Amazon, whose Prime service claims more than 70% of upper-income households in the US — those earning more than $112,000 a year — is suddenly going after customers on government assistance who earn less than $15,444 a year for a one-person household.
The retailer on Tuesday announced it would slash the cost of its monthly Prime membership nearly in half, to $5.99 a month, for customers who have an electronic benefit transfer card, which is used for government assistance like the Supplemental Nutrition Assistance Program, better known as food stamps.
This Tax Day, former Microsoft CEO Steve Ballmer launched a new tool designed to make government spending and revenue more accessible to the average citizen.
The website — USAFacts.org — has been slow and buggy for users on Tuesday, apparently due to the level of traffic. It offers interactive graphics showing data on revenue, spending, demographics and program missions.
The analysis was published by The Trace, a nonprofit organization focused on gun violence. It found that cases of road rage involving a firearm — where someone brandished a gun or fired one at a driver or passenger — more than doubled to 620 in 2016 from 247 in 2014.
Reference: Arms on rise in road rage cases
Well, this morning, everyone should already know the winner and looser of the 2016 US presidential election. So, based on the spirit of this blog, let’s look at the winner and looser in infographic and predictive analytics as a result of the 2016 US presidential election.
Winner: Google Election
Google made it so easy to understand election status and results in one simple page with tabs for overview, president, senate, house, .., etc. The bar between the two presidential candidates is so clear that we know who is winning and who is catching up, as well as how many electoral votes are needed to win.
I especially like the semi-transparent red and blue to indicate the majority stakeholders by states and the status of swing states in the lower part of the dashboard. In contrast to what Google is doing, other media does not use the semi-transparent colors for the remaining states so it becomes less clear who would win the rest of the electoral votes and is difficult to see the trend.
On the other hand, the bar between the candidates in the Google dashboard diffuses the bias introduced by the area chart of the US map (implying large areas having more electoral votes).
The only item I would add to the Google election dashboard is to apply the semi-transparent colors to the bar between candidates as well. This would make the dashboard perfect.
In summary, Google election dashboard does a excellent job for the US presidential election. It brings clarity to both the status and trend of the election results in a very precise manner. It deserves to be the winner of BI dashboard design for this election.
Looser: Predictive Analytics
Predictive analytics does a very poor job in this presidential election. All predictive models consistently say Mrs. Clinton would win the election over Mr. Trump. As we all know this morning, the prediction is a total failure.
538 is a pretty popular site about predictive analytics. The following image shows you its prediction during the night of the election results. The forecast had been in favor of Mrs. Clinton until 10 PM, when the curve started to switch to the favor of Mr. Trump. And, the switch did not become evident until 11:30 PM (at the big gap where red line above blue line in the lower part of the chart), when some people could already tell the trend before the forecast trend.
However, we probably should not blame predictive analytics for such big failure. It is because the strength of predictive analytics is to predict “major trend”, not a single outcome; and most of our predictive analytics today rely solely on “data” and nothing else.
As I pointed out in my recent blog on my site and KDnuggest site, if predictive analytics is purely based on data without understanding the underlying process, its forecast is subject to noise and bias in the data and could be very inaccurate. This becomes evident during this presidential election. Because all data were bias towards Mrs. Clinton, it predict Mrs. Clinton to win.
In addition, since the presidential election result is a single outcome and involves a lot of human factors which cannot be quantified analytically, predictive analytics may not be the right tool for the prediction at all! The totally failed prediction makes predictive analytics the looser of this election.
Predictive analytics still works well in a controlled context, but may not be the right tool for election prediction unless we are able to (1) quantify human factors and correlations accurately, (2) do not depend solely on data, and (3) fully disclose the prediction errors.
This is a very good data insight.
… The Obama administration for years has been pleading with states to expand their Medicaid programs and offer health coverage to low-income people. Now it has a further argument in its favor: Expansion of Medicaid could lower insurance prices for everyone else.
… By comparing counties across state borders, and adjusting for several differences between them, the researchers calculated that expanding Medicaid meant marketplace premiums that were 7 percent lower.
Reference: Expanding Medicaid may lower all premiums
The bar chart shows a big contrast of gender pay gap by ethnicity for the Dallas county in the US. Seniority seems to have more impact than ethnicity on the pay differences. The analysis would be much better, if seniority analysis were included.
More than 75 percent of black and Hispanic workers hired by the county since 2001 work in the bottom three pay scales. Meanwhile, whites are almost four times as likely to make six figures as blacks or Hispanics.
The lack of minorities in higher-paying jobs has created the appearance of a pay disparity, prompting county commissioners to vow to take action.
The median salary for black and Hispanic workers is about $41,000 a year. For white employees, the median is more than $54,300.
Much of the gap can be attributed to differences among racial groups in seniority and job types. Black and Hispanic workers generally are paid the same as whites with the same experience and similar jobs.
But many white employees have had longer tenures with the county and, as a result, make more money.
Reference: The color gap
This is a nice article about infographic history.
Reference: The Surprising History of the Infographic