Urban Planning Sustainability Index Analysis: High-Level Documentation
This document provides a summary of the Urban Planning Sustainability Analysis notebook, designed for submission in a data science competition.
1. Notebook Purpose and Goal
The primary objective of this notebook is to move beyond simple correlation to reverse-engineer the underlying calculation of the urban_sustainability_score. By identifying the precise weights (coefficients) of the constituent urban metrics, the analysis provides quantifiable, data-driven leverage points for effective urban planning, resource allocation, and policy intervention.
Goal: To transform raw urban data into an actionable, weighted formula that guides strategic investment for maximizing city sustainability.
2. Key Questions Explored and Answers
The analysis specifically addresses the following strategic questions:
Data Integrity
- Question: Is the data clean and ready for modeling?
- Answer/Finding: Yes. The dataset is pristine, with no missing values or duplicates. All features are pre-normalized (scaled between 0 and 1).
Exploratory Data Analysis (EDA)
- Question: How is the sustainability score distributed, and which metrics are most correlated?
- Answer/Finding: The score is relatively balanced (approx 0.48$) and is strongly driven by Green Cover Percentage +0.69) and Renewable Energy Usage +0.48).
Land Use Impact
- Question: Does the Land Use Type (Commercial, Residential, etc.) independently influence the score?
- Answer/Finding: No. Land Use Type shows negligible impact, proving sustainability is a function of implemented practices within a zone, not the zone itself.
Predictive Modeling
- Question: What is the precise formula (weights) used to calculate the score?
- Answer/Finding: The score is a perfect linear combination R^2 = 1.0000) of six key features. Green Cover Percentage has the highest positive weight +0.394).
Strategic Planning
- Question: What are the highest-impact policy levers?
- Answer/Finding: Prioritizing Green Infrastructure and mitigating the symmetrical negative risks of Carbon Footprint and Disaster Risk Index.
3. Analytical Methodology and Findings
The notebook follows a rigorous, three-stage process:
A. Exploratory Data Analysis (EDA)
- Target Distribution: A histogram was used to show the score's central, slightly bimodal distribution.
- Correlation Analysis: A bar chart clearly ranked the drivers, isolating the six most impactful metrics.
- Interplay Analysis (Scatter Plot): Visually confirmed the trade-off, showing high sustainability only occurs with simultaneous low Carbon Footprint and high Green Cover.
B. Predictive Modeling (Linear Regression)
- Model Selection: Linear Regression was chosen to reverse-engineer the index's composition due to the clear linear correlations.
- Validation: The model achieved a perfect fit R^2 = 1.0000 and MSE = 0.0000), confirming that the sustainability score is a deterministic, weighted sum.
- Coefficient Extraction: The coefficients of the linear model were extracted and interpreted as the explicit policy weights for the Urban Sustainability Index.
C. Strategic Conclusion
- The findings were distilled into a strategic action plan based on the quantified weights.
- The single most important finding: Green Cover Percentage carries a +39.4% weight, providing the greatest positive leverage.
- Key Insight: Environmental risks (carbon_footprint and{disaster_risk_index) impose an identical -19.7% penalty, emphasizing the need for integrated climate mitigation and adaptation policies.
4. Tools and Libraries Used
The analysis relies on standard, robust Python data science libraries:
- Data Handling: pandas and numpy were used for data loading, cleaning, manipulation, and statistical aggregation.
- Visualization: matplotlib.pyplot and seaborn were essential for generating professional-grade histograms, bar plots, box plots, and scatter plots to visualize distributions and driver interplay.
- Modeling: sklearn.linear_model.LinearRegression was the core algorithm used to reverse-engineer the score's formula and extract explicit weights (coefficients).
- Evaluation: sklearn.metrics was used for calculating R^2 (coefficient of determination) and MSE (Mean Squared Error) to validate the model's perfect fit.
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