View allAll Photos Tagged Visualize
"Vulnerability" is the word that best describes small islands' situation. These territories, placed close to the equator, in Atlantic, Indian and Pacific Ocean, are subjected to extreme natural events, such as cyclones and floodings. These natural catastrophes weaken the territory and affect both population and economy causing, on the one hand, the inability to find a stable position in the marketplace and preventing from reaching, on the other hand, the benefit needed to face such catastrophes.
Global temperature rising strongly contributes to get the situation worse: IPCC's datas show how in the last decades extreme events have increased in number and intensity in conjunction with temperature rise. Even if they're not directly responsible of climate change, small islands are those who most feel these events and they are now experiencing what the rest of the world could be forced to face in one hundred years.
Nowadays, among dissenting opinions, the solutions found are very few, not entirely effective and, most of all, hardly feasible.
Project by:
Marco Agosta
Elisa Angelico
Michele Crivellaro
Federica D’urzo
Elisa Mariangela Raciti
This infographic refers to the 2007 IPCC report about the global warming, with particular focus on food, fibre and forest production.
The data analysis highlights a huge impact on the soil capacity according to the forecast about a rise of the temperature in the next decades.
Despite the positive effect on the crop production in the short term, in 2080 the scenario expected is alarming.
The topside of this visualization shows how the temperature would affect the cereals production (maize, rice and wheat) and how this could directly influence the global percentage of the people at risk of hunger.
The growth of the population and the simultaneous decrease in crop production do not allow the balance between supply and demand: between 2050 and 2080 this gap could cause negative social-economic effects.
The second part visualizes the relations between Humanity and the other actors of the system. Main relations link Humanity with Livestock, Agriculture and Forestry (medium level), which are themselves connected with Soil and Atmosphere. This second level of the system is where the effect of the Global Warming are firstly received. Than, by the connection with the medium level, these effects would fall on Humanity.
The title of the poster encloses the whole meaning: global warming has effects on cereals and their absence causes the death of Humanity. Humanity is also the first cause of the temperature increase, so it is like a sort of self destruction.
The only way to stop this vicious circle is by changing the human behaviors. Humanity can't act directly on Soil and Atmosphere, but can try to do concrete actions against the Global Warming in order to save cereals and also itself.
Project by:
Lara Caputo
Eleonora Cattaneo
Andrea Larghi
Enrico Luparello
Anna Menegolli
Maps of racial and ethnic divisions in US cities, inspired by Bill Rankin's map of Chicago, updated for Census 2010.
Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Yellow is Other, and each dot is 25 residents.
Data from Census 2010. Base map © OpenStreetMap, CC-BY-SA
We interrupt this series of Alaskan images to bring you an important public service announcement ... TGIF!!!
OK, I know I have lots of you feeling the same way. Getting all ready to finish off this work week and dive on in to the upcoming weekend!
Last night, I was going through my archive when I noticed something. Over late spring/early summer, I spent many hours photographing and hanging out with the burrowing owls of Broward County. However, I hadn't shared many of the images of these adorable little owls. Solution ... celebrate this Friday with one of my favorite ones.
As the young burrowing owls learn the ways of their world, they tend to do a lot of flying around. The burrows are staked out, so that's the perfect landing for them. It's also obviously where they launch from as well.
I love this image, as it almost appears as though the owl was using a visualization technique as it prepped for its launch. OK ... get on the edge of the stake... up with the wings... target in sight ... that's how it's done. LOL
Hope everyone knows how their weekend plans will unfold as well. I'm excited to say that I hope to be out photographing this weekend, as it has been a while since I have. Wishing everyone the best!
Thanks for stopping by to view and especially for sharing your thoughts and comments.
© 2014 Debbie Tubridy / TNWA Photography
From Nexus: apps.facebook.com/_nexus_/
Connecticut on the left, Molecular on the bottom, WPI on top, and the Boston/Cambridge social scene on the right..
Maps of racial and ethnic divisions in US cities, inspired by Bill Rankin's map of Chicago, updated for Census 2010.
Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Yellow is Other, and each dot is 25 residents.
Data from Census 2010. Base map © OpenStreetMap, CC-BY-SA
Representatives of different social strata were asked the same question: "What are the mail reason of success?" Poor need to change their life approach primarily, as it is evident from their responses
The University of Hawaiʻi at Mānoa’s Laboratory for Advanced Visualization & Applications (LAVA) was founded in January 1, 2014 by UH Mānoa Professor Jason Leigh. The mission of LAVA is to conduct research and development in big data visualization techniques, and to apply these techniques in cutting edge domain science, engineering, and training applications.
Image from "Flight Thru Instruments," a 1945 US Navy pilot-training manual designed by the Graphic Engineering Staff at General Motors, under the direction of Harley Earl.
More explanation on the blog:
"Flight thru Instruments" and the Fine Art of Instructional Illustration
www.intersectionconsulting.comThis visual, inspired by Seth Godin, illustrates 5 pillars of marketing success: Vision, Objectives, Decision Making, Knowledge and Trust.
