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Propensity to Cycle Tool

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The Propensity to Cycle Tool

The Propensity to Cycle Tool
In pct: Propensity to Cycle Tool 

# get citations
refs = RefManageR::ReadZotero(group = "418217", .params = list(collection = "JFR868KJ", limit = 100))
refs_df = as.data.frame(refs)
# View(refs_df)
# citr::insert_citation(bib_file = "vignettes/refs_training.bib")
RefManageR::WriteBib(refs, "refs.bib")
# citr::tidy_bib_file(rmd_file = "vignettes/pct_training.Rmd", messy_bibliography = "vignettes/refs_training.bib")
options(htmltools.dir.version = FALSE)
knitr::opts_chunk$set(message = FALSE)
library(RefManageR)
BibOptions(check.entries = FALSE, 
           bib.style = "authoryear", 
           cite.style = 'alphabetic', 
           style = "markdown",
           first.inits = FALSE,
           hyperlink = FALSE, 
           dashed = FALSE)
my_bib = refs

library(RefManageR)
my_bib = RefManageR::ReadBib("refs.bib")

# publish results online
cp -Rv inst/rmd/pct-slides* ~/saferactive/site/static/slides/
cp -Rv inst/rmd/libs ~/saferactive/site/static/slides/
cd ~/saferactive/site
git add -A
git status
git commit -am 'Update slides'
git push
cd -

Intro + agenda

This workshop will provide an overview of the PCT for advanced users, including:

--

The workshop will be broken into three main parts:

Part 1: how the Propensity to Cycle Tool works + demo: 'I do' (14:00 - 15:00)

Part 2: Co-coding session: 'we do' (15:00 - 15:45)

☕☕☕ 15 minute break ☕☕☕

Part 3: using PCT data for your needs: 'you do' (16:00 - 17:00)

Description:

In this workshop you will take a deep dive into the Propensity to Cycle Tool (PCT). Beginner and intermediate PCT events focus on using the PCT via the web application hosted at www.pct.bike and the data provided by the PCT in QGIS but the focus here is on analysing cycling potential in the open source statistical programming language R. The majority of the PCT was built on R, which is a powerful object-orientated programming language with a focus on statistical modelling, visualisation and geographic analysis. The workshop will show how the code underlying the PCT works, how the underlying data can be accessed for reproducible analysis, and how the methods can be used to generate new scenarios of cycling uptake.

Preparation

If you are inexperienced with R you should prepare by

If you are new to R but have not completed the above tasks you may be unable to follow the second and third sections of the workshop outlined in the agenda below.

Prerequisites

See here for a guide on installing R and RStudio for transport data research. To get the access code for the tutorial, you will need to first work through the code shown here: https://github.com/ITSLeeds/pct/blob/master/inst/test-setup.R After you have run the code, running the following line should give you a number that will give you the access code for the course:

round(mean(rnet_potential$Potential))

Save that 3 digit number, it will allow access to the workshop. If you have any issues with your computer set-up, please ask a question here (you will need to sign-up for a GitHub account if you have not already done so): https://github.com/ITSLeeds/pct/issues/67

background-image: url(https://media.giphy.com/media/YlQQYUIEAZ76o/giphy.gif) background-size: cover class: center, middle

How the PCT works

The first prototype of the PCT

knitr::include_graphics("https://raw.githubusercontent.com/npct/pct-team/master/figures/early.png")

Photo: demo of the PCT to Secretary of State for Transport (March 2017)

The PCT in 2020

.pull-left[

Geographic levels in the PCT

.pull-right[

See these levels at www.pct.bike

]

Let's look at zones

qtm(od_data_zones_min)

MSOA vs LSOA zones (MSOA zones ~5 times bigger)

# not the best example
# knitr::include_graphics("https://user-images.githubusercontent.com/1825120/96573136-a1f55800-12c5-11eb-8921-c9938cd9e929.gif")
knitr::include_graphics(c(
  "https://user-images.githubusercontent.com/1825120/96583573-d3c1eb00-12d4-11eb-88b8-ca78087b63f7.png",
  "https://user-images.githubusercontent.com/1825120/96583629-eb00d880-12d4-11eb-9211-d015e2991267.png"
))
# 32844 / 6791

OD data

library(tmap)
library(od)
tmap_mode("plot")
od_test = od::od_data_df
od_test$id = 1:nrow(od_test)
od_test$perc_cycle = round(od_test$bicycle / od_test$all, 3) * 100
knitr::kable(od_test, caption = "Origin-destination data. Open MSOA-MSOA commute data from the 2011 census, accessed using the R package pct.")

