This vignette supports workshops on advanced usage and development of 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.
The focus here is on analysing cycling potential in the open source statistical programming language R, in which the majority of the PCT was built. It 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.
If you are an intermediate user, it may be worth brushing-up on your R skills, e.g. by taking a free online course such as that provided by DataCamp or by working through Chapter 2 onwards of the open source book Geocomputation with R (see reading list below for more transport-specific resources).
To ensure your computer is ready for the course, you should be able to run the following lines of R code on your computer:
To test your computer is ready to work with PCT data in R, try running the following command:
If the new method does not work or you would like to be more hands on, run the code below. It should result in the map below, showing the % of short trips in Isle of Wight made by active modes.
library(pct) library(dplyr) # in the tidyverse library(tmap) # installed alongside mapview region_name = "isle-of-wight" max_distance = 7 zones_all = get_pct_zones(region_name) lines_all = get_pct_lines(region_name) # basic plot plot(zones_all$geometry) plot(lines_all$geometry[lines_all$all > 500], col = "red", add = TRUE)
We can also use the data to explore entrenched car dependence, as follows:
# Create car dependent desire lines car_dependent = lines_all %>% mutate(`Percent Drive` = (car_driver) / all * 100) %>% filter(e_dist_km < max_distance) tm_shape(car_dependent) + tm_lines("Percent Drive", palette = "-RdYlBu", lwd = "all", scale = 9) #> Legend for line widths not available in view mode.
Break and presentation of results
The PCT provides data at 4 geographic levels:
Which types of data are most appropriate to tackle each of the questions/problems you identified?
G1: Using the PCT’s online interface, hosted at www.pct.bike/m/?r=isle-of-wight, identify the MSOA zone that has the highest number of people who cycle to work.
G2: Using data downloaded with the command
get_pct_zones(), identify the zone that has highest level of cycling with the function
top_n() and save the result as an object called
z_highest_cycling (hint: you may want to start by ‘cleaning’ the data you have downloaded to include only a few key columns with the function
select(), as follows):
plot()command to visualise where on the Ilse of Wight this ‘high cycling’ zone is (hint: you will need to use the
plot()function twice, once to plot
z$geometry, and again with the argument
add = TRUEand a
colargument to add the layer on top of the base layer and give it a colour). The result should look something like something this:
G4: Using the online interface, identify the top 5 MSOA to MSOA desire lines that have the highest number of people who cycle to work.
G5: Using the function
get_pct_lines(), identify the top 5 MSOA to MSOA desire lines that have the highest number of people who cycle to work (hint: you might want to start with the code shown below).
geography = "lsoa", remember to change the names of the objects you create). The results should look something like this:
G7: Why are the results different? What are the advantages and disadvantages of using smaller zones, as represented by the LSOA data above?
G8 (bonus): do the same analysis but with the top 300 routes cycled and driven. Hint: set the line width with
lwd = l_top_cycling$bicycle / mean(l_top_cycling$bicycle) to portray the relative importance of each route.
pcycleto the object
l_msoathat contains the % who cycle to work (hint: you might want to start this by typing
l_msoa$pcycle = ...) and plot the results (shown in left hand panel in plot below).
uptake_pct_godutch()(hint: the following code chunk will create a ‘Government Target’ scenario):
pct_uptake_godutch()- how could it be modified?
route_osrm()find the route associated with the most cycled desire line in the Isle of Wight. The result should look similar to that displayed in the map below (hint: you may want to start your answer with the following lines of code - warning: the function may need to run a few times before it works):
R2: What are the problems associated with this route from a cycling perspective? Take a look at the help page opened by entering
?route_osrm to identify the reason why the route is not particularly useful from a cycling perspective.
R3: Regenerate the route using the function
line2route(). What is the difference in the length between each route, and what other differences can you spot? Note: this exercise requires an API Key from CycleStreets.net.
R4 (bonus): what features of a routing service would be most useful for your work and why?
overline2()function and begin the script as follows, the results should look similar to the results below):
Goodman, Anna, Ilan Fridman Rojas, James Woodcock, Rachel Aldred, Nikolai Berkoff, Malcolm Morgan, Ali Abbas, and Robin Lovelace. 2019. “Scenarios of Cycling to School in England, and Associated Health and Carbon Impacts: Application of the ‘Propensity to Cycle Tool’.” Journal of Transport & Health 12 (March): 263–78. https://doi.org/10.1016/j.jth.2019.01.008.
Lovelace, Robin, Anna Goodman, Rachel Aldred, Nikolai Berkoff, Ali Abbas, and James Woodcock. 2017. “The Propensity to Cycle Tool: An Open Source Online System for Sustainable Transport Planning.” Journal of Transport and Land Use 10 (1). https://doi.org/10.5198/jtlu.2016.862.