The Cities of Tomorrow – Urban Growth & Sustainability️
Project Objective
This fabric notebook explores the Sustainable Urban Planning & Landscape Dataset. It explores the factors driving urban sustainability and livability in cities, focusing on:
- How green area availability, renewable energy, and other urban features influence population movement.
- Identifying the top cities to live in, based on data-driven insights.
- Understanding the interplay between urban planning and state-level clean energy trends.
Key Features Explored
- Understanding factors leading to High Sustainability Scores.
- Predicting Sustainability Scores.
- Deeper Dive into sample U.S Cities with favorable Urban Sustainability Scores.
| Last Modified Date | 11/6/25 |
| Dataset | Link |
Additional Data sources:
- UN - Open Spaces & Green Areas
- UN - Population & Demographic Trends
- State and Local Planning for Energy
Created At: 11/5/2025
Notebooks Breakdown :
- Data Cleaning & Preparation.
- Exploratory Data Analysis.
- Insight Generation.
- Predictive Modeling - Sustainability Score.
- Deep Dive EDA on sample U.S Cities with favorable Urban Sustainability Scores.
Data Preparation
- Data loaded from built-in CSVs into pandas DataFrames.
- Basic checks: df.info(), df.isnull().sum() — dataset treated as complete (no nulls in provided copy).
- Typical steps applied/available: type conversions for categorical variables, scaling/normalization, feature selection.
Exploratory Data Analysis (EDA)
- Distribution, correlation matrix and scatter matrices were used to inspect relationships.
- Top positively correlated features with urban_sustainability_score: green_cover_percentage, renewable_energy_usage, public_transport_access.
- Top negative correlations: disaster_risk_index, crime_rate, carbon_footprint.
- Visual analyses: histograms, heatmaps, pair plots, scatter plots for selected feature pairs and time-series plots for green area trends.
Key takeaways:
- Green cover and renewable energy are the strongest positive levers.
- Public transport correlates with lower carbon footprint and higher livability.
- High disaster risk and carbon emissions are associated with lower sustainability.
Predictive Modeling
- Model: RandomForestRegressor (n_estimators=100, random_state=42)
- Pipeline:
- X = all features except urban_sustainability_score
- y = urban_sustainability_score
- Train/test split: 80/20
- Evaluation metrics reported: RMSE and R^2
- Feature importance extracted to highlight actionable levers (green cover, renewable usage, public transport, etc.).
A predictive model to estimate urban sustainability scores based on the available indicators. To help guide urban policy and planning.
Feature Importance
Notebooks examines which features are most important for predicting sustainability, providing actionable levers for urban improvement.
Bonus: US Cities — Population Drift & Green Space
- Merged green-area and urban population change datasets to identify cities where population movement aligns with green-area availability.
- Visualizations: mapbox scatter, time-series of green area share and per-capita green area, population change periods.
- Ranked top cities by green area per capita and recent population growth; found greener cities tend to attract population growth.
- Linked city-level green metrics with state-level energy mix (renewable vs fossil) using state energy CSV (stdscen23_low_ccs_cost_state.csv) for 2020.
Recommendations & Policy Actions
Top drivers of urban sustainability:
- Boost green spaces and renewable energy adoption → These are the strongest levers for improving sustainability.
- Invest in public transport → Even moderate improvements reduce carbon footprint and enhance livability.
- Mitigate disaster risks and emissions → Urban resilience and carbon reduction strategies are essential.
- Balance land use → Avoid over-concentration of residential or commercial zones; preserve green areas.
- Policy recommendations: Increase green cover, invest in renewable energy, improve transport infrastructure, and reduce pollution.
- Next steps: Integrate external data (World Bank, UN-Habitat) for benchmarking; explore causal inference and scenario modeling.
What Improves Sustainability?
Protect and expand green spaces: Data shows green area share and per capita are declining or stagnant. Cities with higher green cover have better sustainability scores.
Plan for population growth: Urban population is rising, putting pressure on green spaces and infrastructure.
Integrate renewable energy and public transport: These factors are positively correlated with sustainability and can help offset the negative impacts of urbanization.
What Should Planners Watch Out For?
Disaster risk and carbon footprint: Cities with high disaster risk and carbon emissions score lower on sustainability. Imbalanced land use: Overdevelopment of residential or commercial areas at the expense of green space reduces sustainability.
Driver/Threat | Data Evidence (US Cities) | Action for Planners |
| Green Cover Declining | Declining/stagnant in most cities | Protect/expand green spaces |
| Population Growth | Positive but slowing | Plan for densification |
| Renewable Energy & Transit | Correlated with higher sustainability | Invest in renewables & transit |
| Disaster Risk & Carbon Footprint | Negative correlation with sustainability | Mitigate risks, reduce emissions |
| Land Use Imbalance | Reduces green space, sustainabilit | Balance zoning, preserve green |
Conclusion
- Green space is the cornerstone of urban sustainability.
- Population growth and densification require proactive planning.
- Renewable energy and public transport are key levers.
- Planners must guard against disaster risk, emissions, and land use imbalance.
Detailed Notbook Results on Github Link
GitHub Link : turbo_winner/Nov2025_AbhineetSingh_CitiesOfTomorrow.ipynb at main · abhi2020-ds/turbo_winner
