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welcome to another season of diaspora baseball

i'm dropping down from last year's all-time high of four leagues to a modest two leagues this year. what this means is, my excuses for why my team sucked/was in 7th place will be even more bullshit this year.

so, in re andy's movement for new diaspora members (lost tribes of hyde park), any ideas?

i never really got an answer from joiner, but the league i'm in with him seems to have rallied from the brink of dissolution, so he might be less interested in joining.

what about ned tyrell corp?

n/c

denardspangledbanner back2back

we must protect this house

Just kill me now

Not happy at all about this photo illustration. Die, ESPN.

I got a little misty

You all might get a kick out of this.

spatial relationships, apple-tree division

from a write-up of A's prospects, regarding rickie's little bro:
"Weeks battled through injuries in ‘09 and appeared in just 80 games."

Czap, get ready for another 5-10 years of telling us you would have been a contender if jemile weeks hadn't gotten hurt...

Get Hard

HP diaspora style

dirty as fuck

flags fly forever

just so everybody knows: i'm going back-to-back cardarelli-style. the saud 2008 title was just a houston rockets interlude between dynasties.

Ben's new wallpaper.

Chalabi is a UofC Alum?

Raise your hand if you knew that Ahmad Chalabi was a UofC alum? Jesus christ, not only did the end of the world begin there but the continued fucking over is also sucking on its teat.

Randy Winn?

It's nice to see that the Yankees have finally just become a shitty Fantasy baseball team. Randy Winn? Really?

Massholes?

Parson me for posting a half-assed culture column on Slate, but I'd be interested to know what the Paul Revere in the HPD crowd thinks about massholes. Discuss.

put it on for my city

god DAMN oil can is looking good there -- note his full OCB embroidered on the warmup top and his SHADES. oil can boyd invented skinny-pants rap!

i'm still keeping matt kemp

My Nightmares Look Like This

Scott Brown is bathroom material

The only good thing about Scott Brown's victory?
Scott Brown and Burt Reynolds both appeared nude in Cosmo.
Burt Reynolds appeared in Cannonball Run II, with Tony Danza.
Tony Danza appeared in Going Ape! with Manis (as the Main Monkey).
Manis (as Clyde) appeared in Every Which Way But Loose, with Clint Eastwood.
Clint Eastwood appeared in Pale Rider, with Chris Penn.
And Chris Penn appeared in Footloose, with Kevin Bacon.

Sloppy and Floppy

Well, it only took 9 years but I finally saw the naked polar bear run this afternoon. Running full speed up the center of the Quads: bad naked indeed.

Start the countdown

Just when I was feeling depressed again about the end of the Rickie Weeks era, a ray of light shone forth from the net. Jemile Weeks, pack your bags and report to Muzzy Field for evaluation.

re: mo

"It's facile and leads to self-satisfied conclusions that the data doesn't, actually, support."

this seems to me to be the weakest part of your argument (and it's sort of unfortunately located up in the thesis area). it's actually the "self-satisfied" conclusions that are most supported in the data.

rachel getting married is not a popular movie in compton. it is a very popular movie in lower manhattan. evidence: there is a compton-sized hole in the LA renting patterns map. whereas the lower manhattan map is colored bright red.

brains might be "notoriously poor" at teasing out fine correlations in spatial data, but this one's pretty easy.

can you elaborate on this? there's other stuff in your post, but this seems pretty central and also strikes me as just wrong, regardless of whether or not i like the implications of some of what you're trying to say below (and i think i do).

Why the maps annoy me

The NYT Netflix maps are just the latest in a series of ways geography is being used "interestingly" to make a "point" of some sort that's never actually asserted by the cartographer, but, rather, inferred by the user. This kind of stuff annoys me probably in the same way my using stats annoys scientists (not to say I'm a geographer. I'm not. But I fret about these issues a whole lot). It's facile and leads to self-satisfied conclusions that the data doesn't, actually, support.

Problem 1: The areas are broken up by ZIP code. I think the problems this calls forward are highlighed by the case of Hyde Park, which straddles at least two ZIP codes. You force a kind of "neighborhood" upon the environment that doesn't actually exist there. In some ways this is good, since it randomizes (to a degree) the boundaries. But in other ways it's bad, since 60637 starts to mean something sociologically/anthropologically, not just, you know, postally.

