Cities of Tomorrow – Urban Growth & Sustainability.
This project analyzes urban planning data using Python and popular data science libraries such as pandas, numpy, matplotlib, and seaborn. The dataset, sourced from Kaggle, contains various features relevant to urban planning. The workflow includes loading the dataset, performing exploratory data analysis, and visualizing key insights to better understand urban development patterns and challenges.
Goal | Objetives
- Uncover insights about how cities evolve, sustain, and innovate for the future.
Mission
- Exploratory Data Analysis (EDA), Predictive Modeling, and Data Storytelling.
Findings
- Data quality is high: No missing values or duplicates detected.
- Urban Sustainability Score mean: 0.48, std: 0.17
- Feature correlation analysis completed: Some features have a strong correlation.
Conclusions: Insights Gathered
Through our analysis, several key insights emerged regarding urban sustainability:
- Population Density and Green Space Ratio are strongly correlated with sustainability scores, indicating that cities with balanced density and ample green areas tend to perform better.
- Public Transport Accessibility was identified as a top driver, suggesting that efficient transit systems are crucial for sustainable urban development.
- Renewable Energy Usage and Waste Recycling Rate also showed significant positive impacts, highlighting the importance of environmental initiatives.
- Cities with higher Education Index and Healthcare Coverage consistently achieved better sustainability outcomes, emphasizing the role of social infrastructure.
These findings underscore the multifaceted nature of urban sustainability and point to actionable areas for city planners to focus their efforts.
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