I’ve been keeping watch from time to time this week on the dynamically generated graph from our power company (OG&E) showing how many Oklahoma customers have been without electricity. On an aggregate basis, according to this morning’s Daily Oklahoman newspaper (NewsOK.com) over 600,000+ people are still without electricity statewide. My first screenshot from December 10th shows approximately 220,000 OG&E customers without power as of 10 am. Note the scale of this graph: There is a new line on the y-axis for every 20,000 customers:
The next day, in the late afternoon of December 11th, the OG&E system watch graph showed 300,000 customers without power. The y-axis graph scale had been changed however: Now it shows a new line for every 50,000 customers:
Last night, before going to bed, I noted the graph had changed again, showing a reduction in customers without power down to about 250,000. Again, however, the scale of the graph had changed, this time to show a new line on the y-axis for every 5000 people:
This morning’s graph shows that about 200,000 OG&E customers are without power currently. The scale of the y-axis has changed again, however, reverting this time to the original scale with a new line every 50,000 customers:
What are the implications of these graphs? (What do they mean in real terms?)
Does it matter that the y-axis scale has changed every time I’ve looked at the graph?
Is a positive slope on this graph good or bad?
Why does a negative slope bring hope?
How does a different scale on these graphs present a different perspective on the numbers of people either losing or gaining power in the OG&E electrical grid?
Does that matter from a public relations perspective?
As a saavy citizen, should we care?
How could we create a consistent graph, with the same scale, which accurately shows these changes over time?
I used the free US Department of Education’s online graphing tool from their Kidzone area to graph the following table of data:
- Dec 10th – am 220,000
- Dec 10th – pm (no data)
- Dec 10th – late pm (no data)
- Dec 11th – am (no data)
- Dec 11th – pm 300,000
- Dec 11th – late pm 250,000
- Dec 12th – am 200,000
I didn’t have data points for the 2nd, 3rd and 4th data points, so the line between the 1st and 5th data points is extrapolated. I added that line and the text annotations with Skitch, which I also used to immediately upload the finished image to Flickr. (I LOVE Skitch!) Lots of math connections here for sure! They key is, on a personal level, that the slope of the graph is now negative! Our family in southeast Edmond lost power at approximately 4:30 am on Sunday, December 10th. We got power restored briefly four different times on Monday. The shortest period of restored power was for 10 seconds, the longest period was 2 1/2 hours Monday evening. We got power back (apparently for good, since it has stayed on since) at approximately 11 am on Tuesday, December 11th.
Challenging students to use real-world data like this and think critically about it, as well as CREATE their own versions / knowledge products which reflect their own understanding of what the data MEANS is a valuable way to spend instructional heartbeats inside and outside of class.
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