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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...
Plus(undocumented):
* + and -: Increase or decrease the intensity (brightness) of the particles; multiple presses further increase or decrease the intensity.
* A and S: Add or Subtract particles to the visualizer. You can make the visualizer as complex (or sparse) as you wish.
* R: Reset the intensity and particle count to their default values.
* E: When in nebula mode (press N), this greatly accentuates the nebula clouds, making them very easy to see. (If you’ve used the M key to change modes, you may find that the nebula clouds aren’t visible; it seems they’re only used in certain modes.)
via Lifehacker: lifehacker.com/5055598/itunes-8-visualizers-undocumented-...
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.
From Isotype Revisited project (http://www.isotyperevisited.org) at the University of Reading. Reproduced with permission.
"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.
Data visualizations for earthquakes that killed more than 1K people, 1902-2008.
Source:
(1) spreadsheets.google.com/ccc?key=0AgdO92JOXxAOdFpmY2IzS0JC...
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.
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.
Visualization of Flickr geotagged photos, uploaded between 2007 to 2015 and geotagged with the highest accuracy (street-level). I generated a number of different visualizations. Some are more artistic in style while others are designed more informative.
This type of visualization has been done years before (check out Eric Fischer's maps). Maybe the statistics going on on the lower-right corner provide some additional information not available so far.
Created as part of my research project (maps.alexanderdunkel.com).
1. Visual thinking workshop in Toronto, 2. Geneva workshop, 3. Geneva workshop, 4. Geneva workshop
Created with fd's Flickr Toys.
Visualization of an email list. Each picture reprensents one Month. A Sediment is an author, the height
Visualization of an email list. Each picture reprensents one Month. A Sediment is an author, the height represents the length of teh body, each hair is a word. Answers are red lines.
Visualization of an email list. Each picture reprensents one Month. A Sediment is an author, the height represents the length of teh body, each hair is a word. Answers are red lines.
Created by Martin Wattenberg, the goal of this visualization was to give a quick answer to the question, "what's happening in the market?"
The screen is divided into rectangular tiles that represent publicly traded companies. The area of a rectangle corresponds to the market capitalization of the company, and the color tells you how the stock price has changed since the previous market close.
I mainly uploaded these to submit to the 'Backgrounds App' group for use for cell phone backgrounds on android devices.
if they aren't accepted, I'll be deleting them.
xox
Fusion TIFF File
U – Silk City
Project information
Location: Le Van Luong Road, Van Khe Ward, Ha Dong district, Hanoi
Type: Residential Building
Investor: Song Da – Thang Long Joint Stock Company
Total area: 9.2 hectares
Total investment: 10,000 billion VND
Building start date: November 2008
Building finish date: December 2013
Product by E5:
- Ariel visualization.
- Interior visualization.
- 3D Floor Plan
- Brand Identity Package.
- Catalog
- Signage Design for Model House
The 3D project completed in June 2010.
www.relevantlyspeaking.com/2008/02/22/social-network-visu...
found this FaceBook data visualization tool created by Ivan Kozik called Nexus. Nexus creates a graph of your social network and finds commonalities between your friends.Your can see and activate the tool HERE It calculates friend similarity , and highlights links between friends who share interests and groups. While the generated image is static, browsing the connections is dynamic: clicking a friend node shows who they are friends with, as well as all commonalities with mutual friends. Its a great tool to visualize how your friends are connected, which interests they share and the friends you have the most in common.
Project: Spring Forecasting Experiment
Location: National Weather Center (Norman, Oklahoma)
Date: May 2023
Photographer: James Murnan (NOAA/NSSL)
Summary: The Spring Forecasting Experiment provides opportunities for forecasters to provide feedback to ensure that new guidance products and visualization approaches meet their needs. This is a unique opportunity to see and influence the future of NWS forecasting tools for high-impact weather.
* The experiment is directly aligned with NOAA FACETs and Warn-on Forecast (WoF) programs, including examination and evaluation of real-time forecasts from a prototype WoF ensemble system.
* Activities are formulated to provide evidence-based information on how best to design convection-allowing models and ensemble systems, which is accomplished through contributions to the Community-Leveraged Unified Ensemble (CLUE) by EMC, GSL, NCAR, NSSL, and GFDL.
* The SFE supports NOAA plans to develop a simplified, unified forecast system (UFS) centered on the FV3 dynamic core. A prototype FV3-based CAM system called the Rapid Refresh Forecast System (RRFS) will be evaluated against the operational HRRR and HREF for severe weather forecasting applications..
* New testing and evaluation activities for SFE 2023 will include examinations of regional and global-with-nest versions of the Model for Prediction Across Scales (MPAS). New extended range (Days 3-8) CAM ensemble evaluations will also be conducted.