In Robinlovelace/geocompr: Geocomputation with R

library(sf)
library(spDataLarge)

E1. In much of the analysis presented in the chapter, we focused on active modes, but what about driving trips?

• What proportion of trips in the desire_lines object are made by driving?
• What proportion of desire_lines have a straight line length of 5 km or more in distance?
• What proportion of trips in desire lines that are longer than 5 km in length are made by driving?
• Plot the desire lines that are both less than 5 km in length and along which more than 50% of trips are made by car.
• What do you notice about the location of these car-dependent yet short desire lines?

sum(desire_lines$car_driver) / sum(desire_lines$all)
# 57%
desire_lines_5km_plus = desire_lines |> 
  filter(distance_km > 5)
# Just over are half ar 5km+, 54%:
nrow(desire_lines_5km_plus) / nrow(desire_lines)
# 71 of 5km+ trips are made by car
sum(desire_lines_5km_plus$car_driver) / sum(desire_lines_5km_plus$all)

desire_lines_driving = desire_lines |> 
  mutate(`Proportion driving` = car_driver / all) |> 
  filter(`Proportion driving` > 0.5)
nrow(desire_lines_5km_plus_driving) / nrow(desire_lines)

desire_lines_5km_less_50_pct_driving = desire_lines |>
  filter(distance_km <= 5) |> 
  mutate(`Proportion driving` = car_driver / all) |> 
  filter(`Proportion driving` > 0.5)
desire_lines_5km_less_50_pct_driving |> 
  tm_shape() +
  tm_lines("Proportion driving")

E2. What additional length of cycleways would be built if all the sections beyond 100 m from existing cycleways in Figure 13.8, were constructed?

sum(st_length(route_network_no_infra))
# 104193.6 [m]
# Just over 100 km

E3. What proportion of trips represented in the desire_lines are accounted for in the routes_short_scenario object?

• Bonus: what proportion of all trips happen on desire lines that cross routes_short_scenario?

sum(routes_short_scenario$all) / sum(desire_lines$all) # 13%
d_intersect = desire_lines[routes_short_scenario, , op = st_crosses]
sum(d_intersect$all) / sum(desire_lines$all) # 88%

E4. The analysis presented in this chapter is designed for teaching how geocomputation methods can be applied to transport research. If you were doing this for real, in government or for a transport consultancy, what top 3 things would you do differently?

# Higher level of geographic resolution.
# Use cycle-specific routing services.
# Identify key walking routes.
# Include a higher proportion of trips in the analysis

E5. Clearly, the routes identified in Figure 13.8 only provide part of the picture. How would you extend the analysis?

E6. Imagine that you want to extend the scenario by creating key areas (not routes) for investment in place-based cycling policies such as car-free zones, cycle parking points and reduced car parking strategy. How could raster\index{raster} datasets assist with this work?

• Bonus: develop a raster layer that divides the Bristol region into 100 cells (10 x 10) and estimate the average speed limit of roads in each, from the bristol_ways dataset (see Chapter \@ref(location)).

Robinlovelace/geocompr documentation built on June 14, 2025, 1:21 p.m.