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toronto-collisions's Introduction

Motor Vehicle Collisions Decreased in Toronto During and After the Beginning of the COVID-19 Pandemic

Overview

As one of the fastest-growing and densest Canadian cities, road and pedestrian safety are growing concerns in Toronto, especially after the COVID-19 pandemic. This paper looks at trends of motor vehicle collisions from 2017 to 2023 in Toronto neighbourhoods and wards, types of collisions, and the number of pedestrians involved. The results show that motor vehicle collisions and pedestrian involvement in them have decreased during and after the pandemic but are prevalent in the same areas from 2017 to 2023. Further investigation is needed on the demographics of Toronto areas with a high number of motor vehicle collisions.

This repository is associated with the paper, "Motor Vehicle Collisions Decreased in Toronto During and After the Beginning of the COVID-19 Pandemic".

Important Note

About unedited_collisions_data.csv: This CSV file is currently not in inputs/data due to its file size exceeding 100MB. To download unedited_collisions_data.csv, run the script 01-download_data.R located in the following path: scripts/01-download_data.R.

Statement on LLM usage: No LLMs were used for any aspect of this work.

File Structure

The repo is structured as:

  • input/data contains the data sources used in analysis including the raw data.
  • input/sketches contains the sketches made when planning out how the dataset should look and the resulting graphs.
  • outputs/data contains the cleaned dataset that was constructed.
  • outputs/paper contains the files used to generate the paper, including the Quarto document and reference bibliography file, as well as the PDF of the paper.
  • scripts contains the R scripts used to simulate, download and clean data.

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toronto-collisions's Issues

Peer Review 1

Hello Emily!
Firstly I want to comment on how interesting your topic is as well as the high quality and number of your tables and figures. The tables are simple and easy to understand with the use of the captions. One potential suggestion is to use a box plot for table 2 or in addition to table 2. This would help create a more of a visual. Figure 2 is particularly excellent as you used colors to illustrate multiple variables responsible for the number of collisions.
Another potential suggestion is in your table of contents. I suggest making results its own section, namely section 3, separate from the data section.
The results section is well written and explains what is seen in the tables and figures without going into the why - great work.
As for the discussion, it would be useful to add a short summary of the paper, explaining possible explanations for why the data looks the way it is, what you learned that can be applicable in the real world as well as areas of weakness and suggestions for future work. It looks like you weren't quite done with this section but wanted to comment on it just as a reminder :) Your appendix section is well used.
In terms of code, your references section is well executed. Your use of comments to explain the purpose of each package used is excellent and easy to follow. Your data section code appears to be straightforward. Though you do use some comments, I think it may be useful to use more in the data section particularly in lines 92 to 103, 125 to 152, 165 to 181, 190-193, 204-210 as no comments are used. Here i'd like to know exactly what you'd like to do and how you get there. I suggest more consistency in using comments so that when others are looking at your code, it is clear what your intensions are.
Overall I think this is an excellent paper and I can tell your research was through. I can also tell this subject is of interest to you as you carried that passion into your introduction and throughout the entirety of the paper. Well done!

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