Using clustering to create a new D3.js color scale – David Schnurr – Medium

Design for Interactive Data Vis (Lyon 2016) by Lynn Cherny
▻http://ghostweather.slides.com/lynncherny/deck-8

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#visualisation #d3.js @arnicas

J’en profite pour coller en vrac quelques liens qui traînent dans mes onglets

Formation #d3.js par Martin Daniel
▻https://www.youtube.com/watch?v=ZEtyr9C01cg

Visualisations responsive (lien mort hahhaaha)
▻http://ablesense.com/responsive-d3js-charts

d3 et #react
▻https://hackernoon.com/building-d3-components-with-react-7510e4743288

barcharts et transitions avec un menu dropdown
▻https://vida.io/documents/BtPsrecM8DurSFQDw

la galerie d3 de Chris Viau
▻http://christopheviau.com/d3list/gallery.html

Une échelle de couleurs basée sur #CKmeans
►https://medium.com/@dschnr/using-clustering-to-create-a-new-d3-js-color-scale-dec4ccd639d2

https://cdn-images-1.medium.com/max/800/1*rZw9qP3hn6p_RbD_C4GGRA.png

Stats, Maps n Pix : D3 Charts in QGIS Print Composer
▻http://www.statsmapsnpix.com/2016/10/d3-charts-in-qgis-print-composer.html

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This post is about how to make charts created in d3.js appear in QGIS and how to make them work in the Print Composer so that you can export them with your maps.

How the Financial Times uses d3.js for interactive and animated data visualisations
▻http://johnburnmurdoch.github.io/d3-london

Using clustering to create a new D3.js color scale – Medium
►https://medium.com/@dschnr/using-clustering-to-create-a-new-d3-js-color-scale-dec4ccd639d2

In late July 2015, Tom replaced the Jenks algorithm in simple-statistics with a port of an algorithm called Ckmeans–a 1-dimensional clustering algorithm created by Haizhou Wang and Mingzhou Song that is a slight improvement over Jenks in most cases.
Despite being an improvement over Jenks, the Ckmeans algorithm still had a runtime complexity O(kn²) which would take 300–500ms for visualizations that have thousands of data points such as the US counties choropleth above. This is just too slow to be used reliably in applications where performance is important.
Luckily Wang & Song released Ckmeans 3.4.6 in May 2016, with a new core algorithm that uses a divide & conquer dynamic programming approach to achieve O(kn log(n)) runtime. For web applications this is a huge win–the same US counties choropleth now takes 30–50ms to compute clusters, which is a 10x improvement.

https://cdn-images-1.medium.com/max/800/1*rZw9qP3hn6p_RbD_C4GGRA.png

#d3.js #échelles #K-means #CKmeans #Jenks

voir aussi ▻http://mappemonde.mgm.fr/geovisu/119