Simulate the 2016 Election!

by Douglas Locke, Sara Obaid Ul Islam, & Xingwei Huang

This is a chance for you to play with actual election data from 2016 election and discover how it was misinterpreted and misreported. The result of the 2016 election was shocking for pundits, journalists and generally people who were keeping up the news, polls and data.

This project invites you to take a look at the facts i.e. the pre-election polling data again and determine whether it was the data or how we interpreted it that lead to the shock and confusion about the results. This is a unique opportunity to engage with the data, the uncertainty surrounding it and simulating an actual election based on that data.

Margin of Error & Uncertainty in Data

In our visualization we represent the uncertainty (margin of error) in our data through spinning bars. The higher the margin of error, the less confident one can be that the poll will reflect the true voting results. In our demonstration, lower margin of error will slow down the spinning speed because of higher uncertainty.

Smaller MOE ---> Higher Certainty ---> Faster Speed ---> More Prominence