Note: this vignette is not evaluated to reduce package build times. See https://rpubs.com/RobinLovelace/749112 for the results with the code running.
What modal shift to cycling do you think we could achieve in UK cities in an ideal scenario? Answers to that question are vital to support high level ambition for active transport and wider sustainability policies in settlements worldwide. In UK cities such as Leeds (where the motivation for this article originated, via Twitter) there is already a wealth of freely available data on cycling potential, based on the Propensity to Cycle Tool (PCT) project, of which this package is a part. This article will briefly explain how to get cycling potential data for any city in England and Wales, based on a case study of Leeds.
The first stage in many projects involving geographic data is to define the region of interest. In our case we will use the boundary of the Leeds local authority as the basis of analysis. The PCT is based on ‘PCT regions’ that we can visualise as follows:
library(pct) library(sf) library(dplyr) library(tmap) tm_shape(pct_regions) + tm_polygons() + tm_text("region_name", size = 0.6)
We are interested in Leeds in the
pct package we can get zone data from the PCT as
follows (note: this would work by setting
region_of_interest to any of the regions shown in the map
= "west-yorkshire" region_of_interest = get_pct_zones(region = region_of_interest) zones_region # zones_region = get_pct_zones(region = region_of_interest, geography = "lsoa") # for smaller zones names(zones_region) plot(zones_region["bicycle"])
This shows the number of people who say they cycled to work in the 2011 Census. This may have changed slightly as cycling becomes more popular in some places, but is a good proxy for current cycling levels. But there are two big problems with this: the map is of West Yorkshire not Leeds, and it’s showing current cycling rates, not future potential.
To generalise the analysis outlined above, we can download national data from the PCT project as follows:
A summary of cycling potential nationwide can then be calculated as follows:
= zones_national %>% national_commute_totals st_drop_geometry() %>% select(all, census_2011 = bicycle, govtarget_slc, dutch_slc) %>% summarise_all(.funs = ~sum(.)) = national_commute_totals / national_commute_totals$all * 100national_commute_percentages
We can calculate the mode share of cycling under these same scenarios for any area (e.g. the boundary of Greater London) or for a list of named local authorities, as follows:
= read.csv(stringsAsFactors = FALSE, text = "area r Greater London Greater Manchester Birmingham Leeds Glasgow Liverpool Newcastle Bristol Cardiff Belfast Southampton Sheffield ") = pct_regions_lookup$lad16nm[pct_regions_lookup$lad16nm %in% r$area] matching_las = c("london", "greater-manchester") matching_regions = pct_regions_lookup %>% pct_lookup rename(lad_name = lad16nm) = inner_join(zones_national, pct_lookup) zones_national = zones_national %>% zones_national mutate(area = case_when( == "london" ~ "Greater London", region_name == "greater-manchester" ~ "Greater Manchester", region_name %in% matching_las ~ lad_name, lad_name TRUE ~ "Other" ))table(zones_national$area) = zones_national %>% zones_aggregated ::st_drop_geometry() %>% sfgroup_by(area) %>% summarise( Commuters = sum(all, na.rm = TRUE), Bicycle_census = sum(bicycle), Bicycle_govtarget = sum(govtarget_slc), Bicycle_godutch = sum(dutch_slc) ) # plot(zones_aggregated["Commuters"], border = NA) %>% zones_aggregated inner_join(r, .) %>% ::kable(digits = 0) knitr
Next we can calculate the mode splits as follows:
= zones_aggregated %>% zones_aggregated_percents mutate_at(vars(-Commuters, -area), funs(./Commuters * 100)) names(zones_aggregated_percents)[3:5] = paste0(names(zones_aggregated_percents)[3:5], "_percent") %>% zones_aggregated_percents inner_join(r, .) %>% ::kable(digits = 1) knitr
The results above show that there is huge potential for cycling grow across England and Wales. Around 16% of commuters in Leeds could cycle to work if we ‘Go Dutch’ (25% if we Go Dutch and see high uptake of electric bikes) for the single mode journey to work data under consideration. However, there are some wider considerations that are not captured in the numbers (see the PCT paper for details and other considerations):
Other considerations that are often mentioned are hills and people who are unable to cycle. In fact the PCT accounts for hills and, as the example of Bristol shows, hilly cities can see rapid increases in cycling. Regarding ability to cycle, it is a more inclusive mode than driving, with 8 to 80 year-olds being able to cycle, and many more people being able to afford a bike than a car (which, thanks to the expense of buying, running and insuring the vehicle, is largely the mode of choice of the wealthy). The PCT does not account for the fact that some trips to work are also ‘caregiving’ trips, but with electric cargo bikes and multi-person cycles cycling-focused cities can, to a greater extent than car-dominated transport systems, deliver for all.
To anyone interested in cycling in your city: try re-running the reproducible code above for your PCT region and local authority. Let us know on social media and GitHub how you get on. Any comments/contributions to this analysis: welcome.