Global temperature rises over past century

Boa visualização do aumento de temperaturas médias mundial

Boa visualização do aumento de temperaturas médias mundial

New Scientist mapped global temperature change based on a NASA GISTEMP analysis.

The graphs and maps all show changes relative to average temperatures for the three decades from 1951 to 1980, the earliest period for which there was sufficiently good coverage for comparison. This gives a consistent view of climate change across the globe. To put these numbers in context, the NASA team estimates that the global average temperature for the 1951-1980 baseline period was about 14 °C.

The more red an area the greater the increase was estimated to be, relative to estimates for 1951 to 1980 (especially noticeable in the Northern Hemisphere).

The most interesting part is when you compare all the way back to to the 19th century when it was much cooler. You can also click on locations for a time series of five-year averages.

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Creating Animations and Transitions With D3

Construção de visualizações de dados em java

Construção de visualizações de dados em java

In interactive visualisation, there is the word reactive. Well, maybe not literally, but close enough.

The fact is that reactivity, or the propension of a visualisation to respond to user actions, can really help engage the user in a visualisation, and help them understand its results. Both of which are usually good things. How can this reactivity be achieved? Through animations.

So I’ll go ahead and state that animation, if done right, can make any interactive data visualization better.

How is that?

  • When coupled with interaction, it’s a very useful way to give feedback to the user. What has changed since their last command? If what’s on screen animates from one state to another, it’s obvious, it stands out and it makes sense. Or, when showing any form of real-time data, animation is pretty much required.
  • Animation can bring focus on the important things as a chart loads. Our vision is very sensitive to movement, so using these introduction transitions sensibly helps a lot to ease the effort required to get the right information off a chart.

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Handbook of Statistical Analysis and Data Mining Applications

Livro completo no google books com as ligações entre a estatística e o DM

Livro completo no google books com as ligações entre a estatística e o DM

Índice

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Data Mining for Business Intelligence

Livro completo no google books com boa introdução ao data mining

Livro completo no google books com boa introdução ao data mining

Índice

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Novel Views: Les Miserables

Visualizações de dados inovadoras baseadas em text mining

Visualizações de dados inovadoras baseadas em text mining

Jeff Clark took a detailed look at Victor Hugo’s Les Miserables via character mentions, word connections, and word usage. The above is character mentions with color showing sentiment. Red means negative, and blue positive.

Characters are listed from top to bottom in their order of appearance. The horizontal space is segmented into the 5 volumes of the novel. Each volume is subdivided further with a faint line indicating the various books and, finally, small rectangles indicate the chapters within the books. In the 5 volumes there are a total of 48 books and 365 chapters. The height of the small rectangles indicate how frequently that character is mentioned in that particular chapter.

There’s a good amount of blue towards the end, when everyone decides everyone else isn’t so bad.

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Mathematical Programming in MathProg

Interface para resolução de probs PL ou MIP com java

Interface para resolução de probs PL ou MIP com java

GNU MathProg (part of the open source GNU GLPK project) is a modeling language for describing linear and discrete optimization problems. Use this page to create and solve MathProg models using the glpk.js solver. You can either open a model from your computer, select an example from the menu bar, or create your own from scratch. To use —

  1. Create a MathProg model in the Model Editor.
  2. Click ‘Solve’ to generate the solution.
  3. Click ‘Save As…’ to save the model locally to your computer.

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How to: network animation with R and the iGraph

Construir animações de iformação relacional com R

Construir animações de informação relacional com R

This article lists the steps I take to create a network animation in R, provides some example source code that you can copy and modify for your own work, and starts a discussion about programming and visualization as an interpretive approach in research. Before I start, take a look at this network animation created with R and the iGraph package. This animation is of a retweet network related to #BankTransferDay. Links (displayed as lines) are retweets, nodes (displayed as points) are user accounts. For each designated period of time (in this case, an hour), retweets are drawn and then fade out over 24 hours.

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Women as Academic Authors, 1665-2010

Exemplo de representação gráfica interativa com bolhas num eixo

Exemplo de representação gráfica interativa com bolhas num eixo

The Chronicle of Higher Education has a look at the percentage of academic papers published by women, over the past five centuries.

The articles and authors described in this data were drawn from the corpus of JSTOR, a digital archive of scholarly papers, by researchers at the Eigenfactor Project at the University of Washington. About two million articles, representing 1765 fields and sub-fields, were examined, spanning a period from 1665 to 2011. The data are presented here for three time periods, the latest one ending in 2010, and a view that combines all periods.

Percentage of female authors is on the horizontal, and each bubble is a subfield sized by total number of authors. The graphic starts with publishing for all years, but be sure to click on the tabs for each time span to see changes.

The data is based on the archive of about two million articles from JSTOR, and a hierarchical map equation method is used to determine subfields.

The gender classification they used for names seems like it could be nifty for some applications. Gender is inferred by comparing names against the ones kept by the U.S. Social Security Administration, which includes gender. If a name was used for female at least 95 percent of the time, it was classified as a female name, and the same was done with male. Anything ambiguous was not included in the study

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Advanced Statistics

Bons slides e outros materiais sobre clusters, AFE, SEM, reg logistica, meta-análise, MANOVA, Reliability

Bons slides e outros materiais sobre clusters, AFE, SEM, reg logistica, meta-análise, MANOVA, Reliability

Welcome to Malbowges, the part of Nether Hell dominated by thieves, counsellors of Fraud (or should that just be counsellors), falsifiers and sowers of discord. It’s not a nice place for Sunday lunch. You must wade through rivers of Lucifer’s sputum to reach the answers you seek, and when you find those answers, you’ll probably wish you hadn’t bothered. Revenge is mine, ah ha ha, yah ha ha, ya ha ha ha ha ha ha ha ha ha …

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Introductory Statistics

Muito boas aulas e slides sobre testes não paramétricos e SPSS

Muito boas aulas e slides sobre testes não paramétricos e SPSS

Welcome to Limbo, where the lustful, gluttonous and wrathful wander in endless torment. Here you can uncover the searing agony of SPSS, the stomach churning fear of central tendency and the rancid bile of z-scores. Good luck, you’ll need it.

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