What are you going to do with that degree?
Posted by Armando Brito Mendes | Filed under estatística, visualização
Boa visualização sobre o q fazem os licenciados com os seus títulos.
Jobs by college major
This is a quick Sankey visualization of how college majors relate to professions, based on data from the American Community survey. On the left are the largest college majors; to the right are the most common professions.
To see broad fields like “Sciences” and “Humanities”, see the edited version of this page.
The width of each stream shows how many people with that major are in that field. (The color shows whether that’s more or fewer people than expected based on how big the major is: hover over to see just how many more it is.) The width of each stream shows how many people with that major are in that field. (The color shows whether that’s more or fewer people than expected based on how big the major is).
You surely see that the lines are too small to understand in most cases: to actually see what’s going on with a particular field or job, click on a box and the chart will filter down to just the people who either majored in the field, or ended up employed in the job. (Click on one of the connecting lines to see both at once.)
I have not developed this that far because I am not sure how useful it ultimately is: my basic goal was a quick way to see, for example, what jobs history majors ended up in. (Largest is lawyers, but also schoolteachers; what you would expect, but worth knowing.)
You might also like my visualization of changing college degrees over time.
Tags: belo, Estat Descritiva
Vector maps on the web with Mapbox GL
Posted by Armando Brito Mendes | Filed under mapas SIG's, materiais para profissionais, software, visualização
Novas funcionalidades da biblioteca Java Script para desenhar mapas vetoriais
Online mapping just got an upgrade:
Announcing Mapbox GL JS — a fast and powerful new system for web maps. Mapbox GL JS is a client-side renderer, so it uses JavaScript and WebGL to dynamically draw data with the speed and smoothness of a video game. Instead of fixing styles and zoom levels at the server level, Mapbox GL puts power in JavaScript, allowing for dynamic styling and freeform interactivity.
For the non-developers: Online maps are typically stored pre-made on a server, in the form of a bunch of image files that are stitched together when you zoom in and out of a map. So developers have to periodically update the image files if they want their base maps to change. It’s a hassle, which is why base maps often look similar. With Mapbox GL, making changes is easier because the development pipeline is shorter.
More details on the JavaScript library here.
Tags: desnvolvimento de software, mapas
Wi-fi revealed
Posted by Armando Brito Mendes | Filed under Sem categoria, visualização
Mostrar o invisivel como as ondas eletromagnéticas criadas pelo wi-fi
Digital Ethereal is a project that explores wireless, making what’s typically invisible visible and tangible. In the piece above, a handheld sensor is used to detect the strength of Wi-Fi signal from a personal hotspot. A person waves the sensor around the area, and long-exposure photography captures the patterns.
Reminds me of the Immaterials project from a while back, which used a light stick to represent signal strength rather than a signal light.
Tags: belo
Markov Chains explained visually
Posted by Armando Brito Mendes | Filed under Investigação Operacional, matemática, materiais ensino, visualização
Boa forma de perceber como funcionam as cadeias de Markov
Adding on to their series of graphics to explain statistical concepts, Victor Powell and Lewis Lehe use a set of interactives to describe Markov Chains. Even if you already know what Markov Chains are or use them regularly, you can use the full-screen version to enter your own set of transition probabilities. Then let the simulation run.
Tags: grafos, otimização
Income inequality seen in satellite images from Google Earth
Posted by Armando Brito Mendes | Filed under estatística, visualização
Uso de proxis para identificar vizinhanças pobres
Researchers Pengyu Zhua and Yaoqi Zhang noted in their 2008 paper that “the demand for urban forests is elastic with respect to price and highly responsive to changes in income.” Poor neighborhoods tend to have fewer trees and the rate of forestry growth is slower than that of richer neighborhoods.
Tim De Chant of Per Square Mile wondered if this difference could be seen through satellite images in Google Earth. It turns out that you can see the distinct difference in a lot of places. Above, for example, shows two areas in Rio de Janeiro: Rocinha on the left and Zona Sul on the right. Notice the tree-lined streets versus the not so green.
