A VerySpatial Podcast
Shownotes – Episode 372
September 3, 2012
Main Topic: Our conversation with Dan Adams of TomTom
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I love to cook. My wife says I’m pretty good at it, although that could just be an attempt to not have to cook herself. We don’t have cable so I don’t get to watch many cooking shows. However, occasionally when we travel, I get the Food Network… then I become a couch potato. I’m hooked on watching people compete by preparing different dishes in different ways. I especially love the ‘random box of stuff’ sort of competition where the chef needs to work out something delicious from a collection of ingredients. That’s some real creativity there.
What I don’t love about these shows is the judging part. This collection of experts sits down and looks at their food, smells their food, and tastes their food rendering a ‘this is better than that’ decision. Now I’m no expert (I’m hardly a decent cook, much less a chef or a food expert), but it all seems so arbitrary to me. However, I’ve noticed a bit of linguistic turn coming into the descriptions from the judges. There are what I’d dub ‘axis’ words, such as ‘acidic’, ‘sweet’, ‘sharp’, and ‘bright’. Then there are more descriptive words that seem to modify that a bit, such as ‘tangy’, ‘flavorful’, ‘steep’, ‘heavy’, ‘light’, etc. Finally, there are some comparison words used, most normally ‘balance’ and ‘counter’. So you might hear a description such as, “I like that you balanced the heavy sweetness with a flavorful acidic quality.” To my quantitative ear, it almost sounds like these professional chefs and judges have this sort of equation in their head that moves along the axis of acidic, bright, and sweet. It is as if there is an attempt at an extremely loose quantification of what is basically a qualitative thing. How do you impose a sort of equation on what’s basically opinion? I don’t like raw tomatoes, so anything with raw tomatoes is going to trend ‘negative’ for me personally despite their utility in providing an acidic quality to the food equation. The point being, any one person’s mileage may vary here.
Now let’s jump to geography and GIS. Lately I’ve been straddling the divide between qualitative and quantitative data. We like quantitative data because you can measure it, you can compare it, you can mathematically transform it, if it relates to space you can map it, you can color it…. you can do all sorts of things to it. Not only that, we can represent that in known ways with agreed upon conventions. Things like gradients or relative shape sizes have been reasonably well worked out. We tend to know what to expect, and in fact we recoil when they’re not what we expect. Quantitative data doesn’t present us with much challenges. Qualitative data is a whole ‘nother critter. We don’t always know what to do with qualitative data. We can’t even agree if it has much utility or not (editors note: WHAT? Blasphemy!). We certainly don’t know how to store it in transformable forms, or even how we’d like to transform the information. We don’t know how to compare it, or even if it is comparable. And the issue of representation? That’s so far out there and varied it’s barely on the radar.
You can certainly see why judges seem to be attempting to ‘quantify’ these qualitative measures. With numbers you can work out a ‘winner’ – feelings, emotions, tastes are a LOT harder because of their subjectivity. However, I think we lose so much when attempt to boil down these complex flavors and aromas into a comparable framework. How can we capture that information and still retain the ability to compare, contrast, and represent in agreed upon ways? I’m going to do the thing I hate the most and cop out because I don’t have an answer. To be honest, I don’t think there is ‘an’ answer but a series of answers we have to work out.
Beloit College has released their 2012 list of things that new college freshman have known their whole lives, besides making some of us feel very old, it gives a good overview of the geospatial world today. According to the list, today’s freshman class was generally born in 1990, which would put them in the 1990-1999 GIS history timeline created by the GIS Timeline team at the Centre for Advanced Spatial Analysis. The geospatial elements on the list are a mixture of funny and humbling : 3. They have always been looking for Carmen Sandiego, 4. GPS satellite navigation systems have always been available, 43. Personal privacy has always been threatened, 51. Windows 3.0 operating system made IBM PCs user-friendly the year they were born, and 54. The Hubble Space Telescope has always been eavesdropping on the heavens.
The Mindset List has been compiled by authors, Ron Nief, Emeritus Director of Public Affairs at Beloit College and Tom McBride, Keefer Professor of the Humanities at Beloit College since 1998 to “reflect the world view of entering first year students” born in 1980. They provide suggestions on how the 2016 Mindset List can be used to start conversations and dialogues with students. In case you were wondering, the class of 2016 have always lived in cyberspace so to them working in the cloud is the natural progression of the technology they have always known.
It’s fair to say we over at VerySpatial are big space nerds. And it’s fair to say we’re also pretty big remote sensing nerds. When the guys over at BoingBoing got to ask any question they wanted, they asked a pretty cool one about file compression (scroll down to see the answer). Sending images from Mars and back takes a bit of work and time, which means file compression has to be used. But we all know that we want as loss less file compression as possible, so what’s NASA to do? They turned to a custom implementation that uses a wavelet approach similar to Jpeg 2000. The difference in their compression is that it’s less computationally intensive, which means lower powered CPUs (both in computing and energy needs) can be used to create the compressed images. Pretty cool, huh?