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antropologo.net, dataviz, collective intelligence, algorithms, social learning, social change, digital humanities
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An (incomplete) checklist for making #geodata #visualizations in data journalism | #ddj #dataviz

An (incomplete) checklist for making #geodata #visualizations in data journalism | #ddj #dataviz | e-Xploration | Scoop.it
luiy's insight:

MAPPABLE CHEAT-SHEET

An (incomplete) checklist for making geodata visualizations in (data-driven) journalism.

 

ALWAYS ASK YOURSELF FIRST:

Is a map really the best way to visualize the data set?

 

DATA HANDLING

 

- Got all geographic elements right? (especially borders & place names)

 

- Check the correct position of geocoded and self drawn map elements (thus preventing mistakes from misused spatial reference systems)

 

- Have all outliers and duplicates been eliminated? Correctly dealt with incomplete data entries?

 

- Have data entries that are not necessary for the final visualization been removed?Have the values been normalized (e.g. by population data)? .........

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Visualizing Categorical Data as Flows with Alluvial Diagrams | #dataviz #methods #tools

Visualizing Categorical Data as Flows with Alluvial Diagrams | #dataviz #methods #tools | e-Xploration | Scoop.it
luiy's insight:

Alluvial diagrams are a type of flow diagram that  have traditionally been used to visually show changes in network structures over time. Density Design has included Alluvial Diagrams in their RAW online visualization tool and explored its use to show “relations between dimensions of categorical data.”

 

RAW is such a wonderfully easy tool to use that I wanted to explore the Alluvial diagram functionality with a few different data sets to see how the visualizations would come out.

 

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#Gamification - Defining, Designing and Using it | #context #connective

A presentation that describes the concept of gamification, it's roots, design and application. Minimal words, lots of pics and lots of fun to present. :) Mak...

Via Juanmi Muñoz, Javier Sánchez Bolado, Luciana Viter, Maria Margarida Correia, michel verstrepen, Terheck
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Data Perspective: Data Analysis Steps | #datascience #workflow

Data Perspective: Data Analysis Steps | #datascience #workflow | e-Xploration | Scoop.it
data analysis steps, how to implement data analysis, how to solve data analysis problem
luiy's insight:

After going through the overview of tools & technologies needed to become a Data scientist in my previous blog post, in this post, we shall understand how to tackle a data analysis problem. Any data analysis project starts with identifying a business problem where historical data exists. A business problem can be anything which can include prediction problems, analyzing customer behavior, identifying new patterns from past events, building recommendation engines etc.

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The Data Visualisation Catalogue | #dataviz

The Data Visualisation Catalogue | #dataviz | e-Xploration | Scoop.it
The Data Visualisation Catalogue, helping you find the right data visualization method for your data

Via Jacek Bugajski
luiy's insight:

About the website

The Data Visualisation Catalogue is currently an on-going project developed by Severino Ribecca.

 

Originally, this project was a way for me to develop my own knowledge of data visualisation and create a reference tool for me to use in the future for my own work. However, I thought it would also be useful tool to not only other designers, but also anyone in a field that requires the use of data visualisation regularly (economists, scientists, statisticians etc).

 

Although there have been a few attempts in the past to catalogue some of the established data visualisation methods, there is no website that is really comprehensive, detailed or helps you decide the right method for your needs.

 

I will be adding in new visualisation methods, bit-by-bit, as I research each method to find the best way to explain how it works and what it is best suited for.

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Jacek Bugajski's curator insight, January 29, 1:31 PM
The Data Visualisation Catalogue
Rescooped by luiy from Complex Networks
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Visualization techniques for categorical analysis of social networks with multiple edge sets | #SNA

Visualization techniques for categorical analysis of social networks with multiple edge sets | #SNA | e-Xploration | Scoop.it

Via Becheru Alexandru
luiy's insight:

The node link graph on the left runs into limitation when trying to compare multiple properties, since only one property can be mapped to color at a time. This makes it hard for the user to look at both gender and grade level. In the radial layout on the right, we group by grade and map color to gender. The visualization starts with 8th grade on top and continues counter-clockwise with 12th grade at bottom right and unknown to the top right. The radial layout shows that gender plays less of a role as kids get older (there is more mixing of gender in higher grades).

