snehamariamthomas / UK_Police_Bias

Analyzing UK police stop/search data for ethnic biases
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Peer Review #1

Open busralb opened 5 months ago

busralb commented 5 months ago

The report thoroughly addresses the research question on UK police stop-and-search practices, providing a comprehensive and integrative analysis. It employs geographic visualizations and plots, enhancing the interpretation of the data and making complex findings more accessible. The report is well-structured, clearly articulating the correlation between police deployment and non-white population sizes, offering insightful statistical perspectives on regional policing patterns. The inclusion of public confidence data in local police across various ethnicities is creative and enriches the study. Acknowledging limitations like the study's temporal scope and dataset exclusions adds credibility.

For the code part, the hardcoding of file paths reduces the reproducibility of the analysis; providing these files in a data folder within the GitHub repository would enhance this aspect. Creating functions for common tasks such as data loading, cleaning, and specific visualization styles would make the code more efficient and reduce redundancy. On a positive note, the code is exceptionally well-documented, which significantly aids in the review process. However, some of the code chunks are lengthy, splitting them based on similar functionalities may further improve readability. Lastly, in terms of visualization, the use of pie charts to display the "Ethnic Composition of Police Officers Across Police Force Areas" is less effective, as it becomes challenging to discern differences among the minority groups due to their similar sizes in the charts. A different visualization approach might convey this information more effectively. Overall, the report presents a well-organized and insightful approach to answering the research question.

snehamariamthomas commented 5 months ago

Thank you for the comprehensive feedback. I appreciate the thorough analysis of the report's strengths and areas for improvement. I'll address the issues raised regarding code reproducibility, efficiency, and visualization in the next iteration, ensuring a more robust and user-friendly analysis. Your insights are invaluable for refining the overall quality of the study.