Day 9 of 180 Days of Data Viz Learning #jfdi

I’m doing some form of data visualization learning for 180 days straight because I need to #JFDI.  See post explaining how and why I’m doing this.

Visualization Worked On or Created: 

N/A Today  – > Having my last Saturday in LA and good-bye festivities.  Also placing priority on finishing Scott Murray, Treehouse, and Udacity tutorials so I’ve had enough repetition practice for application before I move next week/start my new job.

Decomposition of a Visualization:

The Global Extremes of Population Density

  • What are the:
    • Variables (Data points, where they are, and how they’re represented):
      • Regions on map represented by color hue
    • Data Types (Quantitative, Qualitative, Categorical, Continuous, etc.):
      • Quantitative, Categorical
    • Encodings (Shape, Color, Position): Position, Color Hue
  • What works well here?
    • Showing frequency and geographic spread
  • What does not work well and what would I improve?
    • I thought he should have done some form of Equal Area projection, which he addressed very well in this follow-up.  I’ve seen this type of population map before, it would have been interesting to see different densities across regions for a comparative view versus just looking at a slice of South Asia.
  • What is the data source?  Do I see any problems with how it’s cited/used?
    • Not clear where the data is from, but he did make it available.  Population by country isn’t that difficult to find or accurately verify though, not a controversial set.
  • Any other comments about what I learned?
    • There’s not as much value as there could, though accurate, I think it’s difficult for most audiences to grasp just a region sliced across two countries, versus using actual countries that people have more of a conceptual framework to absorb the information.

Code Learning:

Udacity Problem Set One Finished – On section 2a Design Principles

Three Takeaways:

  • Keep Pre-Attentive Processing in mind when making viz – it only takes 200-250 milliseconds for the human eye to recognize it, like recognizing facial expressions
  • Color Hue, Color Intensity, Movement, Form, Spacial Position
  • Be careful using them together, some will pop out more than others depending on how is used

Treehouse Using Time Scales in D3.js

Three Takeaways

  • The d3 time scale is extension of linear scale however the domain input are Javascript Data objects.  Map date values to x positions. Take advantage of d3 tools to work with date objects. D3 has built-ins to handle time parse strings into javascript date objects.
  • The d3 time time scale is a extension of linear scale.  The domain inputs are javascript date objects we can map to x positions.
  • d3.extent returns the minimum and maximum value in the given array using natural order.  This is the equivalent to calling d3.min and d3.max simultaneously

Reading and Learning Data Visualization Theoretically/Critically:

The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations.Ben Shneiderman (PDF) from 7 Classic Foundational Vis Papers

Three Takeaways:

  • A useful starting point for designing advanced graphical user interfaces is the Visual InformationSeeking Mantra: Overview first, zoom and filter, then details-on-demand. In all seven data types (1-, 2- , 3-dimensional data, temporal and multi-dimensional data, and tree and network data) the items have attributes and a basic search task is to select all items that satisfy values of a set of attributes.
    • Overview : Gain an overview of the entire collection.
      Zoom : Zoom in on items of interest
      Filter: filter out uninteresting items.
      Details-on-demand: Select an item or group and get details when needed.
      Relate: View relationships among items.
      History: Keep a history of actions to support undo, replay, and progressive refinement.
      Extract: Allow extraction of sub-collections and of the query parameters.
  • Network: sometimes relationships among items cannot be conveniently captured with a tree structure and it is useful to have items linked to an arbitrary number of other items. While many special cases of networks exist (acyclic, lattices, rooted vs. un-rooted, directed vs. undirected) it seems convenient to consider them all as one data type. In addition to the basic tasks applied to items and links, network users often want to know about shortest or least costly paths connecting two items or traversing the entire network. Interface representations include a node and link diagram, and a square matrix of the items with the value of a link attribute in the row and column representing a link
  • History : Keep a history of actions to support undo, replay, and progressive refinement. It is rare that a single user action produces the desired outcome. Information exploration is inherently a process with many steps, so keeping the history of actions and allowing users to retrace their steps is important. However, most prototypes fail to deal with this requirement. Maybe they are reflecting the current state of graphic user interfaces, but designers would be better to follow information retrieval systems which typically preserve the sequence of searches so that they can be combined or refined.

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