Cassieliu77
commented
4 weeks ago Hi Serena, thanks for your review of my repo. Based on your suggestions, I have made the following adjustments:
- As you suggested, I have talked more deeply about the connection between population density, socio-economic disparities, and crime frequency in my discussion section.
- I revise the original data visualization (facet map) since it may be hard to recognize patterns when presenting the data for the whole city.
Thanks!
Opening statement summary: I am peer-reviewing Yongqi's paper examining the relationship between population density and crime frequency in Toronto neighbourhoods from 2014 to 2023. The paper analyzes crime distribution and population, offering insights into crime patterns and the socioeconomic factors influencing them.
Strong Positive Points: The paper uses a variety of data sources and tools, including R, Tidyverse, and Geojsonsf, showing technical depth and reproducibility. You also provide clear visualizations that help communicate complex data effectively.
Critical Improvements Needed: It looks like you haven't perfected the introduction section, go for complete! Figure 5 in 3.2 shows the distribution of population in Toronto and this section could be enriched. In addition to showing that it helps us determine whether these crime-prone areas coincide with neighbourhoods with high population densities, the connection between population density, socio-economic disparities and crime rates could be further explained.
Suggestions for Improvement: Improving the discussion of crime types could The discussion of crime categories (e.g., assault, auto theft) should delve into why certain crime rates have increased after 2020 (e.g., the impact of COVID-19). This could add richness to the interpretation of the data. Refine the conclusions. Currently, the conclusion briefly summarises the findings of the study but does not leave the reader with a deeper insight.
Please Consider Adding/Changing/Removing: I think you might consider revising Figure 6, and ideally analyzing the distribution of crime by type in high-density neighbourhoods at a more granular level, rather than presenting the data as a whole. You could include a short section in the methodology describing the steps for cleaning and analyzing the data would be helpful to the study.
Evaluation: R is cited correctly: 1/1 LLM usage is documented: 1/1 Title: The title is informative and relevant, accurately reflecting the content of the paper. 2/2 Author, date, and repo: the author’s information and GitHub repo are included clearly in the submission. 2/2 Abstract: Great. The abstract introduces the study well but could improve by emphasizing the broader implications of the findings. 4/4 Introduction: Not complete. The introduction could be more detailed and complete, especially the section that is still marked as needing more content. 1/4 Data: Great. The data is explained thoroughly, with raw data, variables, and visualizations well-represented. Minor improvements could be made in connecting the data to the analysis steps. 7/10 Measurement: Good. A thorough measurement section is present, but it could better tie into the data interpretation for stronger clarity. 3/4 Cross-references: Yes, all figures, tables, and equations are properly cross-referenced. 2/2 Prose: Good. The prose is generally coherent, though some repetitive phrasing and minor grammatical issues need improvement. 4/6 Graphs/tables/etc.: Exceptional. Graphs and tables are clear and appropriately formatted. 4/4 Referencing: Perfect. All references are appropriately formatted with in-text citations where necessary. 4/4 Commits: Commit correctly.2/2 Sketches: Sketches are included in the repo. 2/2 Simulation: Variables are simulated in the repo. 4/4 Tests: Perfect. Tests are appropriately used and documented. 3/4 Reproducibility: Fine. The project is generally reproducible, but there are minor gaps in documentation clarity. 3/4 Code style: Code style follows proper conventions.1/1 General excellence: The paper excels in various areas like clear visualizations and comprehensive data analysis, deserving this recognition. 2/3 Total Estimated Mark: 52/64 Based on the rubric and the above feedback, I would currently estimate a mark of 52/64. With improvements, I think your paper has the potential to reach a higher score.