On the website www.gapminder.org, there are two datasets. The Gapminder World datasets are described as older by the website, while the Gapminder Tools are described as new. All of my chosen datasets are from Gapminder Tools. The first data set is Income per Person (GDP/capita, PPP$ inflation adjusted). It is the Gross Domestic Product (GDP) per person, adjusted for differences in purchasing power. The numbers are in international dollars, and fixed to 2011 prices. The source is Gapmind, based on World Bank, A. Maddison, M. Lindgren, IMF and more (https://www.gapminder.org/data/documentation/gd001/). The next dataset is food supply, in kilocalories/person, per day (http://www.fao.org/faostat/en/#data ). Finally, I chose the Body Mass Index (BMI) datasets from Gapminder Tools, for men and women. They are counted in kilogram per square meter. Also noted, the mean is calculated as if each country has the same age composition as the world population. The sourcelink is https://imperial.ac.uk/school-public-health/epidemiology-and-biostatistics/. From these datasets, I will compare if the prosperity of a country tends to influence a higher calorie consumption. Also, I will look at how BMI and caloric consumption have changed over time, for both men and women. The independent variables include:
- Years
- Countries The dependent variables include:
- Gross Domestic Product per person, in international dollars
- Kilocalories per person, per day
- Body Mass Index (BMI) for men
- Body Mass Index (BMI) for women
This analysis was completed in a Jupyter notebook using numpy, pandas, matplotlib, and seaborn.
To conclude, the data analysis here has explored the possible connections between income, caloric intake, and body mass in various countries around the world. While the correlation between income and caloric intake is not as strong as one might expect, there is a moderate reflection of wealthier countries consuming more calories. It is also noted that in the the last three decades, caloric intake has been increasing in most of the world, and that is logically reflected in increased body mass indexes for men and women. The factors that contribute to food accessibility and the culture of food around the world are complicated, and cannot be reduced solely to the correlation of income and caloric intake. Causality cannot be shown by the data provided here.