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A data-visualization of meteorite impact data etched into stone at different locations on the Earth. Using data provided by Peter Jenniskens at the SETI Institute, Kildall wrote custom algorithms that created files specific for a high-pressure waterjet cutting machine.
When a large asteroid enters the earths atmosphere, it does so at high velocity of approximately 30,000km/hour. Before impact, it breaks up into thousands of small fragments, which are meteorites. Usually when they hit our planet in the ocean or at remote locations. Only recently have scientists been able to use GPS technology to geolocate the spread patterns, called Strewn Fields.
Using this data, along with the mass of the meteorites, Kildall has transposed the patterns using custom algorithms into 2D space and the programmed the waterjet machine — a high-pressure CNC water-cutting device to etch the data into stone. The waterjet reflects the kinetic energy of the asteroid and the stone is that of the Earth. Each stone is selected to match the type of rock found at that impact site on Earth.
Part of the group exhibition Future Artifacts
Thanks to Leisure & Culture Dundee
About the Artist Scott Kildall is cross-disciplinary artist who writes algorithms that transform various datasets into 3D sculptures and installations. The resulting artworks often invite public participation through direct interaction. His work has been exhibited internationally at venues including the New York Hall of Science, Transmediale, the Venice Biennale and the San Jose Museum of Art.
He has received fellowships, awards and residencies from organizations including Impakt Works, Autodesk, Recology San Francisco, Turbulence.org, Eyebeam Art + Technology Center, Kala Art Institute and The Banff Centre for the Arts.
FUTURE ARTIFACTS
Gabriel Menotti, Scott Kildall, Roel Roscam Abbing, Thomson & Craighead, Nedyalka Panova
This intervention-exhibition presents artworks and installations across the galleries and public spaces of the McManus: Dundee’s Art Gallery & Museum, and the Mills Observatory. The works conjure up an image of future artifacts, questions ideas of what we leave behind and reveals the hidden material cultures of our technological age. The audience can choose to hunt for these artifacts or find them by chance; either way they will provide new conversations between museum objects and their audience.
Shortest path tree for bike travel from some point Concord, California, accounting for elevation with an extremely high reluctance to climb hills. Compare to "spt-no-hill-reluctance".
same idea, but with size instead of color intensity. It's a little deceptive, because the area of a circle is actually proportional to the square of the quantity I'm trying to illustrate. Thus, it exaggerates areas of higher transitability
designcorner.blinkr.net/visualcomplexity.com/?page=3
Visualizing The Bible
This visualization started as a collaboration between Christoph Romhild and Chris Harrison. As Chris explains: "Christoph, a Lutheran Pastor, first emailed me in October of 2007. He described a data set he was putting together that defined textual cross references found in the Bible. He had already done considerable work visualizing the data before contacting me. Together, we struggled to find an elegant solution to render the data, more than 63,000 cross references in total. As work progressed, it became clear that an interactive visualization would be needed to properly explore the data, where users could zoom in and prune down the information to manageable levels. However, this was less interesting to us, as several Bible-exploration programs existed that offered similar functionality (and much more). Instead we set our sights on the other end of the spectrum - something more beautiful than functional. At the same time, we wanted something that honored and revealed the complexity of the data at every level - as one leans in, smaller details should become visible".
This process ultimately led them to the multi-colored arc diagram shown here. The bar graph that runs along the bottom represents all of the chapters in the Bible. Books alternate in color between white and light gray. The length of each bar denotes the number of verses in the chapter. Each of the 63,779 cross references found in the Bible is depicted by a single arc - the color corresponds to the distance between the two chapters, creating a rainbow-like effect.
This process ultimately led them to the multi-colored arc diagram shown here. The bar graph that runs along the bottom represents all of the chapters in the Bible. Books alternate in color between white and light gray. The length of each bar denotes the number of verses in the chapter. Each of the 63,779 cross references found in the Bible is depicted by a single arc - the color corresponds to the distance between the two chapters, creating a rainbow-like effect. " class="spark_image" />
Data visualizations for earthquakes that killed more than 1K people, 1902-2008.