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Social Network Analysis & an Introduction to Tools | #dataviz #SNA_indatcom #SNA

This presentation covers the basics of network analysis and then goes into the different types of tool that support analyzing networks.

 
Via Stephen Dale, Kenneth Mikkelsen
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Stephen Dale's curator insight, July 21, 2013 7:41 AM

A great introduction to the power and benefits of SNA. Some useful pointers to SNA tools as well.

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Social data is not like most #bigdata. by @ValdisKrebs | #sna #methods

Social data is not like most #bigdata. by @ValdisKrebs | #sna #methods | e-Xploration | Scoop.it
luiy's insight:

When investigating social/relational data, it is usually not the forest that is useful, but the clusters of various trees, and their relationships, inside the ecosystem. We not only want to "see the forest for the trees", but also see the patterns/clusters of trees in the forest! 

Unless the bar has been set too low, for what a link is, most collections of big data on social relationships contain hundreds, if not thousands, of components/subsets.  Patterns reveal much within interlinked data. As we zoom in, we can begin to answer some useful questions :

 

- Who is here?  

- How are they clustered? 

- How are they connected? 

- Who are the key connectors?  

- Who is in the thick of things?

 

Below are various subsets of the above ecosystem -- we put an MRI to our big data pile.  These network maps show various slices of the whole, and how they are connected. We now see patterns worth investigating.

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The Impact Cycle – how to think of actionable insights | #datascience #methods

The Impact Cycle – how to think of actionable insights | #datascience #methods | e-Xploration | Scoop.it
luiy's insight:

I. Identify the question. In a non intrusive way, help your business partner identify the critical business question(s) he or she needs help in answering. Then set a clear expectation of the time and the work involved to get an answer.

 

M. Master the data.This is the analyst’s sweet spot—assemble, analyze, and synthesize all available information that will help in answering the critical business question. Create simple and clear visual presentations (charts, graphs, tables, interactive data environments, and so on) of that data that are easy to comprehend.

 

P. Provide the meaning. Articulate clear and concise interpretations of the data and visuals in the context of the critical business questions that were identified.

 

A. Actionable recommendations. Provide thoughtful business recommendations based on your interpretation of the data. Even if they are off-base, it’s easier to react to a suggestion that to generate one. Where possible, tie a rough dollar figure to any revenue improvements or cost savings associated with your recommendations.

 

C. Communicate insights. Focus on a multi-pronged communication strategy that will get your insights as far and as wide into the organization as possible. Maybe it’s in the form of an interactive tool others can use, a recorded WebEx of your insights, a lunch and learn, or even just a thoughtful executive memo that can be passed around.

 

T. Track outcomes. Set up a way to track the impact of your insights. Make sure there is future follow-up with your business partners on the outcome of any actions. What was done, what was the impact, and what are the new critical questions that need your help as a result?

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Choosing a Chart | #dataviz #methods

Choosing a Chart | #dataviz #methods | e-Xploration | Scoop.it

Via Amanda Dahlquist
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StarGate: A Unied, #Interactive Visualizatio of Software Projects | #dataviz #SNA

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Rescooped by luiy from Homo Agilis (Collective Intelligence, Agility and Sustainability : The Future is already here)
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Harnessing #CollectiveIntelligence: Wiki and Social Network From End-User Perspective #tools

Harnessing #CollectiveIntelligence: Wiki and Social Network From End-User Perspective #tools | e-Xploration | Scoop.it
In the social web in which ¿people socialize or interact with each other throughout the World Wide Web, social interactions lead to the creation of explicit and meaningfully rich knowledge representations¿. Emergence of social web shed light on the

Via Claude Emond
luiy's insight:

