This project, "Toronto Tickets Issued," utilizes a dataset from the Toronto Open Data catalogue to perform an analysis on tickets issued by Toronto Police Services. The project focuses on trends, correlations, and insights into the distribution of tickets through the city. The analysis uses R and a GitHub repository containing the necessary code and data.
Strong positive points
Visualizations are generally easily understandable at first glance
The introduction provides a holistic view on what the analysis is about
Critical improvements needed
Data Visualizations: Some figures are not properly displayed, making them ambiguous as to the information they convey. Some figures are also not displayed at all, showing code instead. Cross-referencing is not evident as well.
Discussion: This portion needs more in-depth information. It is best to relate your results and findings with real-world information and the ramifications of such data.
Prose: Some areas of the paper contain incomplete sentences and typos that diminish the academic tone necessitated by such an analysis.
Citations and References: In-text citations are improperly formatted, and references are not present.
Suggestions for improvement:
Ensure that all tables, figures, and other visualization are properly displayed on the paper
Ensure that academic tone is maintained throughout the paper and all typos are fixed.
Please consider adding/changing/removing:
Change some visualizations to display properly on paper, and avoid ambiguity
Add more discussion points talking about your data: you might consider questions like "What real-world phenomena could be consistent with or explained by my data?" and "What limitations might this analysis have?"
Add cross-referencing to all tables and figures.
Add appropriate references and the necessary citations
Evaluation
Based on the evaluation criteria provided, the project provides a solid start with a good structure, but there are many areas of the paper that need significant improvement, especially the data and discussion sections.
Estimated mark - 36/65
R is appropriately cited: 0
LLM usage is documented: 0
Title: 1
Author, date, and repo: 2
Abstract: 3
Introduction: 2
Data: 4
Measurement: 4
Prose: 2
Cross-references: 0
Captions: 2
Graphs/tables/etc: 1
Referencing: 0
Commits: 2
Sketches: 0
Simulation: 4
Tests: 4
Reproducibility: 4
Code style: 1
General excellence: 0
Reason:
The project presents interesting data but has several issues with the data, discussion, and reference sections. Addressing these areas, along with considering the above suggestions and changes would significantly improve the overall analysis.
This project, "Toronto Tickets Issued," utilizes a dataset from the Toronto Open Data catalogue to perform an analysis on tickets issued by Toronto Police Services. The project focuses on trends, correlations, and insights into the distribution of tickets through the city. The analysis uses R and a GitHub repository containing the necessary code and data.
Strong positive points
Critical improvements needed
Data Visualizations: Some figures are not properly displayed, making them ambiguous as to the information they convey. Some figures are also not displayed at all, showing code instead. Cross-referencing is not evident as well. Discussion: This portion needs more in-depth information. It is best to relate your results and findings with real-world information and the ramifications of such data. Prose: Some areas of the paper contain incomplete sentences and typos that diminish the academic tone necessitated by such an analysis. Citations and References: In-text citations are improperly formatted, and references are not present.
Suggestions for improvement:
Please consider adding/changing/removing:
Evaluation Based on the evaluation criteria provided, the project provides a solid start with a good structure, but there are many areas of the paper that need significant improvement, especially the data and discussion sections.
Estimated mark - 36/65
Reason: The project presents interesting data but has several issues with the data, discussion, and reference sections. Addressing these areas, along with considering the above suggestions and changes would significantly improve the overall analysis.