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.
Aravind Ayyagari Reviewer
25 Sep 2024 02:46 PM
Approved
Relevance and Originality
The research addresses the important urban issue of gentrification, which has significant implications for community dynamics and policy-making. By focusing on predictive modeling as a tool for intervention, the study presents an original approach to understanding gentrification trends. The emphasis on identifying at-risk neighborhoods is particularly relevant for policymakers looking to balance development with community preservation.
Methodology
The study utilizes a supervised learning model, specifically Logistic Regression, to predict gentrification. However, the methodology would benefit from more detailed information on data collection and preprocessing. Clarifying how the data points were selected and how the model was trained and validated would enhance the methodological rigor. Additionally, discussing potential limitations of using Logistic Regression in this context would be valuable.
Validity & Reliability
The reported accuracy rate of 72.5% indicates a reasonable level of effectiveness in the model's predictions. To strengthen the validity and reliability of the findings, the study should include additional performance metrics, such as precision, recall, and F1 score. Discussing potential biases in the data and the implications of those biases on the model's predictions would enhance credibility.
Clarity and Structure
The writing clearly presents the main ideas, but the overall structure could be improved. Clearly defined sections—such as introduction, methodology, results, and discussion—would aid in readability. Including summaries of key findings at the end of each section would reinforce the significance of the research and help guide the reader.
Result Analysis
While the study identifies key variables influencing gentrification predictions, it lacks detailed analysis of how these variables interact with one another and their collective impact on gentrification trends. Including visualizations, such as charts or graphs, to represent the data and model outputs would enrich the result analysis. Furthermore, discussing the broader implications of the findings for urban policy and planning, as well as potential future research directions, would add depth to the study.
IJ Publication Publisher
Thank You Sir
Aravind Ayyagari Reviewer