Weekly Data Decomposition: The Robot Rampage

Weekly data visualization decomps to keep a look out for technique and learning: The Robot Rampage from BloombergGadfly 

Decomposition of a Visualization:

  • What are the:
    • Variables (Data points, where they are, and how they’re represented):
      • Units of robots per country and by forecasted year
        • Bar chart
      • Jobs that could be automated by country, definite and theoretical
        • Stacked bar chart
      • Concentration of industrial robots per 10,000 manufacturing workers per country represented on cartogram
    • Data Types (Quantitative, Qualitative, Categorical, Continuous, etc.):
      • Quantitative, Categorical
    • Encodings (Shape, Color, Position, etc.):
      • Color hue for different forecasts on bar charts
      • Color hue for different regions
      • Sizing on cartogram and colors for region
  • What works well here?
    • Showing a narrative about the rates of robotization across different regions and the potential effect to workers
  • What does not work well and what would I improve?
    • I like these a lot – I think it would be cool to have more population charts proportional to the size of workforces in the middle graphs. It’s hard to see human impact there.
  • What is the data source?  Do I see any problems with how it’s cited/used?
    • International Federation of Robotics and the World Bank
  • Any other comments about what I learned?
    • I liked how different data sources were combined for a cohesive narrative

 

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Weekly Data Decomp: Country Quiz

Weekly data visualization decomps to keep a look out for technique and learning.

This week is the Guardian’s How Well Do You Know Your Country Quiz

Decomposition of a Visualization:

  • What are the:
    • Variables (Data points, where they are, and how they’re represented):
      • Numerical values on a x-axis scale using position
      • Lines showing gap in perception
    • Data Types (Quantitative, Qualitative, Categorical, Continuous, etc.):
      • Continuous
    • Encodings (Shape, Color, Position, etc.):
      • Position
      • Line Length
      • Color Hue for position
  • What works well here?
    • Showing difference between three possibilities
  • What does not work well and what would I improve?
    • Being able to compare with a filter of different countries side-by-side
  • What is the data source?  Do I see any problems with how it’s cited/used?
    • Ipsos Mori survey
  • Any other comments about what I learned?
    • I like how this is a combination of what would traditionally be a survey or quiz with data visualization elements for interactivity and exploration

 

Weekly Data Viz Decomp: Global Sea Ice Level

Weekly data visualization decomps to keep a look out for technique and learning: Global Sea Ice Level I found on Reddit’s DataIsBeautiful 

Decomposition of a Visualization:

  • What are the:
    • Variables (Data points, where they are, and how they’re represented):
      • Months on a radial axis
      • Sea level area scale on radial area
      • Lines along radial to represent sea ice level
    • Data Types (Quantitative, Qualitative, Categorical, Continuous, etc.):
      • Quantitative, Continuous
    • Encodings (Shape, Color, Position, etc.):
      • Color hue and position for line
  • What works well here?
    • The animation and showing the change through time is particularly effected as the overall area shrinks
    • The color hue change to a lighter color for current years is particularly effective
  • What does not work well and what would I improve?
    • The colors seem to be a little off theme – maybe personal nitpick but I would have picked a blue hue or something that relates to the water more
    • No sure how much the seasons adds to this chart, but I like the use of the area on this chart rather than one with a simple xy-axis
    • Maybe add an interactive filter for years to see contrast
  • What is the data source?  Do I see any problems with how it’s cited/used?
    • Cites what looks like a scientific journal – would have liked a link or publication, but I’m not familiar with this subject area
  • Any other comments about what I learned?
    • Makes me want to use a radial chart for something when I get a use case for it

 

Why I Can’t Find Recycling Bins in NYC (visualized)

Although I love NYC, as a Californian transplant the one big pet peeve I have other than the lack of Mexican options and fresh avocados is the lack of recycling and general awareness of doing so. Yes, I’m that Californian who brings the reusable bags to the market and carries a water bottle at times in NYC.

I couldn’t help noticing walking around how many people buy items like bottled water and have no place to recycle them even though NYC has a lot more public waste baskets than a lot of other cities. Taipei, I’m looking at you and all those days I carried my trash for miles.

I played with some of the NYC Open Data in Tableau and did confirm how few Public Recycling Bins are in NYC.

For some reason they’re particularly absent in Queens, outer Brooklyn, and Upper East Side. Population density seems to have no impact. Shrug, come on NYC, you “progressive bastion,” get your recycling on! It kills me seeing how many people here buy beverages in containers that could easily be recycled and toss them into a regular trash bin. It’s crazy.

Click here or the image below to see the visualized data:


Screenshot