After reviewing your paper, I believe you did a great job in several areas. Here are some suggestions that I hope will help in refining your final version:
Strong Positive Points:
The study makes excellent use of service request data to track trends in lead contamination across different districts in Toronto.
The inclusion of time-series analysis and district-level variations adds depth to your study, providing valuable insights into how lead concentrations have changed over time.
Your use of R for data processing and visualization is well-executed, and the references to your code enhance the reproducibility of your analysis.
Critical Improvements Needed:
Missing Axis Labels:
Some graphs, such as Figure 1, are missing axis labels, making it unclear what the x- and y-axes represent. This affects the readability and comprehension of the visualizations. Please ensure that all graphs have clearly labeled axes for clarity.
Plot References Not Working:
Some plot references in the text are not clickable, such as the mentions of Figure 1 and Figure 5. This makes it harder for readers to follow the analysis. Ensure that all plot references are properly linked within the document for easier navigation.
Diversity in Visualizations:
The paper relies heavily on line and bar graphs. Incorporating more diverse visualizations, such as heatmaps or scatter plots, could offer deeper insights. For instance, a scatter plot could better highlight the relationship between district population density and lead concentration compared to the current bar graphs.
Evaluation: Your paper provides a solid foundation and is well-organized. However, the analysis would benefit from a greater variety of graphs, clickable plot references, more attention to detail in visualizations (especially axis labeling), and a deeper discussion of external factors and data limitations.
Estimated Mark: 80 out of 100.
Reason: The paper is well-executed, but the missing axis labels, non-clickable plot references, limited variety in visualizations, and lack of a detailed discussion on external factors and data limitations affect its clarity and depth. Addressing these issues would significantly improve the paper's impact and value for policymakers and other readers.
aj3616
commented
1 week ago
Hi Amy,
After reviewing your paper, I believe you did a great job in several areas. Here are some suggestions that I hope will help in refining your final version:
Strong Positive Points:
The study makes excellent use of service request data to track trends in lead contamination across different districts in Toronto. The inclusion of time-series analysis and district-level variations adds depth to your study, providing valuable insights into how lead concentrations have changed over time. Your use of R for data processing and visualization is well-executed, and the references to your code enhance the reproducibility of your analysis.
Critical Improvements Needed: Missing Axis Labels: Some graphs, such as Figure 1, are missing axis labels, making it unclear what the x- and y-axes represent. This affects the readability and comprehension of the visualizations. Please ensure that all graphs have clearly labeled axes for clarity. Plot References Not Working: Some plot references in the text are not clickable, such as the mentions of Figure 1 and Figure 5. This makes it harder for readers to follow the analysis. Ensure that all plot references are properly linked within the document for easier navigation. Diversity in Visualizations: The paper relies heavily on line and bar graphs. Incorporating more diverse visualizations, such as heatmaps or scatter plots, could offer deeper insights. For instance, a scatter plot could better highlight the relationship between district population density and lead concentration compared to the current bar graphs. Evaluation: Your paper provides a solid foundation and is well-organized. However, the analysis would benefit from a greater variety of graphs, clickable plot references, more attention to detail in visualizations (especially axis labeling), and a deeper discussion of external factors and data limitations.
Estimated Mark: 80 out of 100.
Reason: The paper is well-executed, but the missing axis labels, non-clickable plot references, limited variety in visualizations, and lack of a detailed discussion on external factors and data limitations affect its clarity and depth. Addressing these issues would significantly improve the paper's impact and value for policymakers and other readers.