View allAll Photos Tagged visualization
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
------------------
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
---------------
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.
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.
Yantram 3D Architectural Visualization, Architectural Presentations provides 3D Architectural Rendering and 3d Architectural Illustration services.
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.
DAVOS/SWITZERLAND, 24JAN13 - Geoffrey B. West, Distinguished Professor, Santa Fe Institute, USA; Global Agenda Council on Complex Systems speaks during the session 'Visualizing What We Know' at the Annual Meeting 2013 of the World Economic Forum in Davos, Switzerland, January 24, 2013. .
.
Copyright by World Economic Forum.
.
swiss-image.ch/Photo Moritz Hager
From: www.connectedaction.net
Top most between Twitter users who recently tweeted the word GetRealChat when queried on August 15, 2011, sorted by betweenness centrality.
See: twebevent.com/getrealchat
A visualization of the #GetRealChat network is here: www.flickr.com/photos/marc_smith/6046223381/sizes/o/in/ph...
Betweenness Centrality is defined here: en.wikipedia.org/wiki/Centrality#Betweenness_centrality
Top most between users:
@pammktgnut
@pegfitzpatrick
@dillonrhodes
@mqtodd
@janetcallaway
@stevecassady
@cubed
@paulsteinbrueck
@thehealthmaven
@dabneyporte
Graph Metric: Value
Graph Type: Directed
Vertices: 108
Unique Edges: 744
Edges With Duplicates: 992
Total Edges: 1736
Self-Loops: 145
Connected Components: 3
Single-Vertex Connected Components: 1
Maximum Vertices in a Connected Component: 104
Maximum Edges in a Connected Component: 1726
Maximum Geodesic Distance (Diameter): 4
Average Geodesic Distance: 2.009607
Graph Density: 0.083419868
NodeXL Version: 1.0.1.174
More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/
NodeXL is free and open and available from www.codeplex.com/nodexl
NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) - which is dedicated to open tools, open data, and open scholarship.
The book, Analyzing social media networks with NodeXL: Insights from a connected world, is available from Morgan Kaufmann and from Amazon.
Marc Smith on Twitter.
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.
Same deal as the last few, except all schedule deviations for the notorious number 7 bus are highlighted in red.
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
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
www.visualcomplexity.com/vc/project_details.cfm?id=52&...
Timothy O'Brien used Python and GraphViz to generate this astonishing visualization of his 1st and 2nd level of connections on the O'Reilly Connection social networking site. The red highlighted connections are from Tim O'Reilly to other people, and, predictably, he's at the center of the activity. The graph was created by crawling FOAF (Friend Of A Friend) documents from Timothy's O'Reilly Connection profile and then obtaining the FOAF documents of people associated with him. The results were then stored in a neato format and visualized using graphviz neato.
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
Os LusÃadas is a Portuguese epic poem by LuÃs Vaz de Camões first printed in 1572.
The poem consists of ten cantos and 1102 stanzas.
At the left are the ten most frequent words in the poem by descending order of occurence.
This piece showcase one of those ten words.
Above is an area that directly represents the frequency of that word in each canto.
Each canto has a corresponding list of the ten most frequent words in that canto sorted by descending order of occurrence.
The length of the vertical lines for each canto represents its extension in number of verses.
Os LusÃadas is a Portuguese epic poem by LuÃs Vaz de Camões first printed in 1572.
The poem consists of ten cantos and 1102 stanzas.
At the left are the ten most frequent words in the poem by descending order of occurence.
This piece showcase one of those ten words.
Above is an area that directly represents the frequency of that word in each canto.
Each canto has a corresponding list of the ten most frequent words in that canto sorted by descending order of occurrence.
The length of the vertical lines for each canto represents its extension in number of verses.
(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
This is part of the experimental visualization project for cancer research, which we developed since 2008. For more information, please visit www.qplot.com/cases/cancer_pipeline/