Difference between revisions of "The Challenge"
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Mini-Challenge 2 provides a three month set of data for you to analyze, covering April, August, and December 2016. | Mini-Challenge 2 provides a three month set of data for you to analyze, covering April, August, and December 2016. |
Latest revision as of 13:21, 13 July 2017
Visual Analytics Science & Technology Challenge 2017 MC2
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Mini-Challenge 2 provides a three month set of data for you to analyze, covering April, August, and December 2016.
The primary job is to determine which (if any) of the factories may be contributing to the problems of the Rose-crested Blue Pipit. Often, air sampling analysis deals with a single chemical being emitted by a single factory. In this case, though, there are four factories, potentially each emitting four chemicals, being monitored by nine different sensors. Further, some chemicals being emitted are more hazardous than others. Your task, as supported by visual analytics that you apply, is to detangle the data to help Mitch determine where problems may be. Use visual analytics to analyze the available data and develop responses to the questions below. In addition, prepare a video that shows how you used visual analytics to solve this challenge. Novel visualizations and analysis approaches are especially interesting for this mini-challenge. Please do not use any other data in your work (including other Internet-based sources or other mini-challenge data).
Questions
1. Characterize the sensors’ performance and operation. Are they all working properly at all times? Can you detect any unexpected behaviors of the sensors through analyzing the readings they capture?
2. Now turn your attention to the chemicals themselves. Which chemicals are being detected by the sensor group? What patterns of chemical releases do you see, as being reported in the data?
3.Which factories are responsible for which chemical releases? Carefully describe how you determined this using all the data you have available. For the factories you identified, describe any observed patterns of operation revealed in the data.