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SmartData Collective > Big Data > Data Mining > Netflix zip-code data: How about political overlay?
Data Mining

Netflix zip-code data: How about political overlay?

StephenBaker1
Last updated: 2010/01/12 at 1:47 PM
StephenBaker1
2 Min Read
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I’ve been enjoying exploring this New York Times chart on Netflix rentals, zip code by zip code. The top rental movie in my Montclair zip code, 07043, is Milk. It’s also number one in nearby Maplewood, another liberal suburb (teeming with journalists). But cross into Newark, and Twilight rules. Milk falls from the charts.

It wouldn’t be hard at all to overlay voting patterns on this movie info, and then to use the Netflix data to unearth pockets of one voting tribe or another…


I’ve been enjoying exploring this New York Times chart on Netflix rentals, zip code by zip code. The top rental movie in my Montclair zip code, 07043, is Milk.
It’s also number one in nearby Maplewood, another liberal suburb
(teeming with journalists). But cross into Newark, and Twilight rules.
Milk falls from the charts.

It wouldn’t be hard at all to
overlay voting patterns on this movie info, and then to use the Netflix
data to unearth pockets of one voting tribe or another. I imagine,
though, that for political dataminers, this movie data simply
reconfirms what they already know about the various zip codes. It’s no secret that Montclair and Maplewood vote like Milk-loving
Manhattanites. The political Numerati are busy studying us…nbsp; block by block. (The fun and descriptive movie data would likely be more valuable to them for pitching to clients than running actual campaigns.)

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Seeing all the diversity in single metropolitan areas certainly would make me rethink a lot of TV advertising. In fact, Dave Morgan’s startup, Simulmedia, is busy breaking metro areas into different geographic segments, so that TV stations can segment their promotions. No sense promoting the TV equivalent of Twilight in Montclair, or Milk in Newark.

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TAGGED: data mining, geographic data, netflix
StephenBaker1 January 12, 2010
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