Living Better: OECD Insights

OECD Wellbeing Explorer: an interactive dashboard to compare countries, view wellbeing profiles, and explore links across health, jobs, education, and environment.

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Inspiration

The inspiration behind this project came from the OECD Better Life Index, which provides a comprehensive view of well-being across countries. I wanted to create a tool that allows users to explore these insights interactively, understand the factors contributing to life satisfaction, and make data-driven comparisons between nations.

What it does

This project provides an interactive dashboard that lets users:

  • Compare countries based on well-being indicators like health, education, income, and environment.
  • Visualize trends over time and across regions.
  • Identify correlations between different indicators using charts, maps, and heatmaps.
  • Calculate custom scores using weighted indicators, e.g.,

[ \text{Custom Score} = \sum_{i=1}^n w_i \cdot x_i ]

where ( w_i ) is the user-defined weight for indicator ( i ), and ( x_i ) is the normalized value for that indicator.

How we built it

The project was built using:

  • Python & Pandas: For data cleaning and preprocessing.
  • Plotly & Dash: To create interactive charts, maps, and dashboards.
  • Jupyter Notebook: For exploratory data analysis.
  • CSV/JSON APIs: To fetch OECD datasets dynamically.

The workflow involved:

  1. Collecting and cleaning OECD data.
  2. Normalizing and transforming indicators for comparability.
  3. Developing interactive visualizations to explore insights.
  4. Integrating these visualizations into a cohesive dashboard. ## Challenges we ran into
  5. Data inconsistencies: Different countries report indicators with varying frequencies and units, requiring normalization.
  6. Handling missing data: Some countries lacked recent data for certain indicators.
  7. Interactive performance: Large datasets caused lag in the dashboard, which we optimized using caching and filtering.

Accomplishments that we're proud of

  • Successfully built a fully interactive dashboard with multiple visualization types.
  • Enabled users to create their own custom well-being scores.
  • Made OECD data more accessible and understandable for non-experts. ## What we learned
  • The importance of clean, normalized data when comparing international metrics.
  • How to design user-friendly dashboards that convey insights clearly.
  • Techniques for handling missing or inconsistent data in large datasets. ## What's next for Living Better: OECD Insights
  • Add predictive analytics to forecast well-being trends.
  • Introduce more granular regional data within countries.
  • Allow users to simulate policy changes and see their potential impact on well-being indicators.
  • Incorporate machine learning to identify patterns and clusters among countries based on life satisfaction.

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