Your group will brainstorm an interactive dashboard for viewing election results in a far future election. The complication is that you must design with a specific constraint which will limit what you can do. Here are the rules:
Please follow this procedure:
Step 1 - 11:25AM to 11:33AM - [GROUP] Identify Key GoalsIt's October, 2036 and an election is on in the USA. Two front-runners, Candidate Smith of the Bull Moose Party (party color goldenrod) and Candidate Thompson of the Whig Party (party color indigo), are closely competing for the presidency. As a part of a media agency, you have been tasked constructing a web-based dashboard which uses visualization to show users either:
Please enter the result of your dice roll and press the button to get your group's constraint
Dice result
Constraint:
Each "row" of data is an update of election results for a specific "precinct" (a part of a state).
Precinct name | |
State it is in | |
Total size of precinct | |
Amount of votes currently counted | |
Amount of votes left to count | |
# Votes for Smith | |
# Votes for Thompson | |
Prediction for the precinct | Algorithmic prediction of who will win this precinct (e.g. too close to call, clear Thompson win) |
Margin of error for prediction |
As new precinct results come in, you generate updated state data.
State name | |
Number of electoral votes for state | |
Total # of votes currently counted | |
Total # of votes left to count | |
# Votes for Smith | |
# Votes for Thompson | |
Prediction for the state: | Algorithmic prediction of who will win this state (e.g. too close to call, clear Thompson win) |
Margin of error for prediction |
Finally, you have country-wide data which is updating frequently.
Number of electoral votes won by Thompson | |
Number of electoral votes won by Smith | |
Total # of votes currently counted | |
Total # of votes left to count | |
Prediction for the country | Algorithmic prediction of who will win |
Margin of error for prediction |
Each "row" of data is a poll. Polls can be nation-wide or on a state level. Assume they are coming in regularly and you have data for some but not all states each day. Thus far you've got about 2 months worth.
Poll title: | Name of the poll |
Date: | When the poll was conducted (can be a range) |
Rating: | Your company's "rating" of poll quality |
Sample size: | Number of people polled |
Sample details: | What kind of people were polled (e.g. registered voters, likely voters, adults) |
Sample region: | Area where the sampling was done (e.g. country-wide, NY voters) |
Results: | Polling results (e.g. Thompson 44%, Smith 48% |
Margin of error: | Possible error in results |
Adjusted results: | Polling results corrected by your proprietary algorithm |
You also have access to these data from a computational model for every day of the past two months.
Estimated candidate results: | An estimate of the "true" poll results by aggregating polls |
Margin of error for estimate: | Possible error in estimate |