Data Visualization and D3.js Newsletter Issue 217 Weekly Data Visualization and D3.js Newsletter

Hi Friends -

Welcome to issue 217 of the Weekly Newsletter.

Onwards to this week's links...


  • xvg
    A Chrome extension for debugging SVG paths by converting them to outlines and displaying anchors, control points, handles and arc ellipses...
  • The State Of D3 Voronoi
    A Voronoi diagram is a simple yet powerful concept; given a set of sites in a space, it partitions that space in cells — one cell for each site. Each cell contains all the points that are closer to that site than to any other. This is useful in many different areas, such as spatial/network analysis, pattern recognition, label placement on maps and graphics, generative games, generative art, UX improvements and all sorts of experiments. Here we explore what our favorite javascript library, d3.js, allows to do with this concept...

Data Visualization Reading and Videos

  • Quantifying and Visualizing “Deep Work”
    One of the best books I read in 2016 is Cal Newport’s “Deep Work”...For the end of 2016 I decided to do a more systematic analysis than usual and to share it with the world thinking that: (1) you may be interested in doing the same and (2) you may have ideas on how to improve the process...I am going to to first explain how I collect data and what criteria I use to define work as “deep”, then I’ll show you, through a number of charts, how my deep work in 2016 looks like, and finally I’ll offer a few reflections on the process and how I plan to develop it further...
  • A History of DataViz
    After examining the history of data visualization greats I decided to collect my learnings in the style of history’s data visualization greats. The first of these visual summaries is presented and discussed below.

D3.js Reading and Videos

  • d3-shaded-relief
    The shaded relief technique is a method for representing the topography which is prettier and intuitive...I found some examples about doing them with d3js, but required a previous preparation with gdal. I wanted to show how to create the effect using the DEM file directly. I added the tutorial to the d3-raster-tools-docs tutorial...
  • Responsive Data Visualization
    This is a small prototype of an approach to responsive data visualiation, using D3.js and a mini-library called rdv. The basic concept is to construct a visualization as a collection of features, then turn those features on and off based on the ratio of points to pixels...
  • A Well Factored Pie Graph: React + D3
    Our goal is to analyze two popular tools to find their strengths, then build a well factored integration between the two tools. As an example of this technique, we will create a well factored pie graph. We will focus on separating the responsibilities of state management and graph presentation by using tools that shine for each purpose separately. We will use React to manage state and D3 for presentation...I'm going to focus on overall strategy and critical analysis, rather than provide a comprehensive code listing...
  • Mitchell’s Best-Candidate
    Mitchell’s best-candidate algorithm generates a new random sample by creating k candidate samples and picking the best of k. Here the “best” sample is defined as the sample that is farthest away from previous samples. The algorithm approximates Poisson-disc sampling, producing a much more natural appearance (better blue noise spectral characteristics) than uniform random sampling...

Hope that you had a great past week and that next week is even better!

Wishing you the best, 
Sebastian Gutierrez

Want to better understand this topic?
Check out these super-useful D3.js Screencast Videos (1 in 3 are free...)
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