Source:
(1) spreadsheets.google.com/ccc?key=0AgdO92JOXxAOdFpmY2IzS0JC...
Interactive Installation visualizing the retweet threads on Twitter following the Iranian elections of June 2009. To be shown at the IXDA2010 gallery
So my stock analysis program can take multiple predictors and weight them so they get the best results.
I made a simple predictor based on RSI values. RSI is an oscillator that varies from 0 to 100. A stock that's going up will generally have an RSI over 50, and a stock that's going down will have an RSI below 50. You can asign a number of days in RSI calculations which is sort of how many days it looks back. With a small number of days, the RSI indicator will bounce around like mad, and a large number of days makes it move very slowly.
There are a lot of ways to interpret RSI data, but for this, I just did something very simple. It looks at a stock's history and says "when the RSI is between 20 and 25, the stock changes x% in the next four weeks. When it's between 60 and 65, it changes y%. etc. And it assumes the stock will behave how it has in the past. I also had it average out the extreme RSI values that have only happened a few times.
So I decided to use two RSI predictors with different numbers of days to give better predictions for four weeks ahead. But the problem is how many days should the two RSI numbers have? If the two have very similar numbers (like 21 and 19), they're going to come up with basically the same predictions, so using the two won't be any better than using one.
So I set my program to try and use a whole range of different values and see which comes up with the best answer. In the top-left graph, "higher" is more accurate, and the X and Y values are the number of days for the two predictors.
The chasm running through the middle is where the days value of the two predictors is the same, and as expected, it's less accurate. Also, RSI numbers with an extremely short number of days don't seem to have much predictive value four weeks into the future. It's also clear that with extremely large numbers of days, accuracy tails off too. I'm guessing this is because the RSI values become static and the predictor is essentially just returning the same number all the time then. The two halves (on each side of the chasm) are identical -- A 19 day and 40 day prediction is the same as a 40 day and 19 day one.
The rightmost graph is simply looking at the first graph from directly above. The red areas are the more accurate areas. It's difficult to see what's going on in the red ares though. So I limited the color spectrum thing to just the highest areas of the graph and ended up with the bottommost one. That red patch is where the most accurate combinations are.
It didn't try EVERY combination because it takes a long time to run, but the most accurate it actually tested was a 43 and 150 day RSI. I'm going to have it try the neighbors to find the actual best one, though the difference in accuracy is probably negligible.
Oh, and this is really curve fitting, not predicting -- It's using data from the future to predict stock prices from the past. I may try running that again soon to see whether it does better at guessing future stock prices.
It might be worth noting that most stock traders use RSI in a short term fashion, to predict where the stock price is going to move in the next few days rather than four weeks. They typically use something like 14-21 days for RSI. The systems they use are generally more complex as well -- If you think the stock isn't trending, an RSI of 70 might be overbought, but if the stock is trending up, then it may not mean that, etc. Mine is very low tech at the moment. :-)
Visualization of www.unlekker.net as a graph.
See www.aharef.info/2006/05/websites_as_graphs.htm for explanation. See also the websiteasgraphs tag.
"Twitarium" is a project on visualizing Twitter,developed by a team called Dengaku5.
We have extracted locational information from atweet, and displayed a dot on globe, where it is coming from.
This is an example of using the Radar Chart to show level of expertise in various facets of a particular skill set: SSIS Development, comparing expertise from 2011 to 2012.
How do you extract the cube root of 87?
One of the best ways of finding square roots for all numbers which is commonly taught, is called the Babylonian method. It resembles long division, with remainders that build at an angle under a long radical sign. To understand how it works, you can think of it in geometric terms. On each iteration a root which when squared will account for a larger and larger square area of the total area of the square is discovered. The remaining two-dimensional area to be resolved is then represented by two long rectangles, and a smaller square which border the square of the root discovered so far.
