Day 109 of 180 Days of Data Viz Learning #jfdi

I’m doing some form of data visualization learning for 180 days because I need to #JFDI.

See post explaining how and why I’m doing this.

Code Learning:

 Knight Journalism Intermediate D3 for Data Visualization 

Week Four Discussion Question

Geographic maps are undoubtedly useful tools for communicating data visually, yet they inevitably introduce distortions and mistruths. What are some of the compromises

that must be made when producing a geographic map? (Consider sharing an example to illustrate your point.)

The biggest example I can think of that pops off my head are maps that map data characters of populations over the map of the United States… when you just end up getting a map of the population distribution of the United States rather than being able to denote a trend.  Also, regional-level maps also distort complexity.

For example, an electoral map by state doesn’t reveal the story of urban voting trends.  On the flipside, the newly sectioned map does not account for population clusters.

Week Four Quiz

Three Takeaways:

  • D3 projections take a two-value array: longitude and latitude and return x and y as output
  • You should value maps to area of each circle rather than radius to avoid distortion
  • To make a choropleth, use Javascript to match datasets (CSV and GeoJSON) and bind the merged array to the paths
Reading and Learning Data Visualization Theoretically/Critically:

Reading Nathan Yau’s book Visualize This

258-272 Spotting Differences

Three Takeaways:

  • Multidimensional Scaling can help you find clusters – there are various models you can use to do this p 261
  • Ways to find outliers: bar charts, histograms, boxplots p 269
  • When working with Maps data, think of x and y coordinates as longs and lats p 272

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