Data science project plan

Example submission

Author

Student ID: 123456

Note: this is an example submission for illustrative purposes only. You are welcome to choose a related topic but ensure your submission is original.

See the source code at github.com/itsleeds/tds/tree/main/d2.

See the rendered PDF at gitub.com/itsleeds/tds/releases/.

See the .zip file with the files needed to reproduce this analysis at gitub.com/itsleeds/tds/releases/.

Introduction

Road traffic casualties cause untold pain and suffering, killing an estimated 1.35 million people worldwide each year and ruining the lives of countless others (World Health Organization, 2018). The UK has comparatively safe roads but road traffic casualty rates have plateaued in recent years, according to data from the Department for Transport’s 2023 report. There are many causes of traffic collisions but road traffic infrastructure can play a role, as outlined in Tait et al. (2023), which found that contraflow cycling on one-way streets can improve the cycling experience and increase participation without increasing crash rates. The topic of this project is to investigate the relationship between road traffic casualties and road infrastructure in West Yorkshire, a region that has been found in previous research to have a higher casualty rate for cyclists than other regions (Lovelace et al., 2016).

Working title

What are the links between new infrastructure and traffic collisions in West Yorkshire?

Data

I plan to use the following datasets:

  • STATS19 data from the stats19 R package, 2014-2023
    • I plan to use the crash-level data converted into spatial format with the format_sf() function
  • Traffic calming data from the osmactive package
    • This dataset from OSM contains information on speed bumps, chicanes, and other traffic calming measures

Research question

How does the presence of traffic calming measures correlate with road traffic casualties in West Yorkshire?

Initial analysis

I loaded the tidyverse and the stats19 packages (code not shown) and created a map of road traffic casualties in West Yorkshire by converting the dataset into sf format using the ggplot2 package with the folliwng commands:

class(collisions_2023)
collisions_2023_sf = stats19::format_sf(collisions_2023)
class(collisions_2023_sf)
names(collisions_2023_sf)[1:9]
head(table(collisions_2023_sf$police_force))
collisions_west_yorkshire_sf = collisions_2023_sf |>
  filter(police_force == "West Yorkshire") |>
  # Arrange in descending order of accident severity
  # so most severe accidents are plotted last:
  arrange(desc(accident_severity))
sf::write_sf(collisions_west_yorkshire_sf, "wy.gpkg")
osm_transport_network_20mph |>
  ggplot() +
  geom_sf()
ggplot() +
  geom_sf(
    data = collisions_west_yorkshire_sf,
    aes(colour = accident_severity, alpha = accident_severity)
  ) +
  scale_alpha_manual(values = c(0.8, 0.4, 0.2))

Road infrastructure data was obtained from the osmactive package.

I rendered this document to a PDF file with the following command:

quarto::quarto_render(
  "project-plan.qmd",
  output_format = "pdf",
  output_file = "project-plan.pdf"
)

I created a zip file with the files needed to reproduce this analysis with the following command:

zip(
  zipfile = "submission.zip",
  files = c("project-plan.qmd", "project-plan.pdf", "wy.gpkg")
)

Questions

  • How do I get other types of transport infrastructure, such as cycle lanes and traffic calming measures? I have read the documentation at nptscot.github.io/osmactive but I am unsure how to proceed.

References

Lovelace, R., Roberts, H., Kellar, I., 2016. Who, where, when: The demographic and geographic distribution of bicycle crashes in West Yorkshire. Transportation Research Part F: Traffic Psychology and Behaviour, Bicycling and bicycle safety 41, Part B. https://doi.org/10.1016/j.trf.2015.02.010
Tait, C., Beecham, R., Lovelace, R., Barber, S., 2023. Contraflows and cycling safety: Evidence from 22 years of data involving 508 one-way streets. Accident Analysis & Prevention 179, 106895. https://doi.org/10.1016/j.aap.2022.106895
World Health Organization, 2018. Global Status Report On Road Safety 2018. S.l.