This image represents my attempt to reason by analogy from this geometric understanding of the square root algorithm, to understand how the cube-root algorithm must work.
Once I had this picture, it became obvious that the second and all subsequent steps of the cube root algorithm involves coming as close as possible to the area of 3 squat boxes, 3 long boxes and a cube.
Where in the square root algorithm digits are broken up into groups of 2, beginning at the decimal point, and going into each direction, they are in the cube root algorithm broken into groups of 3. Where in the square root algorithm you begin by finding the closest square that is less than or equal to a number represented by the first group of digits, in the cube root algorithm your first step is to find the cube that is equal to or less than the number represented by the first group of digits. You second step is to take that quantity, call it a and then find a single-digit quantity b, that when the following formula is applied, give an answer equal to or less than the remainder:
b^3 + 3( 10a^2 * b + b^2 * 10a)
or b cubed plus 3 times the expression 10a squared times b plus b squared times 10a.
b cubed is literally the cube of the digit chosen for b. This quantity is taken once in each iteration. With each iteration it becomes the most negligible quantity of them all.
The rest of the formula is multiplied by 3, because we are dealing with 3 identical sets of two different boxes. These represent the 3 undiscovered edge-boxes, and 3 undiscovered face-boxes of the cube discovered so far.
In both of these terms a is multiplied by 10, because we are doing the calculation for the sake of the next place-value digit. We are doing a calculation for one tenth the magnitude of the preceding digit. The last few digits of these numbers seem to always be zeros, except for the cube of b.
Both of these terms have a squared value multiplied by a value without an exponent. They describe a 3 dimensional area, one side of which is always square. They can be thought of as extruded from the face of one of the cubes. Terms with a squared in them are extruded from the faces of the discovered cube, terms with b squared in them can be thought of as extruded from the face of the cube of b, all the way to the edge of the cube of a.
With each iteration of the algorithm the significance of b shrinks to about a tenth of what it was in the previous iteration, and therefore terms that depend on b cubed or squared rapidly diminish in importance. After a couple of iterations it is more important to see if the digit chosen for b produces an acceptable result when multiplied against 30a^2, and move on if it isn't close. (For that matter, just look at 30a^2, and see if it is already large or small compared with the remainder.)
One important lesson of the cube root algorithm is that cubes are very touchy, the slightest change to a digit way off to the right can put you out of the ball-park for the answer you're trying to get. The calculations are so cumbersome and susceptible to error simply because they are so numerous when attempted by hand that you would quickly be satisfied with a gross approximation rather than five correct significant digits.
This method of finding a cube root will find the cube root of 87. Since 87 is not a perfect cube, unlike, say 27, 64, or 125, some other methods that can find cube roots, like prime factoring, will not work for 87.
Funky visualization of my music listening over time from MartinD at last.fm. Read his explanation to understand what it means, or just enjoy the soothing colours.
Deputy Secretary of Agriculture Steve Censky and Deputy Secretary REE Dr. Scott Hutchins present the Awards for Data Visualization Winners on Monday, November 18, 2019 at USDA headquarters. USDA photo by Tom Witham
Created using lastgraph.aeracode.org
(for some reason, even though I've been on Last.fm since 19 March 2009, LastGraph only did 2006-2009...)
Visualization of data on liver disease from archive.ics.uci.edu/ml/datasets/Liver+Disorders . The purpleish colour are subjects who were diagnosed with liver disease, the greenish colour are healthy subjects. The points of the triangle are the relative values of blood tests for things associated with liver disease, in increasing value from left to right and from top to bottom. The intensity of the fill colour is dependent on the amount of alcohol in their system.
I chose this set of data to visualize because there is no apparent correlation between any two pieces of data, and I was wondering if representing every piece of data as a piece of a visual would lead to a significant pattern.