {
# cid_cycle_types = c(cid_cycle_types, class(arrow::infer_type(cid_cycle_lane_schema[]))[1])
# }
# arrow::type(cid_cycle_lane_arrow$columns)
# map(cid_cycle_lane_track, arrow::infer_type)
# cid_cycle_lane_schema$names
# cid_cycle_lane_schema$num_fields
# as.ch(cid_cycle_lane_schema$fields)
# knitr::kable(cid_cycle_schema)
```
```{r runouncesummary, eval=FALSE}
cid_tables = tibble::tribble(
~Table, ~Geometry, ~Rows, ~Columns,
"Advanced Stop Line", "Line", nrow(cid_asl), ncol(cid_asl),
"Crossing", "Line", nrow(cid_crossing), ncol(cid_crossing),
"Cycle lane/track", "Line", nrow(cid_cycle_lane_track), ncol(cid_cycle_lane_track),
"Restricted Route", "Line", nrow(cid_restricted), ncol(cid_restricted),
"Cycle Parking", "Point", nrow(cid_cycle_parking), ncol(cid_cycle_parking),
"Restricted Point", "Point", nrow(cid_restricted_point), ncol(cid_restricted_point),
"Signage", "Point", nrow(cid_signage), ncol(cid_signage),
"Signal", "Point", nrow(cid_signal), ncol(cid_signal),
"Traffic Calming", "Point", nrow(cid_traffic_calming), ncol(cid_traffic_calming)
)
cid_tables$Names = NA
cid_tables$Names[1] = paste0(names(cid_asl), collapse = ", ")
cid_tables$Names[2] = paste0(names(cid_crossing), collapse = ", ")
cid_tables$Names[3] = paste0(names(cid_cycle_lane_track_blackfriars), collapse = ", ")
cid_tables$Names[4] = paste0(names(cid_restricted), collapse = ", ")
cid_tables$Names[5] = paste0(names(cid_cycle_parking), collapse = ", ")
cid_tables$Names[6] = paste0(names(cid_restricted_point), collapse = ", ")
cid_tables$Names[7] = paste0(names(cid_signage), collapse = ", ")
cid_tables$Names[8] = paste0(names(cid_signal), collapse = ", ")
cid_tables$Names[9] = paste0(names(cid_traffic_calming), collapse = ", ")
write_csv(cid_tables, "example_data/cid_tables.csv")
```
```{r, message=FALSE}
# concise dataset
cid_cycle_lane_df = cid_cycle_lane |>
sf::st_drop_geometry()
cid_cycle_lane_arrow = cid_cycle_lane_df |>
arrow::as_arrow_table()
cid_cycle_out = utils::capture.output(cid_cycle_lane_arrow)
```
In a future schema, many of the variables in the schema above could be replaced by a well-defined 'type' column that accepts values including On Road Advisory Cycle Lane, Dedicated Cycle Track and Stepped Cycle Track (covering columns 3 to 5 in the above table).
See the repo <https://github.com/PublicHealthDataGeek/CycleInfraLnd/> and associated paper (Tait et al., [2022](https://www.sciencedirect.com/science/article/pii/S221414052200041X)) for further details on the CID.