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Journalisme graphique
Infographies, design d'informations et data en dessert
Curated by Karen Bastien
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Rescooped by Karen Bastien from visual data!

11 of the Most Influential Infographics of the 19th-Century...

11 of the Most Influential Infographics of the 19th-Century... | Journalisme graphique |
We live in a world steeped in graphic information. From Google Maps and GIS to the proliferation of infographics and animated maps, visual data surrounds us.

While we may think of infographics as a relatively recent development to make sense of the immense amount of data available on the Web, they actually are rooted in the 19th century.

Two major developments led to a breakthrough in infographics: advances in lithography and chromolithography, which made it possible to experiment with different types of visual representations, and the availability of vast amounts of data, including from the American Census as well as natural scientists, who faced heaps of information about the natural world, such as daily readings of wind, rainfall, and temperature spanning decades.

But such data was really only useful to the extent that it could be rendered in visual form. And this is why innovation in cartography and graphic visualization mattered so greatly...

Via Lauren Moss
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Rescooped by Karen Bastien from visual data!

Graphing the history of philosophy

Graphing the history of philosophy | Journalisme graphique |

Each philosopher is a node in the network and the lines between them (or edges in the terminology of graph theory) represents lines of influence. The node and text are sized according to the number of connections. The algorithm that visualises the graph also tends to put the better connected nodes in the centre of the diagram so we the most influential philosophers, in large text, clustered in the centre. It all seems about right with the major figures in the western philosophical tradition taking the centre stage. (I need to also add the direction of influence with a arrow head – something I’ve not got round to yet.)

A shortcoming however is that this evaluation only takes into account direct lines of influence. Indirect influence via another person in the network does not enter into it. This probably explains why Descartes is smaller than you’d think.

It gets more interesting when we use Gephi to identify communities (or modules) within the network. Roughly speaking it identifies groups of nodes which are more connected with each other than with nodes in other groups. Philosophy has many traditions and schools so a good test would be whether the algorithm picks them out...

Via Martin Daumiller, Lauren Moss
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