Another round of Mike Tomlin ObviouslyTracker™ 2014 leaves us with historic news: Coach Tomlin said "obviously" only once this week, the least obvious he's been since October 2012. And this comes after a loss to the Ravens!
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Another round of Mike Tomlin ObviouslyTracker™ 2014 leaves us with historic news: Coach Tomlin said "obviously" only once this week, the least obvious he's been since October 2012. And this comes after a loss to the Ravens!

Anya is live and ready to show you everything. Watch her strip, dance, and perform exclusive shows just for you. Interact in real-time and make your fantasies come true.
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Shale in the Steel City? This may be more Chris Briem's kind of thing, but I whipped up this quick chart after reading Michael Sanserino's story on Pennsylvania's shift from steel to shale.
Of course, labor force numbers don't tell the whole story: Michael notes the steel industry is estimated to have generated 12 jobs for every $1 million in steel, while the gas industry generates just six jobs.
Finding Pittsburgh's dangerous intersections
Crossing a street in Pittsburgh is like Andrew McCutchen avoiding wild pitches. Most of the time, he gets out of the way. There will be near misses. But when he’s hit, it hurts.
Pittsburgh gets a lot of positive press for walkability. But that didn't gibe with the war stories I've heard from folks who actually walk to work, which made the city sound more like this:
Together with four Post-Gazette's superterns (Emma Brown, Madeline Conway, Golzar Meamar and Sarah Schneider), I used PennDOT data, QGIS and PostgreSQLÂ to examinine pedestrian crash trends in Pittsburgh. Among our findings:Â
Pittsburgh, as it was being lauded for walkability, saw more than 2,000 pedestrian-involved crashes between 2006 and 2014;
Hundreds of Pittsburgh intersections saw at least one pedestrian crash;
Most of the crashes were the driver's fault, not the pedestrian's.
Here's how we did it. (And here’s an interview we gave on Pittsburgh’s NPR station explaining the project.)
Mike Tomlin ObviouslyTracker™ 2014: Game 2
Steelers coach Mike Tomlin's use of "obviously" took a hit after Sunday's win against the Browns, supporting the hypothesis that he cuts back on that particular modal adverb after victories.
Last year, he hit 17 "obviously's" after the team's 9-16 loss to the Titans.Â
Mike Tomlin ObviouslyTracker™ 2014: Game 1
In advance of Sunday's game against the Browns, here's the first tick in Mr. Tomlin's "obviously" count, compared to last year's record. He hit 10 at Tuesday's press conference, one more than last year.Â

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Making a 8-bit heat map in Excel
One of the challenges in making maps online is that you almost always have to fire up something moderately complicated, be it QGIS or TileMill or leaflet.js. That’s no sweat for someone who regularly works with maps, but that’s not the average news reporter.
Mapping where new residents to Pittsburgh are from
Anya Litvak, one of our energy reporters, made this map plotting the origin of new residents to Pittsburgh. Red means they previously lived in Allegheny County; yellow, in Pennsylvania but a different county; and aqua, outside Pennsylvania.Â
Big cluster of out-of-state/out-of-county residents in Oakland, likely thanks to Pitt and CMU; another cluster in Uptown, likely thanks to Duquesne and perhaps the jail.
Looks like a lot of out-of-town people moving into Bellevue, Manchester, the South Hills, Oakmont ... and Moon Township? New development? Shale drilling?
Mapping same-sex couples in Pittsburgh
Here's a map I worked on with Post-Gazette executive editor David Shribman and investigative editor Lillian Thomas showing clusters of same-sex unmarried couples. (The Census will have to re-think things next time around!)
Saw the population centers I expected in Shadyside and parts of the North Side, but some surprises, too. Swissvale? McKees Rocks? (Though I suppose that could be Kennedy Township.) And not so many in Mt. Lebanon/Bethel Park ...
Mapping old immigrant groups in new Pittsburgh
Andrew McGill is the Post-Gazette's data reporting coordinator and a newsroom developer.
Two weeks ago, I hosted a class on building scatterplot density maps using QGIS, TileMill and Mapbox. As I said then, they're a great way to show relative densities of population with symbols that actually mean something.Â
A dozen or so staffers pulled their own Census figures to map, with some interesting results. I'll post my favorites for the rest of the week...
... starting with this one from columnist Brian O'Neill.Â
Penn State beat reporter Mark Dent describes how he analyzed the most popular academic majors among Penn State athletes, on the heels of a story he co-wrote showing that athlete-friendly "Rocks for Jocks" classes are a very real problem in top-ranked schools. (Penn State turned out to be an exception.)