In general, if you're trying to determine something about patterns of some sort, you want to split the study area into quadrats (the number of quadrats determined by comparing the total number of observations to the entire study area). That, of course, requires a certain amount of granularity about the data that netflix might not provide (or the NYT not be interested in working through). But we can't assume that we know something about "a ZIP code" based on the fact that they rented x movie more than y.

(This returns to the one way in which ZIP codes are good, as I hinted above. They are only largely based on preexisting boundaries (of cities, towns, etc.) as opposed to entirely, like ward boundaries. In that sense, they shake up the possible sample you get in each code. The idea of gerrymandering a ZIP code only makes sense in LA.)

So I don't particularly think that ZIP codes are a revealing means of looking into what's going on. Fun, yes. Which leads me to point 2.

Problem 2: It's irresponsible to throw out data like this and let it sit to be played with, in my opinion, without another variable or something to provide context. What the NYT has provided us with is basically a big toy. As I said to Ben, it's interesting, but only like a crossword puzzle is interesting. I love crossword puzzles, and I love doing them, but I don't post about them or forward links to them, since I can't escape Postman's criticism of crossword puzzles as basically what overeducated and understimulated people do out of intellectual boredom. Is anyone surprised that the South Side of Chicago likes Tyler Perry? So what does it mean to point it out, other than to recycle something people would have already pretty much assumed? Without context, the analysis becomes circular, flattering the viewer into making conclusions he or she already suspected.

Problem 2a: There are no numbers that would help us analyze the data better. As in, we have no idea how many Netflix subscribers are in each ZIP code, either in toto, or as a percentage of population. Furthermore, we don't know how many movies, total, get shipped to each ZIP code. Finally, we have no idea how much space is between #1 and #5 in any ZIP code, yet those present fixed differences in coloring. That one lone ZIP code that really loves Rachel Getting Married? Maybe there's just one household in the entire ZIP with a one-at-a-time plan that has a serious erection for TV on the Radio. Again, this is a lack of context.

Problem 3: Autocorrelation. Basically, this means that similar observations tend to cluster. It's kind of a problem for geography, from my understanding, since one is always trying to figure out how much of the data is tainted by autocorrelation. If the point of the maps is to show that, yes, this shit is hell of spatially correlated, well, big deal. Again, there's nothing new in telling me that shit spatially correlated. I know that it's statistically very likely for adjacent ZIP codes to have similar renting patterns. I would like to know, in seeing this data, what kind of built in issues it has with autocorrelation, etc. Here's where something like Moran's I comes in handy. It tells you whether the dataset is correlated or not, allowing you to then more comfortably make conclusions about the distribution of rental patterns.I clean this up in the comments

And then, when there are breaks, like in HP, we're not equipped to understand if that's random or an actual blip, since, again, we're provided with such crappy data. We all *assume* that it's because of the UofC that Slumdoggy was so popular in 60637 (or at least that's what Mario Small suggested in his blog post that alerted me to the site in the first place), but we don't *know* that. And we have no way, with what we're given, to guess how much is the UofC or not.

Which leads, again, to circular and convenient conclusions that flatter our prejudices.

So I've got to go to a booze tasting now, but that's my crank attitude for now.

I'll add one last thing: the eye is a bad mathematician, and it is way too eager to see patterns where there are none, which is why it's so easy to lie with maps. Of course, I understand that this is all fun and a way to burn some time (see Postman and crosswords above), but I've seen the explosion of this kind of mapping shit lately as a threat to real geospatial analysis. I dunno. Forward this on to Conzen and see if he thinks I'm crazy. I'm willing to be told I am. But it doesn't change the fact that, in my work, I have to compete with jokes like the google books map that accompanies every novel.

OKOKK... last thing... What I would've liked is a per movie distribution as a hotzone, so, not bounded by ZIP codes. That would've been more interesting and sociologically useful, since it would help account for the variations in potential renting diversity within each ZIP.