Making sense of the world with data visualization

A March 30, 2017 item on features an essay about data visualization,

The late data visionary Hans Rosling mesmerised the world with his work, contributing to a more informed society. Rosling used global health data to paint a stunning picture of how our world is a better place now than it was in the past, bringing hope through data.

Matt Escobar, postdoctoral researcher on machine learning applied to chemical engineering at the University of Tokyo, wrote this March 30, 2017 essay originally for The Conversation,

Now more than ever, data are collected from every aspect of our lives. From social media and advertising to artificial intelligence and automated systems, understanding and parsing information have become highly valuable skills. But we often overlook the importance of knowing how to communicate data to peers and to the public in an effective, meaningful way.

Hans Rosling paved the way for effectively communicating global health data. Vimeo

Data visualisation can take many other forms, just as data itself can be interpreted in many different ways. It can be used to highlight important achievements, as Bill and Melinda Gates have shown with their annual letters in which their main results and aspirations are creatively displayed.

Escobar goes on to explore a number of approaches to data visualization including this one,

Finding similarity between samples is another good starting point. Network analysis is a well-known technique that relies on establishing connections between samples (also called nodes). Strong connections between samples indicate a high level of similarity between features.

Once these connections are established, the network rearranges itself so that samples with like characteristics stick together. While before we were considering only the most relevant features of each live show and using that as reference, now all features are assessed simultaneously – similarity is more broadly defined.

Networks show a highly connected yet well-defined world.

The amount of information that can be visualised with networks is akin to dimensionality reduction, but the feature assessment aspect is now different. Whereas previously samples would be grouped based on a few specific marking features, in this tool samples that share many features stick together. That leaves it up to users to choose their approach based on their goals.

He finishes by noting that his essay is an introduction to a complex topic.

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