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A more complete version of my UN general assembly voting visualization. Each line is a country. Red lines are Africa, Green are Europe, Blue are Asia, Orange are N. America, Yellow are S. America, Purple are Oceania. A yes vote makes the line continue on a tight curve around the centre, a no vote puts them into a spiral farther from the centre, and an abstain or absence makes the line go straight out from the centre. Every absence reduces the alpha of the line by 10, so countries like the Central African Republic, which never show up, quickly vanish.
Beginning Python Visualization: Crafting Visual Transformation Scripts
by Shai Vaingast
Seen at University of Washington Book Store and subsequently I bought this on my Kindle, it is a well written introductory book, I am amazed what you can do with the Python language.
sea 031
Frontop, a top cg enterprise in China, supplies architectural rendering, architectural visualization, 3d walkthroughs, 3d renderings for architects, real estate developers, designers, etc.
This visualization shows 1 million Manga pages sorted by their visual characteristics.
Software: imagej macro written by Lev Manovich
X = standard deviation
Y = entropy
this produces the following map:
horizontal dimension:
the pages on the left progressively haver fewer grey values; the pages on the right have a both black and white
vertical dimension:
pages at the bottom have only black and white
pages on the top have more grey / more detail / more realism
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As we can see, the stylistic space of Manga does not have any distinct clusters. Visualization allows us to describe such a space much better than discrete linguistic categories.
The two visual features chosen for this visualization describe only some dimensions of visual style in Manga - however in terms of these dimensions, we can state this:
the concept of "style" (as a set of distinct categories used to describe a set of objects) may turn out to be meaningless
then we analyze enough objects, their variability can be better described using a continuous function
(our present research in Manga user-generated genre tags is suggesting that the same may apply for genre categories)
therefore visualization is a better language for describing cultural variability than natural languages
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Note that some of the pages - such as all covers - are in color. However in order to be able to render image at this size (the original is 44,000x44,000 pixels - scaled to 10,000x10,000 for posting to Flickr), we rendered everything in grey scale.
Finally, because pages are rendered on top of each other, you don't actually see 1 million of distinct pages - rather visualization shows a distribution of all pages with typical examples appearing on the top.
@ Long Nguyen & Thu Nguyen
Architecture - Interior Design & 3D Visualization
0979 962 864, Ho Chi Minh City
advlongnguyen@gmail.com
This is another interactive visual analysis tool constructed for the Local Experiences of Automobility project. It consists of two primary views on the data--the upper timeline, showing all the drives that a participant made during the study period, and a collection of three timelines showing the details about the drive.
Visualizing the various features of the SwiftRiver distributed reputation and veracity functionality.
Frontop supplies 3d architectural rendering, architectural visualization, architectural walkthrough, etc.
Kiva has quite a few API and SQL interfaces for grabbing data and visualizing it. Actually makes the whole process all the more interactive.
3d-walkthrough-rendering.outsourcing-services-india.com/3...
Custom 3D Walkthrough and architectural rendering can deliver an emotional and immerse experience unlike any other pre-sales marketing tool. From the color of a candle on the bathtub to the leaves on a rare tropical plant, 3D Walkthrough will with high resolution 3d rendering create a presentation that will demonstrate your vision down to the smallest detail.
Photo of a Man on Sunset Drive: 1914, 2008
by: Richard Blanco
And so it began: the earth torn, split open
by a dirt road cutting through palmettos
and wild tamarind trees defending the land
against the sun. Beside the road, a shack
leaning into the wind, on the wooden porch,
crates of avocados and limes, white chickens
pecking at the floor boards, and a man
under the shadow of his straw hat, staring
into the camera in 1914. He doesn't know
within a lifetime the unclaimed land behind
him will be cleared of scrub and sawgrass,
the soil will be turned, made to give back
what the farmers wish, their lonely houses
will stand acres apart from one another,
jailed behind the boughs of their orchards.
