2615 search results for "gis"

Rook rocks! Example with googleVis

August 1, 2012
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Rook rocks! Example with googleVis

What is Rook?Rook is a web server interface for R, written by Jeffrey Horner, the author of rApache and brew. But unlike other web frameworks for R, such as brew, R.rsp (which I have used in the past1), Rserve, gWidgetWWWW or sumo (which I haven't used...

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The Environmental Performance Index, visualized with R

July 31, 2012
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The Environmental Performance Index, visualized with R

The Environmental Performance Index (EPI) ranks countries on performance indicators for environmental public health and ecosystem vitality. Yale University hosts the EPI website, which was used to present the 2012 EPI Rankings to world leaders at the 2012 World Economic Forum at Davos. The Country Profiles section of the website allowed members to browse the performance characteristics of their...

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Application of Horizon Plots

July 31, 2012
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Application of Horizon Plots

for background please see prior posts Horizon Plot Already Available and Cubism Horizon Charts in R Good visualization simplifies, and stories are better told with effective and pretty visualizations. Although horizon plots are not immediately intuitiv...

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Edge Prediction in a Social Graph: My Solution to Facebook’s User Recommendation Contest on Kaggle

July 31, 2012
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Edge Prediction in a Social Graph: My Solution to Facebook’s User Recommendation Contest on Kaggle

A couple weeks ago, Facebook launched a link prediction contest on Kaggle, with the goal of recommending missing edges in a social graph. I love investigating social networks, so I dug around a little, and since I did well enough to score one of the coveted prizes, I’ll share my approach here. (For some background, the contest provided...

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Big data, big analytics, big opportunity

July 30, 2012
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Big data, big analytics, big opportunity

Data, data, every where Nor any byte to think The world today is awash with data. Corporations, governments, and individuals are busy generating petabytes of data on culture, economy, environment, religion, and society.  While data has become abundant and ubiquitous, data analysts needed to turn raw data into knowledge are in fact in short...

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Forecasting the Olympics

July 30, 2012
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Forecasting sporting events is a growing research area. The International Journal of Forecasting even had a special issue on sports forecasting a couple of years ago. The London 2012 Olympics has attracted a few forecasters trying to predict medal counts, world records, etc. Here are some of the articles I’ve seen. Which Olympic records get shattered?, Nate Silver, New...

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Machine learning for better homicide counts in Ciudad Juarez

July 30, 2012
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Machine learning for better homicide counts in Ciudad Juarez

Photo Credit: Jesús Villaseca Pérez Ever since March 2008 Ciudad Juárez began to register an alarming number of homicides becoming Mexico's most violent city. According to the Mexican vital statistics system Ciudad Juárez (coterminous with the Juárez municipality) went from having just 202 murders in 2007 to 1,616 in 2008, 2,397 in...

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unsupervised classification of a raster in R: the layer-stack or part one.

July 29, 2012
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unsupervised classification of a raster in R: the layer-stack or part one.

In my last post I was explaining the usage of QGis to do a layerstack of a Landsat-scene. Due to the fact that further research and trying out resulted in frustration I decided to stick with a software I know well: R. So download the needed layers here and open up your flavoured version of

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Hi R and Axys, I’m d3.js “Nice to Meet You” (On the Iphone)

July 27, 2012
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Hi R and Axys, I’m d3.js “Nice to Meet You” (On the Iphone)

I am still definitely in the proof of concept stage, but as I progress I get more excited about the prospects of combining d3.js with R and Axys through Bryan Lewis’ really nice R websockets package (even nicer now that he has added the daemonize fun...

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Linear regression by gradient descent

July 26, 2012
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Linear regression by gradient descent

In Andrew Ng's Machine Learning class, the first section demonstrates gradient descent by using it on a familiar problem, that of fitting a linear function to data. Let's start off, by generating some bogus data with known characteristics. Let's make y just a noisy version of x. Let's also add 3 to give the intercept term something to...

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