Graphed in this image are all the items that were featured on the front page then sold within the day, sorted into columns by price. It was generated from data spanning the last two weeks of September 2007 using a program written in Flash AS3.
Please view the original resolution.
Looks like there is a sweet spot at $15, as well as most of other multiples of $5. The sole item in the $0 column, was actually listed as $.20 and rounded down for the graph placement (also known as a P.I.F.).
www.etsy.com is a marketplace to buy and sell handmade goods and is a company I helped co-found in June 2005.
Really interesting visualisation by Nexus: view interactive version
I've added some notes explaining the clusters. They're remarkably distinct.
* The left cluster is personal, the right cluster is work.
* There are 3 sub-clusters in Personal, and 4 sub-clusters in Work
* Jared connects both personal and work clusters. He connects with both Wheel/LBi (where he and I used to work) and Isotoma (where I currently work), and he and his wife became good friends of ours.
* Besides my wife and my brother, there are virtually no family members in the graph. They're not very wired.
* I've lost touch with nearly all people I knew in school, and most of those I knew in uni
* I tend to add only people I know fairly well in real life, and very rarely clients
Nexus also shows you what you have in common with people in your network (Interests and Groups), ordered by the number of similarities. In my cases mostly Interests since I don't tend to join Groups. (Interests are fuzzy and unreliable.) Interestingly, the person at the top of my similarity scale is one of the outliers, Mary, whom I only know through Flickr.
Would love to see something like this for Twitter. TwitterAnalyzer is similar, but does not do the same kind of clustering. Also want this for Linkedin and Flickr
PROJECT:Jinhui Park
DESIGNED BY SCDRI
RENDERED BY FRONTOP
Frontop creates 3d rendering, architectural rendering, architectural visualization and architectural animation for architects, designers, real estate developers and much more.
sent to my group www.flickr.com/groups/abc-visualized for the letter C: 1. Happy Furry weekend..., 2. Maiden Flight Concorde 002, 3. Colliding planes, 4. Untitled
I'd like to say hola! to my most frequent visitors, friends, family and not so friends but I really appreciate your kind support. I'm including the ones I know they silently come time to time. Also hello to my 4 well known spies who decided to choose the dark side of the force. I guess you're planning your holidays. I'll go to Mallorca and Ibiza soon. Well, this is not completely true yet but I need to visualize it to make it real! ;-D
This song always cheer me up. I don't know what it says... It sounds like Disney into me and I don't know why. I hope you're having a good time!
Listening...
www.goear.com/listen/f083f46/LDN-Lily-Allen
<3
I was astounded by Bill Rankin's map of Chicago's racial and ethnic divides and wanted to see what other cities looked like mapped the same way. To match his map, Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Gray is Other, and each dot is 25 people. Data from Census 2000. Base map © OpenStreetMap, CC-BY-SA
CMS utilizes a distributed infrastructure of computing centers to provide access to data stored on disk only at Tier-2 centers and tape with disk caches at Tier-1 centers. Attached are CPU resources for organized processing and analysis. Data is organized in datasets which consist of files grouped in blocks for performance reasons. CMS uses it's data transfer system PhEDEx, to transfer datasets from site to site and its data bookkeeping service DBS to track location and metadata. Integrated over the whole system, even in the first year of data taking, the available disk storage approaches 10 petabytes of space. Maintaining consistency between the data bookkeeping service, the data transfer system, and physical storage is an important operational task which guarantees uninterrupted data availability.
This is a Social network anlysis from www.linkedinlabs.com/inmaps of my LinkedIn contacts. The color coding corresponds to different groups that I know, and how the tool classifies them. (I will say that it is remarkably correct)
LinkedIN20120610a
Creative Commons licensed image via R-chie: a web server and R package for visualizing RNA secondary structures by: Daniel Lai, Jeff R. Proctor, Jing Yun, Irmtraud M. Meyer
Nucleic Acids Research, Vol. 40, No. 12. (01 July 2012), pp. e95-e95, dx.doi.org/10.1093/nar/gks241
FiGURE 1:
An example of a ‘double structure arc diagram’, showing the Cripavirus Internal Ribosomal Entry Site [family RF00458 from the RFAM database]. The RNA secondary structure shown above the horizontal sequence line has been predicted by TRANSAT. Every arc corresponds to one base pair whose colour indicates its P-value, where dark blue is ≤1e-06, light blue is ≤1e-05, orange is ≤1e-04 and red is ≤1e-03 (P-value threshold). The RNA structure shown below the horizontal sequence line shows the consensus RNA structure from RFAM.