De Chant notes:
It’s easy to see trees as a luxury when a city can barely keep its roads and sewers in working order, but that glosses over the many benefits urban trees provide. They shade houses in the summer, reducing cooling bills. They scrub the air of pollution, especially of the particulate variety, which in many poor neighborhoods is responsible for increased asthma rates and other health problems. They also reduce stress, which has its own health benefits. Large, established trees can even fight crime.
Okay, I don’t now about that last part about fighting crime. Without seeing the data, I think that sounds like a correlation more than anything else, but still. Trees. Good.
Tags: análise de dados, data mining, image mining, mapas
Site sobre visualização da GE.com
Posted by Armando Brito Mendes | Filed under estatística, visualização
Site com muitos exemplos de visualização mantido pela GE
GE Works. Building, Moving, Powering and Curing the world. In the process, our technologies are generating data on a petabyte scale. This data contains valuable information that will drive insights, innovations, and discoveries, but it can be difficult to access and digest. Using data visualization, we’re pairing science and design to simplify the complexity and drive a deeper understanding of the context in which we operate.
We encourage you to explore the projects below.
For further information about GE’s data visualization program, please contact us at datavizinfo@ge.com
To share your own visualizations, please visit www.visualizing.org
Tags: análise de dados, belo, data mining, Estat Descritiva, mapas
Posted by Armando Brito Mendes | Filed under estatística, visualização
Tutorial para explicar como acrescentar um segundo eixo aos gráficos do Excel
Data Visualization – Banking Case Lab : Microsoft Excel – use Secondary Axis to Create Two Y Axes
25th May, 2014 · Roopam Upadhyay
Analytics Lab
Banking Case
Using Secondary Axis to Create Two Y Axes in Excel
Tags: Estat Descritiva, Excel
Humor com gráficos kindofnormal
Posted by Armando Brito Mendes | Filed under estatística, materiais ensino, materiais para profissionais, visualização
Humor com esquemas e gráficos
Alguns exemplos:
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- June 12, 2014
- 0
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- June 11, 2014
- 3.8
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- June 10, 2014
- 5.9
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- June 9, 2014
- 5
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- June 6, 2014
- 5.5
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Tags: belo, Estat Descritiva
Warm and cold weather anomalies
Posted by Armando Brito Mendes | Filed under visualização
Mais um exemplo de boas visualizações, agora com dados de clima
This year’s polar vortex churned up some global warming skeptics, but as we know, it’s more useful to look at trends over significant spans of time than isolated events. And, when you do look at a trend, it’s useful to have a proper baseline to compare against.
To this end, Enigma.io compared warm weather anomalies against cold weather anomalies, from 1964 to 2013. That is, they counted the number of days per year that were warmer than expected and the days it was colder than expected.
An animated map leads the post, but the meat is in the time series. There’s a clear trend towards more warm.
Since 1964, the proportion of warm and strong warm anomalies has risen from about 42% of the total to almost 67% of the total – an average increase of 0.5% per year. This trend, fitted with a generalized linear model, accounts for 40% of the year-to-year variation in warm versus cold anomalies, and is highly significant with a p-value approaching 0.0. Though we remain cautious about making predictions based on this model, it suggests that this yearly proportion of warm anomalies will regularly fall above 70% in the 2030’s.
Explore in full or download the data and analyze yourself. Nice work. [Thanks, Dan]
High-detail maps with Disser
Posted by Armando Brito Mendes | Filed under mapas SIG's, software, visualização
Software open source para trabalhar com mapas
Open data consultancy Conveyal released Disser, a command-line tool to disaggregate geographic data to show more details. For example, we’ve seen data represented with uniformly distributed dots to represent populations, which is fine for a zoomed out view. However, when you get in close, it can be useful to see distributions more accurately represented.
If the goal of disaggregation is to make a reasonable guess at the data in its pre-aggregated form, we’ve done an okay job. There’s an obvious flaw with this map, though. People aren’t evenly distributed over a block — they’re concentrated into residential buildings.
So Disser combines datasets of different granularity, so that you can see spreads and concentrations that are closer to real life.
Tags: belo, image mining, mapas
