dwz92 / crime-in-toronto

Crime-In-Toronto

Overview

This repo contains the full analysis of the 'Neighbourhood Crime Rates' dataset from opendatatoronto. The focus of this paper is to discover and analyze the relationship between city areas and crime rates before, during, and after the covid-19 period.

The repo is organized as follow:

/scripts/ contains .R files that simulates, downloads and cleans the dataset.

/outputs/paper/ contains the .qmd file that generates the report, references, and pdf version of the paper.

/inputs/ contains the .csv files that stores the retrieved data from opendatatoronto and cleaned data.

Usage of LLM

Direct LLM usage is not included within the report as it was mainly used for instructions on .R and .bib functions notations and usages.

crime-in-toronto's Issues

Peer Review on Crime In Toronto

GitHub Repository

Repository Name

• Feedback: The current repository name appears to be somewhat ambiguous. A more descriptive name would greatly enhance the clarity and immediate understanding of the repository's focus and content.

README.md

• Missing:
  • Overview Section: Please include a section describing the sources of data and a summary of the key findings from your analysis.
  • Repository Structure: Please provide a detailed explanation of the repository structure, including directories and main files, for easy navigation.
  • LLM Usage: If any Large Language Models (LLMs) like ChatGPT were used, explicitly state this, including the version and purpose of their usage.

LICENSE

• Feedback: To maintain current and clear copyright information, update the copyright statement to "Copyright (c) 2024 Emma Teng." This will help in protecting your intellectual property and clarifying usage rights.

Use of Large Language Model (LLM)

• Missing:
  • Version Documentation: Please clearly document the specific version of ChatGPT used in the usage.txt file. This helps in replicating or understanding the context of the analysis.
  • Chat Inclusion: Please include the entire conversation or interaction with the LLM. This provides transparency and allows for a deeper understanding of how the LLM contributed to your research.

Sketches

• Missing: Please include sketches or initial conceptual diagrams of your dataset can significantly aid in visualizing and understanding the data structure and relationships. These sketches can be preliminary drafts or more polished diagrams.

Simulation

set.seed(853)

simulated_crime_data <-
  tibble(
    hoods = rep(1:158, each=9),
    crime_type = rep(x=1:9, times=158),
    crimes_committed = runif(n = 158 * 9, min = 0, max = 1000) |> floor()
  )

• Feedback: The simulation aspect of your research is commendably executed. It reflects a solid understanding of the dataset's characteristics post-manipulation, indicating thorough preparatory work.

Data Cleaning

• Feedback: The process of data acquisition and subsequent cleaning is well handled and correctly documented. This ensures the reliability and validity of the dataset used in your research.

Testing

• Missing: Please introduce test cases to assess the integrity and robustness of your dataset. This could include sanity checks, data validation tests, or specific scenario tests to ensure data consistency.

Paper Content

Title

• Feedback: The title effectively encapsulates the main theme of the paper, indicating a focused analysis of crime trends in Toronto. It is informative and provides a clear indication of the study's scope.

Abstract

• Feedback: The abstract is well-composed, offering a concise yet comprehensive overview of the study's aims, methods, and findings. It is accessible to a non-specialist audience, making the research more inclusive.

Introduction

• Suggestions:
  • Context Establishment: The introduction should set the stage for your research by providing relevant background information and context.
  • Significance and Research Questions: Highlight the importance of your study and clearly outline the research questions or hypotheses you intend to address.
  • Methodology and Structure Explanation (Missing): Describe the methodologies employed in your research and provide an overview of the paper's structure, guiding the reader through your research journey.

Data and Measurement

• Feedback: The selection and use of Toronto Police Services' crime data are appropriate for the study. It ensures that the data collection methods are transparent and any limitations or biases in the data are openly acknowledged.
• Missing: However, please thoroughly explain the methods used for data measurement and analysis. This includes detailing the statistical methods, data processing steps, and any specific criteria used for data inclusion or exclusion.

Discussion

• Missing: It's necessary to add a discussion section to demonstrate your comprehension and insights. This section offers an opportunity to showcase your knowledge and what you have gleaned from the research process.

Numbering and Cross-references

• Feedback: Effective use of numbering and cross-references for sections, figures, tables, and equations enhances the paper's readability and navigability.

Reference List

• Feedback: The reference list should be comprehensive and formatted according to a standard citation style. Ensure that all sources referenced within the paper are included in this list and that there is a one-to-one correspondence between in-text citations and the reference list.

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Note: The review is based on the provided rubric and accessible content. As the PDF of the paper is not available, it's not possible for me to provide specific feedback on aspects like writing style, graphical presentations, and other similar details. If you upload the final PDF version of your paper later and need my suggestions, please let me know (e.g. comment on this issue). I will then be able to review it and offer any additional recommendations or insights.

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