Bonus Content

1 Thanks for attending!

We hope you enjoyed the course. You can find all the materials and updates on the course website.

Here is a quick visualisation of the data we explored:

1.1 Places

library(sf)
library(mapview)
# Read places data
places = read_sf("https://github.com/itsleeds/ai4transport/raw/main/data/places.geojson")
# Visualise places
mapview(places, zcol = "name")

1.2 OD Data

library(tidyverse)
library(sf)
Download and prepare OD data for TfSE 
library(od)

od = read_csv("od_tfse_2021.csv")
zones = read_sf("tfse_msoas.gpkg")

# Check 1st column of zones matches 'o' and 'd' in od
summary(zones[[1]] %in% c(od$o, od$d))
od_sf = od_to_sf(od, zones)
od_sf = od_sf |>
  mutate(`% Car` = Car / (Car + Bicycle + Walking + Bus + Train))
od_sf$length = sf::st_length(od_sf) |> as.numeric()
sf::write_sf(od_sf, "od_tfse_2021_sf.gpkg", delete_dsn = TRUE)
od_sf = read_sf("od_tfse_2021_sf.gpkg")
od_sf |>
  filter(Car > 10 & length > 10 * 1000) |>
  arrange(desc(`% Car`)) |>
  ggplot() +
  geom_sf(aes(colour = `% Car`)) +
  scale_colour_viridis_c(direction = -1) +
  theme_minimal()

OD commuting flows in TfSE, coloured by % car use

2 Further Learning

We recommend taking Anthropic’s full AI Fluency course to deepen your understanding of AI.

If you want more experience with data science:

3 Get in touch

If you are interested in learning more or collaborating, please get in touch with the team. You can find our email addresses on our institutional profiles:

4 Data Access

You can find details on how to access the data used in this course on the Get Data page.

Reuse