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How we crunched data to discover NCAA athlete's favorite majors
On Sunday, the Post-Gazette ran a story analyzing academic records from nearly 50 top-ranked NCAA football and basketball teams, trying to determine whether universities were shuttling athletes to easy majors. The numbers were startling. Here, co-author Michael Sanserino explains how he put the story together.
The future could bring about a lot of change to college athletics. A unionization push threatens to change the nature of the university-athlete relationship. Antitrust lawsuits could destroy compensation caps on college athletes. The NCAA as we know it could drastically change as a result. The entire college athletics reform movement is one of the biggest – if not the biggest – story in the sports world.
Over the past 30 years, academics and critics have questioned whether athletes are being forced into certain majors because of time restrictions due to their sports; whether many different athletes naturally choose the same majors at their schools; and even whether coaches or athletic departments steer athletes into certain majors.
The Pittsburgh Post-Gazette reviewed the declared majors for players on the Top 25 football and men's basketball teams, as ranked by The Associated Press, during the 2013-14 academic year. The review showed that 13 of the 22 top-ranked football teams that disclose majors and 16 of the 20 basketball teams that disclose majors have athletes clustered in areas of study.
Building a scatterplot map in QGIS and TileMill
Scatterplot maps are great way of showing geographic density and diversity, conveying more visual information than your typical color-coded chloropleth.
I like how every point represents something concrete, giving an intimate feel close up and a thoughtful aggregate take when zoomed out. (Here is one of my favorites, showing population shifts in Washington, DC.)
What if Tom Wolf had never run for governor? Yes, the York businessman swept the Democratic primary, making for a pretty boring electoral map. But I found it interesting to see which candidate won second place in each county -- and presumably would have taken gold if not for Mr. Wolf.
In the Tom-Wolf-Stayed-In-York Mirror Universe, Rob McCord sweeps most of the state, with Allyson Schwartz holding on to Philadelphia and its suburbs, and Katie McGinty picking off a few central counties.Â
The winner? If Wolf's voters stayed home or split themselves between Schwartz and McCord, it'd be Schwartz -- by only 600 votes or.
(Here are the two datasets I generated to make this map.)
See where immigrants to Pittsburgh live in this interactive map. (How-to coming soon!)

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Ethan Magoc: How I mapped Pittsburgh's street paving plan
On Tuesday, Post-Gazette social media editor Ethan Magoc published a map plotting Pittsburgh's street paving plan. He writes on how he pulled the data, cleaned it and put it online.
It's a go-to data visualization method that is by now overused. Still, in some situations, a map is easiest to understand.
That was the case with a dataset the City of Pittsburgh recently provided. It was sent out to the public looking like this:
Pretty, but not the easiest to glean the useful information at a glance.
Pittsburghers who bump along certain deteriorated city roads care little about what the colors mean or which ward a to-be-paved road is in.
They want to know what exactly will be paved and — ideally — when it will be done (apparently not available in this case).
Got bored with the normal ways of displaying election return data -- tables and colored maps -- so I'm experimenting with some alternative visualizations. I don't think this treemap will make the cut, but it's a neat look at the varying influence of Pennsylvania's counties. (Everyone complains that Pittsburgh and Philadelphia dominate state politics, but this shows how important their suburbs are -- to both Democrats and Republicans) Â Full app here.
EDIT:Â Size is proportional to county's population. (Yes, voting totals would have been smart, but this is a demo.) Color is either red-Romney/blue-Obama or a delightful palette of automatically generated colors, depending on which view you're on.Â