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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.
Film studios like to compare movies at the box office. However, due to inflation, tickets price goes up and new movies have an advantage. Considering inflation many latest blockbusters are significantly inferior to the old movies.
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.
You can see what remains of a ledge where the Freemont people likely stood a 1,000 years ago to carve the figures in the stone. Sadly, the ledge has lasted to current times so it enables people to vandalize the ancient symbols.
Kunal Anand was kind enough to do some crazy ass Python/Processing hack to create a cluster of all my tags and how they interoperate. Looks cool and cloudy.
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
I had a goal to walk 5000 km (3107 miles) in 2015. I ended up exceeding my goal as I covered 5016 km (3117 miles) in 2015. This meant I needed to average 13.7 km (8.52 miles) a day. I would track my mileage every day.
Fitness2015histo
This is a visualization of the frequency of occurrence of the words 'internet' , 'web', and 'twitter' in the New York Times, from 1990 - 2009.
Interesting here is the very steep rise in mentions of Twitter so far in 2009. Compare the leading edge of the Twitter curve to both web and internet - it is clearly on a steeper climb.
Compare this image to one made in February, to see the very clear 'Twitter explosion' -
www.flickr.com/photos/blprnt/3256480403/in/set-7215761338...
Built with Processing (http://www.processing.org)
This is a visualisation of my Facebook friends. I have created it as part of an assignment for "Social Network Analysis" -- an online course run by the University of Michigan.
The circles represent people, and the lines represent friendships. Darker circles indicate people with higher number of friendships.
Inspired from Whole Foods CEO John Mackey's excellent speech about Spiral Dynamics entitled “The Upper Flow of Human Development.”
I really like how Mackey describes each value system meme with bulleted lists describing the unique Characterstics, How they Make Decisions, Education, Family, Community & Life Space.
The only problem with the layout of all of this information in a linear fashion is that it has been really hard to compare and contrast the different vMemes with each other. That was why I created a Cheat Sheet Graphic with all of the six categories and characteristics in one big massive table.
More details here
Archived at web.archive.org/web/20060910031642/http://www.wholefoods....
This is the A3 version (300 dpi) of the final uberinfographic. The uberinfograhic is an overview of over 365 beautiful infographics and visualizations. The core of this overview is an infographic in itself, a schematic that structures all infographics and visualizations.
interactive version of my former work. it's build with actionscript. you can play with it at blob.creanode.com/blob/eu2009/ if you want.
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
Functional notation is only available for a subset of functions. Here is an alternative syntax for factoring and expanding polynomials.
Viva apartments visualizations created for Adele Bates' interior design project in Brighton.
Software used: 3ds Max, Corona and Photoshop
sent to my group www.flickr.com/groups/abc-visualized for the letter B: 1. big small, 2. Boat floating on clouds, 3. harmony . . ., 4. Playin_de_Blues
Sage contains interfaces to most common mathematics software including Mathematica, Maple, and Matlab.
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
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
When taking photos, while the subject isn't ready, can lead to some very intriguing and interesting photos. Here for example, the hair and eyelashes were better emphasized than if a direct portrait were to be taken. A prism was also used in the bottom right corner to add a little bit of depth and effects into the photo.
I just started an excellent online class via Coursera titled Exploring Neural Data Exploring Neural Data that started 29 September 2014. Monica Linden and David Sheinberg of Brown University are teaching this class. Our first assignment was to detect spikes (action potentials) in a few seconds of recorded data from a neuron. The assignment was to modify some Python code to plot the data, and then detect and plot the spike activity, and plot all the detected spikes.
The data plot above shows the detected spikes "stacked up". The detection of each plot occurred at time 0, and the plot displays the time domain behavior of the spike from 3 milliseconds before the spike until 3 milliseconds after.
In this data, there were 79 actual spikes, and the detector I made found 68 of them correctly. These spikes all have a similar shape, so they stack one on top of the other with a bit of jitter. Some of that jitter may be due to the sampling time of 34.4 microseconds (which means there are 29 samples in one millisecond.)
Unfortunately, my detector also found about 11 false spikes in this data set. In a simpler data set, it ran correctly detecting 10 of 10 spikes and not detecting any false spikes. You can see some of these, they don't fit the pattern of the correct spikes.
It was very satisfying to work with this data, and to learn a bit more about neuroscience (and Python). We are using the Spyder environment, which is quite a nice development environment. This assignment took me a little longer than I thought it would (finished about 530 am Sunday morning).
Patrick van der Pijl: On January 4th 2011 Alex Osterwalder reinversed the business model of Facebook. On his blog he reconstructed together with his smart and loyal followers the model of Facebook. We thought it would be great to build on the work that has been done by visualizing this business model. More on www.businessmodelsinc.com - Illustration: Joeri Lefevre