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Barbara Lindsey

If San Francisco Crime were Elevation | Doug McCune - 0 views

  • Really nice. Be great to see the two combined – heatmaps and topography or atleast some kind of colour banding added to the topography. That would open up all kinds of possibilities – you could slice horizontally along the bands and create layers of different ranges. In fact mixing colour and topography would also give you a way of showing two sets of data concurrently – topography for prostitution and some kind of colour banding for wealth for example.
  • Makes the numbers come alive. G
  • Brilliant work! Can you cross this data with the physical typography? I’ve always been curious if safer neighborhoods are uphill.
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  • It would be interesting to pull the data in from previous decades and see how the elevation has changed in different areas.
  • @adrian – it’s just raw totals, grouped geographically. These aren’t scientific by any means, I basically took the underlying pattern and extruded it out and smoothed it a bit to make it look “pretty”. But basically each image is the aggregate numbers for a single year of crime data.
  • @richard – yes, there is some smoothing in effect, which means that the ridge along Shotwell St (for the prostitution map) is indeed a bit smoothed between peaks. That’s not to say that there are only two peaks at Shotwell and 19th and Shotwell and 17th. There are incidents in between as well, but the big peaks at those major intersections does mean that the ridge between them appears higher than the actual incidents along those blocks support. A lot of people have commented on the usefulness of maps like these. I want to stress once again: this was done as an art project much more than a useful visualization. My goal was not to provide useful information that one could act on.
  • “one trick pony. these maps add nothing of value to a standard color plot.” I disagree: allowing for a third dimension of elevation makes the reality of concentration clearer – and half the point of crime mapping is to measure concentration, not simply “intensity.”
  • Great idea and nice work on the graphics, but there are at least three improvements you should make to reveal *true* patterns. Forgive me if you already did these. 1) Availability bias – normalize for population density (i.e. per capita activity) 2) Sampling bias – normalize for the number of cops on the beat (geographic and crime type) 2) Frame bias – break it up by daytime and night time
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    Visual representation of various crime stats from San Francisco
Barbara Lindsey

Talk with Media - home Visual Literacy & Digital Storytelling - 0 views

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    Talk with Media: Visual Literacy and Digital Storytelling
Barbara Lindsey

A visual contrast in old and new « Moving at the Speed of Creativity - 0 views

  • I can’t imagine living without being able to see what is going on around the world from my living room.
Barbara Lindsey

Opportunities for Creating the Future of Learning - 2020 Forecast: Creating the Future ... - 0 views

  • It remains to be seen whether new learning agents and traditionally certified teachers will cooperate or compete.
  • Secondly, it emphasizes the need for learning to be an ongoing process whereby we all become engaged citizens of a global society. T
  • By embracing technologies of cooperation, prototyping new models of learning, and cultivating open and collaborative approaches to leadership, “amplified” educators and learners will become the organizational “superheroes” of schools and districts.
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  • The globalization of open learning systems characterized by cooperative resource creation, evaluation, and sharing will change how educational institutions view their roles and will offer new forms of value in the global learning ecosystem.
  • The result will be an emerging toolset for designing personalized, learner-centered experiences and environments that reflect the differentiation among learners instead of forcing compliance to an average learning style and level of performance.
  • As the hierarchical structure of education splinters, traditional top-down movements of authority, knowledge, and power will unravel. Before new patterns get established, it will seem as if a host of new species has been introduced into the learning ecosystem. Authority will be a hotly contested resource, and there will be the potential for conflict and distrust.
  • Learning geographies will be accessible to communities through a range of key tools, such as data aggregated from disparate sources, geo-coded data linking learning resources and educational information to specific community locations, and visualization tools that help communicate such information in easily understood visual and graphic forms. Such information will often contain multiple layers of data (for example, school performance statistics, poverty rates, and the degree of access to fresh food).
  • These new dimensions of learning geographies will require new core skills. Among them will be navigating new visual cartographies, identifying learning resources in previously unexpected places, leveraging networks to take advantage of learning opportunities, and creating flexible educational infrastructures that can make use of dispersed community resources. Through enhanced visibility and accessibility, learning geographies will bring new transparency to issues of equity in learning.
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    By embracing technologies of cooperation, prototyping new models of learning, and cultivating open and collaborative approaches to leadership, "amplified" educators and learners will become the organizational "superheroes" of schools and districts.
Barbara Lindsey

Becoming a (More) Socialized Organization: 10 Tips for UNICEF to Tap the Social Web 04 ... - 0 views

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    An example of a more visual powerpoint presentation
Barbara Lindsey

My Mom's on Facebook? / Flowtown (@flowtown) - 0 views

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    Visual representation of various stats related to social media participants
Barbara Lindsey

