MapBrief™

Geography · Economics · Visualization
September 16, 2014

Open—Wide Open—in Portland: A FOSS4G Review

 

Having been to previous FOSS4G conferences—2007 in Victoria, 2011 in Denver—I was eager to circle back to this year’s iteration in Oregon to assess what’s changed, what’s new, and what I can adapt for everyday use in my consulting business.

I. Sprawl

With up to eight tracks running simultaneously, everyone’s conference was going to be different. But knowing that all the sessions would be available on video, one could take more “chances” with the unusual-sounding and catch up later with the more familiar (PostGIS, MapServer,etc. ) later.  Be honest, how many conferences leave you eager to review another few hours of talks a week later?

II.  Open Source Is A Development Strategy, Not A Price Point

The biggest cultural change from 2011’s FOSS4G ?

GitHub.

We all know what open source is and don’t have to address the legacy stereotype of open source participants as an exotic tribe of code natives uncorrupted by the ordinary lures of the market economy.  Everyone has a GitHub account, from your favorite 3-letter acronym government agencies to your other favorite 4-letter geospatial vendor. With this new broad understanding of the open source model comes the opportunity to have a richer conversation about the efficacy, adoption, and sustainability of the tools–themes touched on by keynoter Mike Bostock.

III. Remember the Browser Wars?  The Browser Won.

By far the greatest impression was made by the WebGL and the manifold possibilities of powerful applications that run in the browser with no plugins.  The technology itself is a little trippy with shaders and meshes made up of points, lines, and triangles.  Except you actually want to render your lines as triangles too (don’t ask).

Life was easy when there were hard-and-fast distinctions between Server, Desktop, and Web.  Then along came the mobile web.

Now we have the dominance of the browser.

Database? It’s inside your browser. With spatial features.  That are spatially indexed.

Spatial operations?  Inside the browser. Hefty on-the-fly rendering?  Inside the browser.

Let me throw out three implications:

  • the “basemap” is dead—your whole map, base features and thematic overlays—is being rendered on-the-fly. If we were at the dawn of a new Golden Age of Cartography a couple of years ago, I suspect we ain’t seen nothing yet.
  • the web is no longer synonymous with “connected to the Internet”. The use-case that kept cropping up was the new user expectation that their web app would keep working when disconnected from the interwebs and seamlessly sync with a canonical datastore when connectivity was restored.
  • no install software, because the browser is the operating system.  Anyone whose business required the tender mercies of Enterprise IT to install software will appreciate a more direct connection to end users via the browser. It’s one of those nudges that amplifies open source software’s natural advantages in rapid deployment and fast iterations.

IV.  No Paint-by-Numbers Business Models

Just as diverse as the tools on offer were the ways of incorporating open source to make money.  While funded startups Mapbox and CartoDB certainly commanded a great deal of attention, the true breadth of geospatial open source was perhaps best seen towards the margins.  Cloudant, recently acquired by IBM, talked up its platform in the context of PouchDB and the burgeoning smart car market.  The Climate Corporation, largely fueled by open weather and agricultural data ( acquired last October by Monsanto for $1.1B—yes, that’s a ‘B’ ) presented a bit on better spatial indexing in the Lucene/Solr/ElasticSearch family of search technology. What’s clear is that far from stale cliche of open-source-as-hippie-altruism, FOSS4G encompasses a much richer variety of possible income models than the increasingly cramped GIS-software-and-services subset.

V.  Location, Location, Location

With the logistics of the event well organized from top-to-bottom, I’d be remiss if I didn’t compliment the host city itself.

Sized quite manageably for visitors, Portland is one of the few US cities you can show off proudly to your Progressive Urbanist friends: ubiquitous public transit, Dutch levels of citizen cycling, high taxes, and restrictive zoning (edited for politeness).

And the weather belied the cliched stereotype of the rainy northwest:  a week 80F temps and bright sunshine. Reminded me of San Diego.

 

—Brian Timoney


Bridge photo courtesy of   Aaron Hockley’s Flickr stream
Netscape photo courtesy of   Toshihiro Oimatsu’s Flickr stream
Paint-by-numbers photo courtesy of   VJ Beauchamp’s Flickr stream

 

 

May 23, 2014

Data Insight, Reputational Risk, and Bill Belichick: Sports Analytics & Business Analytics, Part II

 

Can everyone immediately cite your worst professional decision?

If you’re 3-time Super Bowl champion and coaching mastermind Bill Belichick, any moderately savvy NFL fan would blurt out “4th and 2“.

 

Go for it on 4th-and-2 on your own 29 yard line with 2:08 remaining or punt to the Colts and give Peyton Manning the opportunity to march down the field?  Given the Colts had only one timeout, a first down would almost guarantee victory.

Belichick went for it; they didn’t make it; the Colts march in from the short distance to win the game.

And by most accounts, the odds were in his favor.

Insight Is Not Cost-Free

So if widely respected championship coach can get crucified for playing the percentages in a non-traditional way (during a regular season game in November), why are you surprised when your precious analytic insights that have even a whiff of the counter-intuitive fail to gain traction?

Statistical reasoning is abstract; middle-management reputational risk is very real, very immediate.

