Oh, the BEAUTY..

Visually mind-melting, mouth-drooling gorgeous gorgeous gorgeousness. Too beautiful to ignore or not acknowledge..

The choices of hues, the intercity, the time, the craftsmanship, the talent.


Wall painting by Supakitch and Koralie

VIDEO  – http://vimeo.com/15076572

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functions and behaviours


in addition:

Scale number box = 0

a piece of code to never go below zero.

ie. if there are five or more transitions (shakes/movements) per second then ADD one to scale number.

if there are 4 or fewer transitions per sec then SUBTRACT one from the number.

Delay time in relation to scale number, bottle must be still, counts down, then thunder claps. Realistic to how lightning gathers and builds up, then gets released. Like a carbonated drink in a bottle.

To do:

find lightning sound samples. rumbles to thunder claps.


Mercury switches.

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a series of wonders

Finally inspiration has struck! Friggin’ A.

Bottled lightning.

Canned tornado.

Rainbow in a jar?

In the long run I’d like to create a series of “Packaged Wonders” but that’s a little ambitious for this class project so I’ll settle on one that’s simplest to make for now.

Bottled Lightning:

– As if lightning was captured and contained, allowing its behaviors, quality and characteristics to be played with. Like how a conch shell holds the sound of the ocean. Technology has empowered me with unlimited possibilities. The tension between humans and nature continues. I’m trying to bottle lightning like hunters hunting animals, paparazzi capturing celebs, or storm chasers seeking strange weather.

Notes to self: Black bottle. Shakes/vibrates and makes noise. Lighting possibilities? Similar to bottled soda pop. Input jack to connect Arduino. Sensor that collects data of tilts, shakes from someone interactive with the bottle. Should it be glass, plastic, metal? Can’t fit a motor in a narrow glass wine bottle, so shove under a bottle with a caved groove at the bottom. Stainless steel water canteens have larger openings.

The Canned Tornado would be like as if a Tasmanian devil was trapped inside the tin can.

Feeling good about this..

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David Hoffos – “Scenes From the House Dream”

"Winter Kitchen" from David Hoffos' Scenes From the House Dream

I stumbled upon the multi-video installation, “Scenes From the House Dream” by David Hoffos during TIFF week while trying to kill time before Todor Kobakov‘s lovely piano performance at the 107 Shaw gallery.

I have to say, it was one of the most inspiring exhibition show I’ve witness (though I haven’t witnessed too many). The ingenious use of mirrors to project moving images from tube tv’s, to the beautiful craftsmanship of the miniature models, to the David Lynch inspired atmosphere, all combined, left me in awe. It is definitely a piece that needs to be experienced in person. Each “scene” was captivating, sometimes had narratives, but was simply so well executed that you immediately immerse into these little worlds he’s created for you to observe.

I liked what was said in this review: http://www.eyeweekly.com/arts/galleries/article/102370–david-hoffos-scenes-from-the-house-dream

Great photos here: http://www.designboom.com/weblog/cat/10/view/11589/david-hoffos-scenes-from-the-house-dream.html

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Spooky Tesla Spirit Radio

I was surprised by how appropriate the responses from the radio were. Especially, the sounds that came from the flame of a candle.

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mpm35a – Assignment 1: Many Eyes

Part I – pick a dataset on Many Eyes that already has at least one visualization and create a different visualization of that same data. Is your version better? Worse? Why? Write a short explanation (about one paragraph)

Data: Crimes in Lawrence, Kansas (1999-2006) / Data visualization: Bubble Chart


My choice: Stack graph.


I think that my version of the visualized data isn’t better, but different. I like that mine gives a better sense of crime rates over the years thanks to how stack graphs show a sum, whereas the bubble chart doesn’t do that with just the scale legend on the side. However, it doesn’t make full use of the given data as specific types of crimes are too recorded. What would significantly improve my data visualization is if I was able to categorize all the types of crime into its related levels of punishment for said offences under Lawrence, Kansas’ jurisdiction. For example, I would like my graph to group all possessions of drugs, trespassing, simple assaults and so on as misdemeanours. Felonies, federal crimes, infractions, and etc would also be mapped with the appropriate crimes like how


shows “human resources”, “net interest”, and “national defense”. This would be a nice addition of information as it gives a sense of the severity of the crimes that’s been committed. Come to think of it, maybe the scatterplot would have resulted in the best represent of the given data…

*edit: http://manyeyes.alphaworks.ibm.com/manyeyes/visualizations/crimes-in-lawrence-ks-by-year-1999

The simple stack graph worked better than the stack graph with categories in my case! I like that you can also toggle between the “total” and “percentage” for different perceptions.


Part II – create your own dataset by importing some data into Many Eye. Create a visualization for that data and explain why you decided to use this particular visualization type over the others. Did you discover any pattern in the data?


First I made this one, but with the line graph (http://manyeyes.alphaworks.ibm.com/manyeyes/visualizations/us-emissions-metric-tons-per-perso). I felt it wasn’t effective in presenting such a heavy issue. The line graph had a lot of empty, negative space. So I went with the stack graph once again. I think the bar graph would of worked too, but again, the negative space in between the bars seem contradictory to the presented issue. From the visualization of my data, I can see that the US, by themselves, contribute half of the total amount that the entire North America emit as a whole. Further observations show that North America contributes one third of the world’s emissions. I may not have realized this as easily if I was just dealing with the numbers.

and also, because I felt my first visualization was too simple:


Surprise, surprise. The States and North America are the largest meat consumers in the world. It was a given to use the world map as my data provide stats for the entire world. I prefer the colour code over the bubbles to demonstrate the data. From the data visualization I can also see that there’s a strange intense drop in meat consumption in 1995. Not sure why, would need to research that a bit to find out… Also, Russia did not/barely consume any meat until 1992. Greenland did not consume a vase amount until 2002. Mongolia is the most consist in their meat consumption. Czech Republic did not form until 1993. Good information!

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