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Step 1: Get list of recent past problems at different refineries from news or people there after the fact or any number of ways.

Step 2: Get past location data that includes these periods (but spans a much larger time).

Step 3: Analyze what's different about the problem times compared to normal operation.

In some cases, it might actually be as simple as X% of work force doesn't come in for normal shift on what is otherwise a normal day.

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The discoveries through big-data analysis and visualizations can be endlessly counter-intuitive, mindblowingly unpredictable but also dynamic and powerful, even visceral when you have massively voluminous data that can be fed into systems like Tableau where you can actively tweak data queries, aggregations, transformations and visualization types.

The result can often be something akin to: "wow, who could have ever predicted that this data would extrapolate out to show such a unique visualization or trend"

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