Transparent Peer Review By Scholar9
Supervised Learning Model for Predicting Gentrification in U.S. Cities
Abstract
Gentrification, characterized by the influx of affluent residents and investments into historically marginalized neighborhoods that lead to the displacement of original residents, is a pressing urban issue. The findings of this research highlight the potential of predictive modeling to inform targeted interventions and support sustainable urban development. By identifying at-risk neighborhoods, policymakers can implement measures to mitigate displacement, promote affordable housing, and preserve cultural diversity. However, challenges such as data availability, model interpretability, and ethical considerations remain significant. Predictive modeling, particularly through machine learning, offers a promising approach to understanding and anticipating gentrification trends in U.S. cities. By analyzing various data points, these models can identify key indicators associated with gentrification, allowing policymakers to develop proactive strategies My study employs a supervised learning model, specifically Logistic Regression, to predict gentrification in select U.S. cities. The model achieved an accuracy rate of 72.5%, demonstrating its effectiveness in identifying areas susceptible to gentrification. Key variables influencing the model's predictions included income levels, educational attainment, housing vacancy rates, proximity to amenities, and crime rates.
Chandrasekhara (Samba) Mokkapati Reviewer
25 Sep 2024 03:18 PM
Approved
Relevance and Originality
The research addresses a significant urban issue, gentrification, which affects communities and policymakers alike. Its focus on predictive modeling to anticipate trends is particularly relevant in today’s urban studies context. To enhance originality, the study could incorporate unique variables or alternative predictive models that have not been widely applied in previous research.
Methodology
The study employs a supervised learning model, specifically Logistic Regression, to predict gentrification. While the choice of model is appropriate, the methodology section would benefit from a more detailed explanation. This should include the data sources used, the criteria for selecting neighborhoods, and how the model was trained and validated. Additionally, discussing potential limitations of Logistic Regression in this context would strengthen the methodology.
Validity & Reliability
The reported accuracy rate of 72.5% indicates a reasonable level of effectiveness in predicting gentrification. However, to improve validity, the paper should provide more context on how this accuracy compares with other models or approaches. Additionally, discussing the reliability of the data sources used and any potential biases in the dataset would be beneficial for assessing the overall robustness of the findings.
Clarity and Structure
The paper is generally well-structured, but clearer section headings (e.g., Introduction, Methodology, Results, Discussion) would enhance readability. Further, defining technical terms related to predictive modeling for a broader audience would aid comprehension. Including visual aids, such as graphs or charts, to illustrate key findings and trends would also improve clarity.
Result Analysis
The analysis focuses on key variables influencing gentrification, which is crucial for understanding the model's predictions. However, the paper could be strengthened by providing more in-depth analysis of how these variables interact and influence each other. Additionally, discussing the implications of the findings for policymakers, along with specific recommendations for intervention strategies, would enhance the practical relevance of the research.
IJ Publication Publisher
Thank You Sir
Chandrasekhara (Samba) Mokkapati Reviewer