He'll never buy sugar at the general store,
mail love letters at the post office, or take
a train at the depot of the town that will rise
out of hundred-million years of coral rock
on promises of paradise. He'll never ride
a Model-T puttering down the dirt road
that will be paved over, stretch farther and
farther west into the horizon, reaching for
the setting sun after which it will be named.
He can't even begin to imagine the shadows
of buildings rising taller than the palm trees,
the street lights glowing like counterfeit stars
dotting the sky above the road, the thousands
who will take the road everyday, who'll also
call this place home less than a hundred years
after the photograph of him hanging today
in City Hall as testament. He'll never meet
me, the engineer hired to transform the road
again, bring back tree shadows and birdsongs,
build another promise of another paradise
meant to last another forever. He'll never see
me, the poet standing before him, trying
to read his mind across time, wondering if
he was thinking what I'm today, both of us
looking down the road that will stretch on
for years after I too disappear into a photo.
How to visualize memory usage on Linux
If you would like to use this photo, be sure to place a proper attribution linking to xmodulo.com
Illustrative Visualization of a german climate change adaption research network – using processing and a metaball force field fpr moving agents
quick fluxus script visualizing email data flow for the hungarian freedom not fear 2008 event against the eu data retention directive.
wiki.vorratsdatenspeicherung.de/Freedom_Not_Fear_2008/Bud...
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
Y axis: incident energy per square meter as measured at the University of Washington, in joules per square meter. The top line, 30000000 J/m^2, is about 8.3 kilowatt-hours per square meter.
X axis: day of year
The same is composed of minutely recordings at the UW between Jan 1 2006 and Jan 1 2010.
A few features pop out to me. First, it's interesting to note that through December to the end of January, insolation around 1 MJ/m^2 is not uncommon, a factor of 25 less than a typical insolation of 25 MJ/m^2 in July. The average household uses about 110 MJ/day. To power a household during the summer would take only about 4 square meters of (100% efficient) panels, during winter an average household would require over 100 square meters of (impossibly efficient) panels, or a square ten meters on a side.
This graph also shows the characteristic cloud cover at different times of year. A greater spread indicates more clouds. A tighter grouping towards the top of the graph indicates clear weather. It's more cloudy in winter, and the sun comes out reliably between the summer solstice into late October.
Finally, total energy input at ground level is a metric with one of the greatest level of spread because it's influenced by a combination of two properties that move together - the total sunlight time and the angle of sunlight. As a result, whereas both the angle of sunlight and total sunlight hours might seem to improve painfully slowly through the spring, the total insolation is really hopping to new highs every couple of days. If you're a SAD-sufferer looking for hope through January and February, keep your eyes on this metric.
The script is here: gist.github.com/761474
(24-6-11-Cyberspace / I've had to tiddy-up)
The reason for calling the images on this Flikr set snapshots is simple: they try to capture the essence of a live network. It is constantly growing and shifting -and new information keeps completing the imperfect image all the time.
The left side of this image represents how the graph looked like yesterday. On the new network diagram to the right, you can see that the International presence has been placed in a square and moved to the opposite side. This positioning is irrelevant in a way, because the best manner of visualizing the network would be in 3D.
The Green dots all represent buses from the #CaravanaMX. The blue dots are coordination nodes for the Movement and the ones on the outskirts of the graph represent hash-tags. Light blue nodes are Twitter accounts and some follow the green buses because they were ridding on them.
Red dots are also hash-tags, but I would say of a much more public nature. The red dot at the right side of the new graph, for example, represents #CaravanaVirtualMX which was another way people followed the trip.
Richard Nieman, Global Medical Officer; Senior Vice-President, Teva Pharmaceutical, USA capture during the Session: "Visualizing Disease" at the World Economic Forum - Annual Meeting of the New Champions in Dalian, People's Republic of China 2017. Copyright by World Economic Forum / Sikarin Fon Thanachaiary