ABSTRACT
Visually examining RNA structures can greatly aid in understanding their potential functional roles and in evaluating the performance of structure prediction algorithms. As many functional roles of RNA structures can already be studied given the secondary structure of the RNA, various methods have been devised for visualizing RNA secondary structures. Most of these methods depict a given RNA secondary structure as a planar graph consisting of base-paired stems interconnected by roundish loops. In this article, we present an alternative method of depicting RNA secondary structure as arc diagrams. This is well suited for structures that are difficult or impossible to represent as planar stem-loop diagrams. Arc diagrams can intuitively display pseudo-knotted structures, as well as transient and alternative structural features. In addition, they facilitate the comparison of known and predicted RNA secondary structures. An added benefit is that structure information can be displayed in conjunction with a corresponding multiple sequence alignments, thereby highlighting structure and primary sequence conservation and variation. We have implemented the visualization algorithm as a web server R-CHIE as well as a corresponding R package called R4RNA, which allows users to run the software locally and across a range of common operating systems.
For a nice comparison, this is a graph originally done when Etsy was 2 months old. It shows all registered Etsy users with avatars on August 11, 2005. Ordered from top left to bottom right by date of registration.
See the same visualization for the month of October 2007.
2017 DownUnder Championships
Australia + New Zealand + USA
Griffith University Athletics Track
Gold Coast
Australia
Good grief...!!! This reminds me of the walls of my room growing up. We weren't allowed to put posters up, but I won a B&W poster of Tarzan at the State Fair and it was all over from there. By the time I moved out, my room was one giant Vision Board with the walls and ceiling completely covered!
I did my first Vision Board when I was 10. You know me, I still have it somewhere. It is all about women's fashion a la 1970 and is on purple construction paper. This was before I knew I would have a purple room and spend many years of my career in women's and men's fashion. So there must be something to the concept of a Vision Board and the achievement of one's future dreams.
Now, my Vision Board isn't so much about having material things. That's ok and I already have enough things. It's more about how I aspire to be and the time I would like to have to do it all.
In the instructions, they say to not worry about being artistic. How do you tell an artistic person to not be artistic...lol? And they say to put it in a place where you can see it often. So there you have it!
Now, I've got to go clean my room. Or NOT!!!
Thank you, Joe for letting me use the pic of me. One reason I love this pic is because it was taken in front of the statue of Columbus. Someone who had a definite vision of where he wanted to go. . .
Please!! NO Awards or Large Graphics...Group Buddy Icons are OK. Thank You!
© CPMcGann. All rights reserved. If you are interested in using my images, please contact me first.
Maps of racial and ethnic divisions in US cities, inspired by Bill Rankin's map of Chicago, updated for Census 2010.
Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Yellow is Other, and each dot is 25 residents.
Data from Census 2010. Base map © OpenStreetMap, CC-BY-SA
After seeing Cooper Smith's visualizations of data from runners in New York City, I wanted to see what similar data sets would look like for other cities. Nike+ doesn't have public GPS logs, but MapMyRun does, if you are willing to spend several hours clicking through search results to hit the "Download" buttons, so that's what I did to get the tracks for these 771 runs (from June 13 through August 9) in San Francisco.
As Open Source Planning has pointed out, uploaded runs come from a fairly small, self-selected group of people, the most obvious result of which is the total absence of the southeastern corner of the city from this map. It is also a very self-conscious process, so it is biased toward intentional, and often intentionally difficult, trips made for their own sake, and away from the repetitive patterns of everyday life.
Unfortunately the MapMyRun tracklogs do not have date and time stamps, so it is not possible to do the time of day, pace, and interruption analyses that Cooper Smith did. I should have done direction of travel, though.
I was astounded by Bill Rankin's map of Chicago's racial and ethnic divides and wanted to see what other cities looked like mapped the same way. To match his map, Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Gray is Other, and each dot is 25 people. Data from Census 2000. Base map © OpenStreetMap, CC-BY-SA
8-9 hours of sleep is recommended to feel comfortable. However, 3 hours are enough in emergency. Most important is to awake in time.
This is a visualization of the frequency of occurrence of the words 'internet' , 'web', and 'twitter' in the New York Times, from 1990 - 2008.
Built with Processing (http://www.processing.org)
Prints from this and other NYTimes visualizations are available on my Etsy store: blprnt.etsy.com
I've been having some issues with our MoMA-bound Cabspotting visualization lately, and, as is often the case, ended up having to create another visualization just to figure out what the problem was.
Each of the white dots represents a discreet data sample–the location of a specific cab at a particular time. Here, samples for each cab are placed on a separate row and arranged temporally from left to right. More "active" cabs (i.e., the ones with more available samples) are placed at the top.
The green and red marks at the top represent the start and end times of the displayed period. For each cab, an algorithm seeks through the list of segments between each sample that fall within them. The hue corresponds to the position in the line between the start and end of the period: Green lines are closer to the start time, red ones to the end time.
So, what does it show? Primarily, that there is quite a bit of "bad" data in our set. Those long lines at the bottom indicate extended periods of time during which those cabs weren't transmitting their locations. Most cabs tend to ping the depot every 30-60 seconds, but some do it less than once per hour. For the most part, though, the consistency of that green-to-red column seems to indicate that we've got a pretty good idea of where most of the cabs were in that time period, and with a reasonable degree of resolution.
God, I'm such a geek.