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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)
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....
They show the relationships between the 99 bowls of the exhibition. The squares on the left are adjancency matrices that reveal thematic clusters among the bowls. The ones on the right are relationship graphs based on a hexagonal grid.
Link to the exhibition: www.skd.museum/en/special-exhibitions/the-things-of-life-...
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.
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
Functional notation is only available for a subset of functions. Here is an alternative syntax for factoring and expanding polynomials.
Functions can also be plotted by generating a line that approximates the function. In fact, this is what happens behind the scenes when the 'plot' function is called.
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.
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
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
If you look deep in to the image, you can visualize alot of funny stuff from this image...give it a try! :)
What you see from distance in the image is a sand bank with small trees which is eventually going to be an island.
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. This plot shows voltage versus time, there are about 88,000 samples in this data. To figure out the "spikes", a threshold is applied (in voltage and time) to separate the spike from the noise. The red lines were drawn for every spike detected.
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).
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
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
Paper at www.stat.columbia.edu/~gelman/research/published/signif4.pdf (PDF)
Let's say that a country, Lagutrop, wants to increase its PISA results and, before deciding on policy, the decision-makers want to know what works by running experiments or studies of intervention effectiveness. These studies compare two variables with similar intervention scales, say expenditure in computer-assisted learning (U) and expenditure in teacher selection and incentive systems (V).
In Study 1, data is collected measuring the effect of U and V (possibly with a lot of covariates). The parameter estimates for the effect of U and V are given by the two bell-curve-like curves on the left above.
Conclusion (as is traditionally presented): Lagutrop should invest in teacher selection and incentive systems, since computer-aided education has no significant effect.
Gelman and Stern problem 1: but the difference between the effects is itself non-significant! If significance is the criterion for disposing of U, then it should also be explained to the decision-makers that significance cannot be used to separate U from V. Specifically, policy-makers in Lagutrop should be told that the rejection of computer-aided education is based on a criterion that also suggests that computer-based education is as effective as teacher recruitment and incentives.
Meanwhile, another group of researchers run a single-variable study (Study 2) considering only the effects of spending money on teachers (in Lagutrop this study would probably have been done by teachers :-).
The results of Study 2 are then presented as supporting the conclusions of Study one, phrased as "Expenditure on teachers shows a significant effect on PISA scores in both studies."
Gelman and Stern problem 2: Studies 1 and 2 predict very different effect sizes for variable V; why the discrepancy? How can two parameter estimates that are significantly different from each other be considered corroboration?
My own take on this problem 2 is the following: suppose the policy-makers in Lagutrop have to decide how much to allocate to this PISA-improvement project, out of a budget that includes other considerations (national defense, jobs for the families and friends of the politicians, police, fire-fighters, etc.). Budgeting will require forecasting. Which of the parameter estimates for effect size will they use to build a forecasting model? Since the two estimates are significantly different, any attempt at aggregation would violate the basic meaning of that significance.
That's what we engineers call a serious execution problem.
(Reblogged at my personal blog.)
The last four years of my data from Facebook: gadgetwise.blogs.nytimes.com/2010/10/06/facebook-now-lets...
Visualized with GrandPerspective: grandperspectiv.sourceforge.net/