APEXvj - Visualize your favourite tunes online blog » Hexbins! Binning is a general term for grouping a dataset of N values into less than N discrete groups. These groups/bins may be spatial, temporal, or otherwise attribute-based. In this post I’m only talking about spatial (long-lat) and 2-dimensional attribute-based (scatterplot) bins. If you’re just after that sweet honey that is my code, bear down on my Github repository for this project — hexbin-js. Rectangular binning The simplest 2D bin is rectangular. The above is a shot from a little example I produced on jsFiddle, while learning Mike Bostock’s fantastic D3 JavaScript library for HTML and SVG data-binding and visualization. Binning can be good for both the users and the creators/developers of static or interactive thematic maps or other visualizations. In the above image (from Antaeus Concepts) the data points are represented in black, and due to overlap the true concentration/density distribution is indiscernible from the graphic. Hexagonal binning Adler writes, Hex history and theory
About Google+ Ripples - Google+ Help Google+ Ripples creates an interactive graphic of the public shares of any public post or URL on Google+ to show you how it has rippled through the network and help you discover new and interesting people to follow. Ripples shows you: People who have reshared the link will be displayed with their own circle. Inside the circle will be people who have reshared the link from that person (and so on). Circles are roughly sized based on the relative influence of that person. The comments users added when they reshared a link are displayed in the sidebar of Ripples. At the bottom of the Ripples page, you can play an animated version of the visualization that shows how the link was shared over time. Beneath the timeline on the Ripples page statistics on the link. While Ripples displays a lot of cool information, you’re not actually seeing all the action that’s taken place. Not sure if a post is public?
d3.js ITO - Road Fatalities USA This web site and the information it contains is provided as a public service by ITO World Ltd, using data supplied by the National Highway Traffic Safety Administration (NHTSA), U.S. Department of Transportation (DOT). ITO World Ltd makes no claims, promises or guarantees about the accuracy, completeness, or adequacy of the contents of this web site and expressly disclaims liability for errors and omissions in the contents of this web site. No warranty of any kind, implied, expressed or statutory, including but not limited to the warranties of non-infringement of third party rights, title, merchantability, fitness for a particular purpose and freedom from computer virus, is given with respect to the contents of this web site or its links to other Internet resources. Users of the service should note that the NHTSA/DOT makes no claims, promises or guarantees about the accuracy, completeness, or adequacy of the road fatality data used within this web site.
Data Science Toolkit Gephi, an open source graph visualization and manipulation software Protovis Protovis composes custom views of data with simple marks such as bars and dots. Unlike low-level graphics libraries that quickly become tedious for visualization, Protovis defines marks through dynamic properties that encode data, allowing inheritance, scales and layouts to simplify construction. Protovis is free and open-source, provided under the BSD License. Protovis is no longer under active development.The final release of Protovis was v3.3.1 (4.7 MB). This project was led by Mike Bostock and Jeff Heer of the Stanford Visualization Group, with significant help from Vadim Ogievetsky. Updates June 28, 2011 - Protovis is no longer under active development. September 17, 2010 - Release 3.3 is available on GitHub. May 28, 2010 - ZOMG! October 1, 2009 - Release 3.1 is available, including minor bug fixes. September 19, 2009 - Release 3.0 is available, including major performance improvments, bug fixes, and handy utilities such as scales and layouts. Getting Started How does Protovis work?
MALLET homepage MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. MALLET includes sophisticated tools for document classification: efficient routines for converting text to “features”, a wide variety of algorithms (including Naïve Bayes, Maximum Entropy, and Decision Trees), and code for evaluating classifier performance using several commonly used metrics. Quick Start / Developer’s Guide In addition to classification, MALLET includes tools for sequence tagging for applications such as named-entity extraction from text. Topic models are useful for analyzing large collections of unlabeled text. Many of the algorithms in MALLET depend on numerical optimization. In addition to sophisticated Machine Learning applications, MALLET includes routines for transforming text documents into numerical representations that can then be processed efficiently.
The Overview Project » How Overview turns Documents into Pictures Overview produces intricate visualizations of large document sets — beautiful, but what do they mean? These visualizations are saying something about the documents, which you can interpret if you know a little about how they’re plotted. There are two visualizations in the current prototype version of Overview, and both are based on document clustering. The first is the items plot, which grew out of the proof-of-concept system we presented a year ago. Every document is a dot. Overview also has a “tree” view. The tree view and the items plot show the same thing, just in different ways. Extracting Key Words All of Overview’s clustering depends on grouping similar documents together, but what does that mean? But Overview doesn’t know any of this. Two documents are similar if they have overlapping sets of key words. Where do those documents go? The tree view finds not only clusters but sub-clusters. For more information, see the discussion of our WikiLeaks visualization.