Peer review by Ruizi Liu

[RIRI0527]

Summary

The paper investigates funding patterns in Toronto's PollinateTO initiative, focusing on the relationship between garden characteristics and funding allocations. By employing a Bayesian logistic regression model, the study identifies trends and patterns in urban greening efforts. The findings have significant implications for biodiversity, community equity, and resource optimization.

Strengths:

• Topic Relevance: The project aligns with current ecological and urban planning challenges, making it highly relevant.
• Use of Bayesian Models: The choice of Bayesian logistic regression is appropriate for understanding funding likelihoods and provides valuable uncertainty quantification.
• Data Source: The use of the publicly available PollinateTO Primary Project Garden Locations dataset ensures transparency and reproducibility.

Critical Improvements Needed:

• Include detailed justifications for modeling choices, specifically the Bayesian framework, and consider alternative methods.
• Ensure cross-references in the figures and text are fully functional.
• Enhance captions to make figures interpretable independently.

Evaluation

Core Requirements

1. R/Python Cited: 1/1

   • R is properly cited in both the text and references.
   • Excellent use of rstanarm and tidyverse packages.

2. Data Cited: 1/1

   • The dataset is correctly cited as sourced from the Toronto Open Data Portal.
   • The data's accessibility ensures transparency.

3. Class Paper: 1/1

   • No signs indicate this is a class project.

4. LLM Documentation: 1/1

   • The README includes a clear section documenting the use of ChatGPT.

Paper Sections

1. Title: 2/2

   • Informative and relevant, clearly outlining the study's focus.

2. Author, Date, and Repo: 2/2

   • Author details, submission date, and GitHub repository link are included.

3. Abstract: 3/4

   • The abstract summarizes the study but lacks explicit details on key findings.
   • Suggestion: Include specific results for a stronger impact.

4. Introduction: 4/4

   • Provides context, highlights the study’s significance, and outlines the structure.

5. Estimand: 1/1

   • Clearly stated as the relationship between garden characteristics and funding.

6. Data: 8/10

   • Data features and variables are well-described, but data cleaning processes need more detail.
   • Suggestion: Discuss challenges and transformations applied during preprocessing.

7. Measurement: 4/4

   • Measurements and data attributes are well-justified.

8. Model: 6/10

   • The Bayesian logistic regression is appropriate, but alternative models are not explored.
   • Suggestions:
     • Discuss why specific variables were included.
     • Add limitations of the model.
     • Mention the software used.

9. Results: 8/10

   • Well-presented with meaningful visualizations.
   • Suggestion: Expand figure captions and ensure all tables use consistent formatting.

10. Discussion: 8/10

    • Connects findings to broader implications but could benefit from additional citations.
    • Suggestion: Highlight actionable insights for policymakers more explicitly.

11. Prose: 5/6

    • The writing is clear and professional, with minor typographical errors.

12. Cross-references: 1/1

    • Cross-references are present but need fixing for some figures.

13. Captions: 1/2

    • Captions lack detail and clarity.
    • Suggestion: Ensure captions explain figures without requiring additional context.

14. Graphs/Tables: 4/4

    • Graphs are well-designed and informative.

15. Surveys, Sampling, and Observational Data Appendix: 0/10

    • Not included.
    • Suggestion: Add an idealized methodology for community surveys.

16. Referencing: 4/4

    • All references are correctly formatted.

17. Commits: 2/2

    • Multiple meaningful commits with clear messages.

18. Sketches: 2/2

    • Included and relevant.

19. Simulation: 3/4

    • Interaction effects are not fully explored in the simulation.

20. Tests: 2/4

    • Limited test coverage; consider using testthat.

21. Parquet: 0/1

    • Data is not saved as Parquet.
    • Suggestion: Use the arrow package for efficiency.

22. Reproducible Workflow: 4/4

    • Workflow is well-organized and documented.

23. Enhancements: 4/4

    • Includes a datasheet and model card.

24. Miscellaneous: 3/3

    • Repository is clean and well-maintained.

Estimated Mark: 84/112

Suggestions for Improvement

1. Model Justification:

   • Discuss alternative models and justify the Bayesian approach in greater depth.
   • Include situations where the model may not apply.

2. Appendix:

   • Add an idealized survey design to explore potential enhancements.

3. Captions:

   • Improve figure and table captions for standalone clarity.

4. Use Parquet Format:

   • Save cleaned data using the arrow package for better compatibility and performance.

Overall Comments

This is a strong and well-structured submission with a clear research focus. Minor refinements in methodology, results presentation, and appendix content could elevate the paper further. Great effort!

[alizamithwani]

Thank you for your feedback!