In the social web in which “people socialize or interact with each other throughout the World Wide Web, social interactions lead to the creation of explicit and meaningfully rich knowledge representations”. Emergence of social web shed light on the concept of collective intelligence (CI). Web 2.0 technologies as key part of social semantic web, play an important role to harness the CI. Web 2.0 technologies aredivided into the end-user and technical perspectives. In this paper CI and Web 2.0 is assessed with more details and througha theoretical framework regarding the end-user  perspective. From all various kinds of Web 2.0 technologies Wikis and Social networks are chosen due to their huge contribution to CI. This paper focuses on end-user perspective of Wiki and Social network; categorizes the end-user perspective of these two technologies into 4 core aspects; and on the basis of  findings from a web-based questionnaire, tests the relationship between each component of these 4 aspects and the CI

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Claude Emond's curator insight, February 2, 9:49 PM

You can download this article right from the link (might require or not registering with FB or whatever) :)

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Imagine A Pie Chart Stomping On An Infographic Forever | #dataviz #context

Imagine A Pie Chart Stomping On An Infographic Forever | #dataviz #context | e-Xploration | Scoop.it
A certain category of design gaffes can be boiled down to violations of audience expectations.
luiy's insight:
Know Your Data: Mind, Body, And Soul

Our data’s “body” is its form and function. Like the human body, it consists of both overarching similarities and individual differences. Here we can draw an analogy with dislocation: just because your arm twists a certain way does not mean that it is supposed to; on the other hand, some people are indeed flexible enough to move in just that way. When we exceed the limitations of our body, we hear the body’s complaints loudly. But your data won’t let you know when you twist it out of shape (though you may hear some poor statistics professor screaming on its behalf). When we consider our data’s “body,” we’re considering the performance it can achieve and the stresses it can tolerate. That process often begins with these simple questions:

 

How was it collected? What are its limitations? Which mathematical transformations are appropriate? Which methods of display are appropriate? 

Our data’s “soul” is its context and broader meaning. One popular understanding of a soul is that it is some part of an individual that glorifies their uniqueness even while making them a part of a profound commonality. In a mystical context (whether you believe in it or not), we can easily understand this apparent contradiction. In a mundane context, many people would find the dissonance hard to swallow. This is unfortunate, because data must be understood in just this way: it is both an individual entity and a unit of a larger whole. Reconciling these is a notoriously tricky task, but some of the worst mistakes are avoided by collecting a few crucial bits of information:

 

Who collected it? Why was it collected? What is its context in a broader subject? What is its context in the field of research that created it?

 

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The science behind data visualisation I #dataviz #SNA

The science behind data visualisation I #dataviz #SNA | e-Xploration | Scoop.it
Courtesy of Net Magazine and originally posted by Alberto Cairo Graham Odds outlines principles that will help you to design more...
luiy's insight:
System 1 vs System 2

Daniel Kahnemann, in Thinking, Fast and Slow, introduces the terms System 1 and System 2 to differentiate between the information processing that occurs in our sub-conscious and conscious minds respectively. The former encapsulates the functions that are uncontrolled, always-on and effortless, while the latter refers to those functions that are controlled but require effort to engage.

 

To better understand the differences between System 1 and System 2, consider Figure 1. In the photograph on the left we immediately perceive an angry man and probably associate loud noise and aggressive movement with the depicted scene. This exceedingly sophisticated interpretation of mere pixels is almost immediate, requires no effort and comes completely naturally. Contrast that with the multiplication on the right. We instantly recognise what is being asked of us and that we are able to work it out, but most will not attempt the mental arithmetic involved because of the conscious effort required. The initial reactions in both cases are pure System 1, while the mental arithmetic is an example of System 2.

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Professor Gunnar Carlsson Introduces Topological Data Analysis | #TDA #dataviz

An Introduction to Topological Data Analysis by Ayasdi's Gunnar Carlsson
luiy's insight:

Data ---> structures, representations and forms analysis

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