BigDataFr recommends: The Data Behind a Season Without Snow Days

[…] As spring arrives in Washington, D.C., we close the chapter on an abnormal winter in the District: For the first time in the last five years, the federal government did not declare a single full day closure due to snow.
D.C. had its federal snow day chances. On March 14, D.C. braced for Snowstorm Stella, which threatened six or more inches. Typically, such threats cause the all-powerful federal snow day authority Office of Personnel Management (OPM) to declare a closure. But OPM didn’t bite and only issued a three-hour delay with the option to work remotely. Even my office — General Assembly’s D.C. campus, where I’m a full-time Data Science Immersive instructor — declared a snow day, as did many others. (OPM was probably right this time: Only a light dusting followed exaggerated predictions.)
Because of the excitement surrounding impending snow, it seemed like a given that OPM would issue a closure, as it has in the past. It made me think about whether there was any relationship between the emotional buildup resulting from a snowy-weather forecast and the chances of the OPM actually declaring a snow day.
I wondered: Are OPM’s opaque closure decisions truly, entirely based on the forecast, or are they also susceptible to human impulse?
To answer this question, I turned to data science.
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