Desire lines

l = od_to_sf(od_test, od_data_centroids)
l = dplyr::select(l, id, foot, bicycle, car_driver, perc_cycle)
p = od_data_centroids
p = p[l, ]
tm_shape(l) +
  tm_lines("perc_cycle", palette = "viridis", lwd = "car_driver", legend.lwd.show = FALSE, scale = 9, alpha = 0.5) +
  tm_text("id") +
  tm_shape(p) +
  tm_text("geo_code")

Routes

library(cyclestreets)
r = stplanr::route(l = l, route_fun = journey)
saveRDS(r, "routes_od_data_df_df.Rds")
piggyback::pb_upload("routes_od_data_df_df.Rds")
piggyback::pb_download_url("routes_od_data_df_df.Rds")
file.remove("routes_od_data_df_df.Rds")

tm_shape(r) +
  tm_lines("perc_cycle", palette = "viridis", lwd = "car_driver", legend.lwd.show = FALSE, scale = 9, alpha = 0.5) +
  tm_shape(p) +
  tm_text("geo_code")

Route networks

u = "https://github.com/ITSLeeds/pct/releases/download/0.5.0/routes_od_data_df_df.Rds"
f = basename(u)
if(!file.exists(f)){
  download.file(u, f)
}
r = readRDS(f)

rnet = stplanr::overline(r, "bicycle")
plot(rnet, lwd = rnet$bicycle / 10)

Cycling uptake

--

Dose/response modelling: about cycling in response to distance, hilliness and other factors. Source: pct R package website

background-image: url(https://user-images.githubusercontent.com/1825120/96583573-d3c1eb00-12d4-11eb-88b8-ca78087b63f7.png)

Live demo of the PCT for Bristol

See https://www.pct.bike/

.pull-left[

Uses of the PCT

Uses that were not initially planned

]

--

.pull-right[

What it cannot do

]

--

For further info, see the training materials at itsleeds.github.io

Many use cases on the PCT website: pct.bike/manual.html

Estimating health benefits of cycling uptake with the PCT

From evidence to network plans

Plans from Leeds City Council responding to national guidance and funding for 'pop-up' cycleways (image credit: Leeds City Council):

background-image: url(https://raw.githubusercontent.com/cyipt/popupCycleways/master/figures/results-top-leeds.png)

The Rapid tool - see cyipt.bike/rapid

The PCT Advanced Workshop

12th November, 2pm, online

The team

Robin Lovelace

--

Joey Talbot

Rosa Félix

How about you?

.pull-left[

]

--

.pull-right[

]

Overview of workshop

Part 1: how the Propensity to Cycle Tool works + demo: 'I do'

--

Part 2: Co-coding session: getting started with transport data in R: 'we do'

☕☕☕ 15 minute break ☕☕☕

Workshop overview - Part III

Part 3: using PCT data for local transport planning: 'you do'

library(leaflet)
l = stplanr::geo_code("Institute for Transport Studies, University of Leeds")
leaflet() %>% 
  addProviderTiles(provider = providers$OpenStreetMap.BlackAndWhite) %>%
  addMarkers(lng = l[1], lat = l[2])

Learning outcomes

--

It's about free and open source software for a sustainable future r emojifont::emoji("rocket")

background-image: url(https://media.giphy.com/media/YlQQYUIEAZ76o/giphy.gif)

Coding

Ideal:

od_test$perc_cycle = round(od_test$bicycle / od_test$all) * 100
l = od_to_sf(od_test, od_data_centroids)
r = stplanr::route(l = l, route_fun = journey)
rnet = overline(r, "bicycle")

--

Reality

Key stages of PCT approach

--

# Aim: get top 1000 lines in repo
library(dplyr)
library(sf)
desire_lines_all = pct::get_pct_lines(region = "isle-of-wight")
desire_lines = desire_lines_all %>% 
  top_n(1000, all)
write_sf(desire_lines, "desire_lines.geojson")
piggyback::pb_upload("desire_lines.geojson")

# Set-up, after installing pct and checking out www.pct.bike:
library(dplyr)
library(sf)
desire_lines_all = pct::get_pct_lines(region = "isle-of-wight") %>% 
  top_n(n = 1000, wt = all)

Stage II: Subset data of interest

library(sf)
desire_lines = desire_lines_all %>% 
  filter(all > 50) %>% 
  select(geo_code1, geo_code2, all, bicycle, foot, car_driver, rf_dist_km)
plot(desire_lines)

Stage III: Visualisation

.pull-left[
plot(desire_lines)
]
.pull-right[
]

library(tmap)
tmap_mode("view")
tm_shape(desire_lines) +
  tm_lines("all", scale = 9) +
  tm_basemap(server = leaflet::providers$OpenStreetMap)

Stage IV: Origin-destination data analysis

car_dependent_routes = desire_lines %>% 
  mutate(percent_drive = car_driver / all * 100) %>% 
  filter(rf_dist_km < 3 & rf_dist_km > 1) 

routes = stplanr::line2route(car_dependent_routes)
car_dependent_routes$geometry = routes$geometry

Other topics

Transport software - which do you use?

u = "https://github.com/ITSLeeds/TDS/raw/master/transport-software.csv"
tms = readr::read_csv(u)[1:5]
tms = dplyr::arrange(tms, dplyr::desc(Citations))
knitr::kable(tms, booktabs = TRUE, caption = "Sample of transport modelling software in use by practitioners. Note: citation counts based on searches for company/developer name, the product name and 'transport'. Data source: Google Scholar searches, October 2018.", format = "html")

Data science and the tidyverse

knitr::include_graphics("https://d33wubrfki0l68.cloudfront.net/b88ef926a004b0fce72b2526b0b5c4413666a4cb/24a30/cover.png")

A geographic perspective

Getting support

--

With open source software, the world is your support network!

--

--

--

References

PrintBibliography(my_bib)
# RefManageR::WriteBib(my_bib, "refs-geostat.bib")


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pct documentation built on May 31, 2023, 7:55 p.m.

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