View allAll Photos Tagged Visualization
VISUALIZE IT!
A. Hoffman Awning Company in Baltimore Maryland, has been designing awnings and canopies for 100 years. This is an example of a rendering shown to customers and architects as to how their awning is actually going to look on their building or house. We design awnings and canopies and serve Maryland,
Northern Virginia, Washington D.C. and Pennsylvania. (York and Lancaster Pennsylvania)
Hoffman Awning Baltimore Maryland
5113 Belair Rd.
Baltimore, Maryland 21206
410-685-5687
E-mail: info@ahoffmanawning.com
5113 Belair Rd.
Baltimore, Md. 21206
Link back to Gallery: www.flickr.com/photos/hoffmanawning/sets/
Worldwide Visualization for a Breakthrough -
Please Join Us!
visualizedaily.com/action1-en.html
Transformation transformacja transformation transformace Transformation transzformáció преобразование transformación trasformazione 2012
www.flickr.com/photos/arjuna/sets/72157628371178639/with/...
First Hacks/Hackers Meetup held at Atherton Studio at HPR. Great presentations by Ben Trevino, Jared Kuroiwa and Misa Maruyama.
3d-walkthrough-rendering.outsourcing-services-india.com
Yantram Architectural photorealistic renders creates high-quality 3D facades in a virtual studio environment. Our team of architects and industrial designers build 3d models from CAD files, sketches, or photographs
This project was an attempt to visualize population density in NYC and the correlation it has to people feeling claustrophobic as a result. As well, it was also a reflection upon my own relationship to the city and the daily patterns that shape it.
I placed a proximity sensor in the front pocket of my jacket and logged the data it recorded for the 4+ hours I wore it. I then pulled that data into Processing to manipulate the image. As the distance between me and anything/anyone in front of me became closer, the image begins to blur.
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.
Data visualizations for earthquakes that killed more than 1K people, 1902-2008.
Source:
(1) spreadsheets.google.com/ccc?key=0AgdO92JOXxAOdFpmY2IzS0JC...
Architectural visualization of Minimalist House
Architects: Shinichi Ogawa & Associate
Location: Okinawa, Japan
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).
Illustrative Visualization of a german climate change adaption research network – using processing and a metaball force field fpr moving agents
A look at mobile traffic trends by website type. Data comes from sites that SwellPath has engagements with. See the blog post that goes with it here: www.swellpath.com/2010/10/mobile-traffic-website-type-inf...
This is a java applet produced using Processing that visualizes my personal friends network from Facebook. It clearly shows the different groups from schools that have attended over the years. The java applet looks a bit worse and runs slower than the standalone application, but it gives a pretty good idea of the project
TwitterGraph of Twitter user Molly_Ultra
generated by:
bradkellett.com/twitter_stats.html
As the software author describes it, a "totally ugly engine" - but once you start to think about the data that's out there - Twitter or otherwise - you start to think about all the ways this data could be visualized.
Can anybody recommend other engines peeps have written to viz network data?
Note that the graphs are labeled "Tweets per Day" and "Tweets per Hour" -- I think it really means "BY" not "PER" as in "40 of your Tweets came on Mondays" - not, your "average" Monday had 40 Tweets.
Visualizing the various features of the SwiftRiver distributed reputation and veracity functionality.
Click for full view. Go here for bigger image: samismyth.deviantart.com/#/d4mswda
Made for an Information is Beautiful Challenge, they provided the raw data, sources they used are listed below smoke stack. Concept and execution is mine. I also researched the uses and decided to visualize carbon emissions (black smog).
Key is on the left. Height dimension is years remaining until we are out of *known reserves currently economic to extract*, without recycling (many elements not recycled at present), assuming production growth continues to rise at current rate (lets hope not.)
Top width based on total known reserves, using log scaling. Bottom width based on amount used last year (also log scaled).
Three main uses for each element shown in factory line boxes. Smoke outline is the usual carbon emissions graph flipped onto a vertical axis (so that date is the vertical axis.) Dates only apply to the carbon emissions (note the drop after the Great Depression circa 1929.)
Request permission from me before any reproduction.