🌎 Global Health vs. Wealth Analysis (1952 - 2007)

📌 Project Overview

This project explores the Preston Curve—the historical relationship between a nation's economic output (GDP per Capita) and its public health outcomes (Life Expectancy). Using the Gapminder dataset, I visualized how global development has shifted over 55 years, highlighting the "health-wealth" gap and the rapid progress of developing nations.

🛠️ Tech Stack

• Language: Python
• Libraries: Pandas, Matplotlib, Seaborn
• Datasets: Gapminder (Global development metrics)

🔍 Key Insights from the Data

1. The Preston Curve (Wealth vs. Health)

• Observation: Life expectancy rises sharply with GDP at lower income levels but plateaus after reaching approximately $20,000 per capita.
• Technical Detail: A Logarithmic Scale was applied to the X-axis to linearize the relationship and account for the exponential disparity in global wealth.

Figure 1: Relationship between GDP and Life Expectancy in 2007.

2. Global Progress: 1952 vs. 2007

• Observation: While the wealth gap remains wide, the "Health Gap" has narrowed significantly. In 1952, many nations had a life expectancy below 40; by 2007, most surpassed 60.
• Insight: Medical advancement and public health infrastructure have scaled faster than economic wealth in developing regions.

Figure 2: Comparative analysis of global development over 50 years.

3. Regional Clustering

• Observation: African nations cluster at the lower end of both metrics, while European and North American nations dominate the top-right quadrant.
• Insight: Outliers in the middle (high GDP but lower-than-expected life expectancy) often highlight internal inequality or specific historical health crises.

📊 Visual Encoding

In this project, I successfully encoded four dimensions of data into a single 2-dimensional plot:

1. X-Axis: Economic Power (GDP per Capita)
2. Y-Axis: Health Outcome (Life Expectancy)
3. Bubble Size: Population Size
4. Color: Geographic Continent

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