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Specifications

Who is the target user and what is the context it is used for?
Apply different interactive visualization methods to describe the evolution of COVID-19. Make sure the users can compare spatial-temporal features.
Analyze potential correlations between confirmed cases (death cases) and possible analytical factors. (Based on your own question, for example, do smokers have a higher risk to die, do handwashing or life expectancy have an impact toward COVID-19?)
What is your prediction for COVID-19 evolution in the next 3 months (can be trend, sequence or on your own choice)? How do you evaluate your results? Use different prediction methods and compare your predictions.
Can you find other factors which could affect COVID-19? For example, temperature, weather, even stock prices, or social media emotions? Find new datasets to prove your points.
Users should be able to do exploratory analysis with the visualization to answer the final question: How to fight COVID-19?

7th Dec

The visualization tool - either as a set of files with instructions on how to run it, or a URL to a website where the tool is available for trial.
A 5-minute intro video of the tool, explaining the main functions, and reflection questions.

Filter the data according to the different combinations of attributes. It can be based on your own choice, for example, to show only countries where the median age of the population is over 40 years and GDP per capita is less than 5000.
Explore the relationships between attributes with expressive figures
Incorporate another dataset (of your choice) into the visualization.

Describe the theoretical principles that you followed when working on this assignment. This is an important part where you show that your choices were not random. Describe how you chose colors and chart types; how did you design the views and layouts. We have discussed a lot of theory in the course, this is the place where you connect the theory to practical exercise. You don't have to re-cite long chapters. Assume the teacher (and evaluators) knowsthe theory. You should show how you considered the theory during your design and implementation process.
Describe some hypotheses and findings that one can see using your tool. Here you should not go deep into machine learning, statistics etc. You should rather show how your tool is useful to get insight.
The assumptions you made about the unclear attributes in the data

Non-Graded part

What were the most difficult things?
How much time did it take? Try to estimate it in hours.
Do you feel there is something missing? What other things should be included in the assignment?