We can all applaud the “democratization of data” and tools that empower analysis-for-everyone. But if that means the status quo is at risk, well then, that’s a whole different conversation.  A conversation that’s typically the province of well-dressed, well-paid consultants rather than, say, you the underling who is a bit brighter than the good folks you report to.

Or else it’s the rare don’t-give-a-damn outsider like “The Coach Who Never Punts“.

 

 

Probabilistic Thinking In A Binary World

The most entertaining side-show of the 2012 Presidential election was the dust-up between Nate Silver and TV host Joe Scarborough when the latter decried anyone who declared that the race was anything but a 50-50 proposition.  Silver, of course, was drawing massive page views at the New York Times with his daily calculations of election probabilities using a large dose of Bayesian methods.  The revelation was that putatively intelligent people such as Morning Joe much prefer thinking about outcomes in binary/yes-no/Manichean terms rather than as shifting set of probabilities.  Especially when it pertains to a topic in which they are emotionally invested.

So when pitching a middle-manager on workflow improvements that have  an 83% chance of success, you think you’re saying “pretty good odds” when in fact what they are hearing is “it might not work.”

*  *  *  *  *  *

“Moneyball” has become the fashionable pop culture short-hand for analytics-driven decision making: but recall even Brad Pitt and his good looks encountered institutional resistance.

Without Brad’s good looks and Bill’s Super Bowl rings, what are you to do?

Spend as much time analyzing your organization’s culture as its data.

 

—Brian Timoney


 

 


 

March 5, 2014

Why We’re Smarter on Sunday: Sports Analytics & Business Analytics, Part I

We all remember when we first fell in love with tabular data.

For me it was the list of baseball batting averages of every major league player that was published in the Sunday newspaper. Still in church clothes, I’d commandeer the sports section and, donut in hand (the other Sunday indulgence), spend the next twenty-five minutes digesting line-by-line the offensive output of hundreds of players.

In 2014 it’s a bit different: all of this information and much, much more gets updated in near real-time on my phone.

We sports fans live a very strange cognitive double-life: we’re far more analytical about the teams we cheer for on the weekend than the organizations that provide our paychecks during the week.

* * * * * * * *

Information So Important, It Comes Once A Month


Before the first Friday morning of every month, a curious hashtag pops up on Twitter: #NFPguesses. Economists, business journalists, and assorted finance types weigh in with their guesses of the US unemployment rate and jobs added to be reflected by the Non-Farm Payrolls (NFP) report.

This number moves markets and political polls.

And it comes out once a month.

UPS used to have a slogan “moving at the speed of business”. Turns out, even in 2014, a whole lot of business is based on the same old monthly and quarterly reports–just like in 2004, 1994, 1984…

In part III of this series we’ll examine the tempo of data and business models.  Suffice to say when we tell our grandchildren about how we used to gather around Twitter at 8:30AM EST on the first Friday of the month to get the latest unemployment numbers, it’ll sound stranger than the Pony Express anecdotes of yesteryear.

(Well) Beyond the Pie Chart

Not only is the sports world is ahead of most of the work world in the tempo and freshness of its data, but it’s also showing itself more adventurous in the realm of data visualization.

An interesting application of heat mapping seen in the Daily Telegraph and BBC’s Match of the Day are the plots of player movements in the other football (soccer).  Below is a heat map of the wandering lethality of Chelsea’s Eden Hazard against Manchester City–

 

Worth noting that Match of the Day is broadcast on late Saturday evening in the UK: can we assume a good portion of the viewing audience is less than 100% sober? And yet what they’re being called on to process is more nuanced than whatever xeroxed chart is being passed around at our Monday morning staff meeting.

Where presumably everyone is 100% sober.

A Geographer Dominating Sports Micro-Geographies

Here in the States, some of the most innovative sports visualization is being done by Geographer Kirk Goldsberry.  (Like a legit Geographer–PhD and everything).  And we’re not talking a few color plates accompanying a paper in the Annals of the AAG–his stuff appears on ESPN’s web platform.

Which would make him the most read academic Geographer working today.

 

What’s worth pondering about Goldsberry’s work is how he communicates dense, multi-variate data to a very large general audience and draws out the most salient information, even it takes hand-drawn circles.  One of the bigger sins of visualization is, in the name of displaying all of the data, not directing the viewer to the most interesting/important information.

And not providing a meaningful point baseline of comparison.

What I like about the chart below is that the variation in LeBron’s shooting percentages in different areas of the court is a little bit interesting.  But it’s the diverging color-ramp of “efficiency” that lets us assess his relative strengths compared to other players in the league.  Those dark blues and dark reds are the real story of the visualization.

 

 

No Cheering in the Office

What’s been fascinating about the rise of sports analytics is that the momentum has been largely created outside the leagues themselves.  Take the emotional over-investment of a large portion of the public and amp it up with gambling, fantasy leagues, and multiple 24-hour sports tv and radio networks: the hunger for information is immense.  Despite the data deluge, resistance among traditionalists has been very real over the last 20 years.  In part II of this series will take a look at the battle happening both in your workplace and the stadium: the struggle between Analytical Insight and Reputational Risk.

 

—Brian Timoney


Calendar photo courtesy of   Joe Lanman ‘s Flickr stream