2010 Horizon Report » Technologies to Watch - 0 views

  • The near-term horizon assumes the likelihood of entry into the mainstream for institutions within the next twelve months; the mid-term horizon, within two to three years; and the far-term, within four to five years. It should be noted that the Horizon Report is not a predictive tool. It is meant, rather, to highlight emerging technologies with considerable potential for our focus areas of teaching, learning, and creative inquiry.
  • virtually all higher education students carry some form of mobile device, and the cellular network that supports their connectivity continues to grow. An increasing number of faculty and instructional technology staff are experimenting with the possibilities for collaboration and communication offered by mobile computing. Devices from smart phones to netbooks are portable tools for productivity, learning, and communication, offering an increasing range of activities fully supported by applications designed especially for mobiles.
  • Far more than a collection of free online course materials, the open content movement is a response to the rising costs of education, the desire for access to learning in areas where such access is difficult, and an expression of student choice about when and how to learn.
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  • Already in the mainstream of consumer use, electronic books are appearing on campuses with increasing frequency.
  • applications for laptops and smart phones overlay digital information onto the physical world quickly and easily.
  • Devices that are controlled by natural movements of the finger, hand, arm, and body are becoming more common. Game companies in particular are exploring the potential offered by consoles that require no handheld controller, but instead recognize and interpret body motions.
  • Visual data analysis is an emerging field, a blend of statistics, data mining, and visualization, that promises to make it possible for anyone to sift through, display, and understand complex concepts and relationships.
Barbara Lindsey

Vyew - 0 views

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    Continuous meeting rooms for real-time & anytime visual collaboration
Barbara Lindsey

Failed Tech Predictions | Visual.ly - 0 views

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    fall 2011 syllabus
Barbara Lindsey

Syntax Untangler | University of Wisconsin-Madison - 1 views

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    Syntax Untangler is an online activity that asks the learner to visually mark up a short primary text in any language, in order to improve small-scale reading skills. Any instructor can easily create and publish their own Syntax Untangler content (go to the Instructor Tools link below).
Barbara Lindsey

What we learned from 5 million books | Video on TED.com - 0 views

    • Barbara Lindsey
       
      From YouTube version of this talk: "[Google's digtized books] are very practical and extremely awesome." Erez Lieberman Aiden and Jean-Baptiste Michel from Harvard University use the 15 million books scanned and digitized by Google to show how a visual and quantitative analysis of text can provide insights about fields as diverse as lexicography, the evolution of grammar, collective memory, the adoption of technology, the pursuit of fame, censorship, and historical epidemiology.
  • ELA: There are more sobering notes among the n-grams. For instance, here's the trajectory of Marc Chagall, an artist born in 1887. And this looks like the normal trajectory of a famous person. He gets more and more and more famous, except if you look in German. If you look in German, you see something completely bizarre, something you pretty much never see, which is he becomes extremely famous and then all of a sudden plummets, going through a nadir between 1933 and 1945, before rebounding afterward. And of course, what we're seeing is the fact Marc Chagall was a Jewish artist in Nazi Germany. Now these signals are actually so strong that we don't need to know that someone was censored. We can actually figure it out using really basic signal processing. Here's a simple way to do it. Well, a reasonable expectation is that somebody's fame in a given period of time should be roughly the average of their fame before and their fame after. So that's sort of what we expect. And we compare that to the fame that we observe. And we just divide one by the other to produce something we call a suppression index. If the suppression index is very, very, very small, then you very well might be being suppressed. If it's very large, maybe you're benefiting from propaganda.
  • Now when Google digitizes a book, they put it into a really nice format. Now we've got the data, plus we have metadata. We have information about things like where was it published, who was the author, when was it published. And what we do is go through all of those records and exclude everything that's not the highest quality data. What we're left with is a collection of five million books, 500 billion words, a string of characters a thousand times longer than the human genome -- a text which, when written out, would stretch from here to the Moon and back 10 times over -- a veritable shard of our cultural genome.
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  • we're going to release statistics about the books. So take for instance "A gleam of happiness." It's four words; we call that a four-gram. We're going to tell you how many times a particular four-gram appeared in books in 1801, 1802, 1803, all the way up to 2008. That gives us a time series of how frequently this particular sentence was used over time. We do that for all the words and phrases that appear in those books, and that gives us a big table of two billion lines that tell us about the way culture has been changing.
  • You might also want to have a look at this particular n-gram, and that's to tell Nietzsche that God is not dead, although you might agree that he might need a better publicist.
  • JM: Now you can actually look at the distribution of suppression indexes over whole populations. So for instance, here -- this suppression index is for 5,000 people picked in English books where there's no known suppression -- it would be like this, basically tightly centered on one. What you expect is basically what you observe. This is distribution as seen in Germany -- very different, it's shifted to the left. People talked about it twice less as it should have been. But much more importantly, the distribution is much wider. There are many people who end up on the far left on this distribution who are talked about 10 times fewer than they should have been. But then also many people on the far right who seem to benefit from propaganda. This picture is the hallmark of censorship in the book record.
  • ELA: So culturomics is what we call this method. It's kind of like genomics. Except genomics is a lens on biology through the window of the sequence of bases in the human genome. Culturomics is similar. It's the application of massive-scale data collection analysis to the study of human culture. Here, instead of through the lens of a genome, through the lens of digitized pieces of the historical record. The great thing about culturomics is that everyone can do it. Why can everyone do it? Everyone can do it because three guys, Jon Orwant, Matt Gray and Will Brockman over at Google, saw the prototype of the Ngram Viewer, and they said, "This is so fun. We have to make this available for people." So in two weeks flat -- the two weeks before our paper came out -- they coded up a version of the Ngram Viewer for the general public. And so you too can type in any word or phrase that you're interested in and see its n-gram immediately -- also browse examples of all the various books in which your n-gram appears.
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    fall 2012 syllabus
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