Prerequisites

The following packages must be installed and loaded to run the code in this vignette.

library(pct)
library(dplyr)
library(sf)
library(tmap)

Picking a study area

region_name = "wales"
zones_all = get_pct_zones(region_name)

unique(zones_all$lad_name)
#>  [1] "Cardiff"               "Wrexham"               "Conwy"                
#>  [4] "Newport"               "Caerphilly"            "Rhondda Cynon Taf"    
#>  [7] "Carmarthenshire"       "Swansea"               "The Vale of Glamorgan"
#> [10] "Bridgend"              "Neath Port Talbot"     "Pembrokeshire"        
#> [13] "Denbighshire"          "Flintshire"            "Monmouthshire"        
#> [16] "Powys"                 "Torfaen"               "Blaenau Gwent"        
#> [19] "Ceredigion"            "Gwynedd"               "Merthyr Tydfil"       
#> [22] "Isle of Anglesey"

la_name = "Cardiff"

Plotting Current Cycle Uptake

Now we have chosen our region and city, we can easily plot the total number of cyclists based on the 2011 census.

zones = zones_all %>% 
  filter(lad_name == la_name)
plot(zones["bicycle"])

TaDa! The plot shows us that cycling is most prominent within the centre of Cardiff. While this is a first step in understanding where new cycle infrastructure is needed, it doesn’t outline the road network where new demand is likely to occur.

Plotting New Demand Based On PCT Models

PCT provides an easy way to get road network data for any pct_region. The road network data can then be filtered to the zones within our local authority.

rnet_all = pct::get_pct_rnet(region_name)
rnet = rnet_all[zones, ]

With our road network data fitted to our study area, we are now ready to plot where investment should be prioritised based on a demand model. PCT is built with numerous demand models based on varying situations (e.g. govtarget_slc,gendereq_slc and ebike_slc. In this example, we use the go dutch demand model; where demand is modelled on a cycle uptake equivalent to that in the Netherlands.

plot(zones$geometry)
plot(rnet["dutch_slc"], add = TRUE)

Voila! The blue lines represent the road network with colour proportional to estimated potential based on the go dutch model. This plot helps us understand where to prioritise cycle infrastructure and what a new demand may look like.

Comparing Two Cities

Now we have seen the ease and versatility the PCT package provides, we can now use the method to explore more cities. Take Devon for example, the region has two major cities Exeter and Plymouth. Both cities obtain major universities, growing industries and have a relatively low cycle uptake. The latter should change, and the PCT package can provide the tools to help that.

We can now wrap the method we used for Cardiff into a function in order to compare the differences between the two cities.

pct_zones_rnet = function(la_name, region_name = "devon") {
  zones_all = pct::get_pct_zones(region_name)
  zones = zones_all %>% 
  filter(lad_name == la_name)
  plot(zones["bicycle"])
  rnet_all = pct::get_pct_rnet(region_name)
  rnet = rnet_all[zones, ]
  plot(zones$geometry)
  plot(rnet["dutch_slc"], add = TRUE)
  list(zones = zones, rnet = rnet)
}

plymouth_results = pct_zones_rnet(la_name = "Plymouth")

exeter_results = pct_zones_rnet(la_name = "Exeter")

Nice! The plots indicate a spatial spread for cycling in Plymouth, with investment necessary not only in the centre of the city. While for Exeter the distribution remains monocentric, with investment necessary mostly in the centre.

We can create side-by-side interactive maps of the route network potential for of same cities for a more detailed comparison as follow:

tmap_mode("view")
#> tmap mode set to interactive viewing
b = c(0, 100, 200, 500, 1000)
m1 = tm_shape(plymouth_results$rnet) +
  tm_lines("dutch_slc", breaks = b, palette = "viridis", lwd = 2) +
  tm_scale_bar()
m2 = tm_shape(exeter_results$rnet) +
  tm_lines("dutch_slc", breaks = b, palette = "viridis", lwd = 2) +
  tm_scale_bar()
tmap_arrange(m1, m2, ncol = 2)
#> Warning: Values have found that are higher than the highest break

#> Warning: Values have found that are higher than the highest break

Conclusions and next steps

As demonstrated, the PCT package provides an easy way to understand current cycle statistics and potential cycle uptake in any UK city. The methods used in this article can be used for planning future cycle infrastructure and can be expanded on using the other methods part of the PCT package. If this article is of use/interest, why not try for your local region showing the potential based on different models. You can also try the following resources (in R and online):

• Have a play with the PCT web application
• For more of a deep dive into the methods, see the pct_training vignette
• See the pct-international to see how to apply the methods internationally

We’re interested to know how you’ve used the methods/data so please get in touch on (social media) and (Github) letting us know how get on. Any comments/contributions to this analysis: welcome.