The paper provides a detailed statistical analysis of Toronto's police budget and crime counts between 2020 and 2023, aiming to evaluate the effectiveness of the police budget in reducing crime. The study utilizes data sourced from Open Data Toronto and analyzes trends using R, demonstrating the author's technical proficiency. However, some areas, such as clarity of presentation and methodology, could benefit from improvement.
Strong Positive Points:
Use of Credible Data Source: The paper uses official datasets from Open Data Toronto, ensuring a high level of credibility. The author also acknowledges alternative sources, demonstrating careful selection and evaluation of available data.
Clear Data Presentation: The tables summarizing the police budget and crime statistics (Table 1 and Table 2) are well-organized and presented clearly. This makes it easier for the reader to quickly understand the key findings of the analysis.
Critical Improvements Needed:
There are several grammatical errors and typos throughout the paper (e.g., "recently years," "fnding," "fulflling"). These issues can make it harder for readers to engage with the content and should be addressed with thorough proofreading.
The findings section is brief and lacks depth. It would benefit from a more in-depth interpretation of the results. Specifically, the paper should explore why the police budget appeared to have no effect on crime rates and whether external factors or dataset limitations might explain this observation.
Suggestions for Improvement:
Improve Grammar and Sentence Structure: A thorough proofreading will enhance the paper’s readability and make the arguments clearer.
Clarify Statistical Methods: The statistical methods used should be explained in more detail, especially for non-technical readers. This will help them follow the analysis more easily and understand the results.
Estimated Mark: 6/10Reason: This is a solid paper with strong technical execution, but it needs more work in the areas of interpretation, literature context, and methodological robustness. With improvements in these areas, it could make a more significant contribution to understanding the relationship between police budgets and crime rates.
Peer Review
Opening Statement Summary:
The paper provides a detailed statistical analysis of Toronto's police budget and crime counts between 2020 and 2023, aiming to evaluate the effectiveness of the police budget in reducing crime. The study utilizes data sourced from Open Data Toronto and analyzes trends using R, demonstrating the author's technical proficiency. However, some areas, such as clarity of presentation and methodology, could benefit from improvement.
Strong Positive Points:
Use of Credible Data Source: The paper uses official datasets from Open Data Toronto, ensuring a high level of credibility. The author also acknowledges alternative sources, demonstrating careful selection and evaluation of available data.
Clear Data Presentation: The tables summarizing the police budget and crime statistics (Table 1 and Table 2) are well-organized and presented clearly. This makes it easier for the reader to quickly understand the key findings of the analysis.
Critical Improvements Needed:
There are several grammatical errors and typos throughout the paper (e.g., "recently years," "fnding," "fulflling"). These issues can make it harder for readers to engage with the content and should be addressed with thorough proofreading.
The findings section is brief and lacks depth. It would benefit from a more in-depth interpretation of the results. Specifically, the paper should explore why the police budget appeared to have no effect on crime rates and whether external factors or dataset limitations might explain this observation.
Suggestions for Improvement:
Estimated Mark: 6/10 Reason: This is a solid paper with strong technical execution, but it needs more work in the areas of interpretation, literature context, and methodological robustness. With improvements in these areas, it could make a more significant contribution to understanding the relationship between police budgets and crime rates.