_13-ex.Rmd

```{r 13-ex-e0, message=FALSE}
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?

```{r 13-e1, eval=FALSE, echo=FALSE}
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?

```{r 13-transport-29, eval=FALSE, echo=FALSE}
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`?

```{r 13-transport-30, echo=FALSE, eval=FALSE}
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?

```{r}
# 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)).