Clearsky21z
commented
1 month ago Thank you Xinxiang for your detailed and thoughtful peer review! I appreciate your feedback and will address the suggestions you've provided to improve the paper and repository. Your insights, especially regarding the README updates and data visualization, are valuable, and I will work on refining those areas. Thanks again for taking the time to review the work!
Opening Statement Summary
This paper conducts an analysis of Toronto Island ferry ticket sales, focusing on temporal patterns and passenger flow trends using time series data. By examining sales and redemption data at 15-minute intervals, the study identifies daily and seasonal patterns in ferry usage from 2016 to 2023. The findings reveal that ferry demand peaks during weekends and summer months, with significant increases in traffic during holidays and major events. The insights gained from this analysis aim to help optimize ferry operations by adjusting schedules and staffing to better manage passenger flow, particularly during high-demand periods.
Strong Positive Points
The paper effectively uses a variety of graphs to present data. It includes a catalog, improving navigation and organization. There are numerous citations, demonstrating thorough research. The scripts shows a proper documentation for its usage. Cross-references are clearly illustrated in the text. The version control, more specifically the commit message, is informative.
Critical Improvements Needed
Add sketches to complement the data and analysis. Update the README file with necessary details. More specifically, Some checks section needs to be removed. In scripts/03-test_data.R, the simulated dataset should be read instead of the cleaned one.
Suggestions for Improvement
Document LLM usage in the location indicated by the README file. Improve the first three figures for visual clarity. Further develop the discussion section, focusing on the causes behind trends (e.g., reasons for increased absences) and the limitations of the current data. Remove any unrelated files from the repository.
Evaluation
R Citation: 1/1 LLM Statement: 0/1 Title: 2/2 Author, Date, and Repo: 2/2 Abstract: 3/4 Introduction: 4/4 Data: 8/10 Measurement: 4/4 Cross-References: 2/2 Prose: 3/6 Graphs/Tables/etc: 3/4 Referencing: 4/4 Commits: 2/2 Sketches: 0/2 Simulation: 4/4 Tests: 2/4 Reproducibility: 4/4 Code Style: 1/1 General Excellence: 2/3 Estimated Mark: 51/64
Reasoning:
While the graphs, content, and script are mostly satisfactory, several key elements are missing, such as the README file and the 03-test_data.R file updates. The paper shows good promise but requires these improvements to reach its full potential.