CS 448B: Final Project

Global Central Death Rates x Development indicators

To better highlight these trends over time, we offer a dual-map visualization with accessible manipulation of variable global development indicators to contrast with central death rates. In respect to functionality, these two independent but interconnected maps are meant to display for the years 1980 to 2019, the following :

• The left map represents central death rates as a heat map, using a gradient to signify variation in mortality rates across countries for a selected year.
• The right map provides an interactive platform for users to overlay development indicators — such as GDP, population, energy consumption, and energy production— on a global scale. User’s can toggle between these indicators to observe trends and potential correlations between these indicators and central death rate.

The comparative framework of this visualization captures the complex relationship between energy systems, development metrics, and health outcomes. While this remains an overview of our topic of interest, we are able to pinpoint that countries with greater sustainability – reflected in higher energy consumption and production — and higher GDP demonstrate a stronger capacity to reduce central death rates. Reinforcing the notion that a nation’s level of development is closely tied to improvement in human well–being; better life expectancy.

Correlations Between Mortality and Development Conditions”

Our second visualization explores the relationship between Central Death Rates (CDR) and a range of socio-economic and health conditions. The scatterplot allows users to select and compare (up to) two countries across various health metrics and development indicators, such as:

• Births attended by skilled health staff (% of total)
• Coverage of social insurance programs (% of population)
• Exposure to PM2.5 air pollution levels exceeding WHO guidelines (% of total)
• CO2 Emissions
• Energy Consumption

The user can filter by gender and age groups (from infants to the elderly), to calculate CDR values averaged across the different demographic groups they select. By examining these correlations, the visualization sheds light on how various development indicators, energy systems, and public health factors might influence (or at least coincide with) mortality trends across nations.

Dynamic Bubble Chart over Time

We created a dynamic bubble chart, enabling users to adjust the year increment, select x-axis and y-axis variables, and define the circle size parameter over time. For instance, users can analyze the correlation between energy consumption and averaged central death rate, tracking country trends throughout 1980 to 2019.

By analyzing various x-axis and y-axis combinations, we observed an interesting phenomenom: China excelled in areas like energy production, surpassing the United States in 2005, while maintaining a low central death rate. However, its CO₂ emissions exceeded those of the United States around the same time, despite similar energy consumption levels. Additionally, India showed significant potential, catching up with China and the United States in many factors over the period.