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Tableau Practice: Los Angeles Water Usage

Playing with a little Tableau using open data from Los Angeles Open Data on “Residential water use by month averaged for fiscal year. Numbers represent Hundred Cubic Feet (HCF) of water use.”

Couple of observations with the data:

  • Water usage seems to correlate with residential density, eg. the high water usage in Downtown and Central City that have more high-density apartment blocks versus suburban tract homes that make up most of Los Angeles.
  • Water usage dropped dramatically in Fiscal Year 12/13, I’m almost wondering if that data was never fully populated. Either way, there seems to be a good downward trend which is good because of the CA drought and just being a region where water is scarce.
  • Tableau’s sum filters can be puzzling for those who aren’t familar with the platform when you filter the data – eg suddenly the scale is off so you have to reduce that filter to see the values.

A few process notes

  • I had to pivot the raw data a bit before bringing it to Tableau
  • I imported a custom shape for the water drop effect
  • I used the area chart for trend overtime to kind of hit home the point the amount of water usage rather than a simple line to show time series.

Click here or on the visual below to take a look:

water.PNG

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

 

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

Weekly Data Viz Decomp: Visualizing the Burden of Cancer

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

This week’s decomp of Visualizing the Burden of Cancer: Incidence Rate by 32 Cancers from 2005 to 2015

Decomposition of a Visualization:

  • What are the:
    • Variables (Data points, where they are, and how they’re represented):
      • How number of occurrences of cancers have shifted between 2005 and 2015 – date encoded with huge and increase and decrease done with green or red
    • Data Types (Quantitative, Qualitative, Categorical, Continuous, etc.):
      • Quantitative, continuous
    • Encodings (Shape, Color, Position, etc.):
      • Position, Color Hue
  • What works well here?
    • Showing large changes
    • Filtering functions
  • What does not work well and what would I improve?
    • Small changes looked too scrunched
    • Weird that increase and decreases are show on same axis, would have changed it to show decreases on a double axis
  • What is the data source?  Do I see any problems with how it’s cited/used?
    • No, great full academic citing here
  • Any other comments about what I learned?
    • Always difficult to use this type of chart with data of varying ranges – not sure I would have done that much better

Weekly Data Viz Decomp: The Guardian’s Premier League Transfer Window Summer 2016

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

This week’s viz: Premier League: transfer window summer 2016 – interactive

Decomposition of a Visualization:

  • What are the:
    • Variables (Data points, where they are, and how they’re represented):
      • Bubble size for size of transfer
      • Color hue denoting transfer or out of team
      • Position for date close to transfer window
    • Data Types (Quantitative, Qualitative, Categorical, Continuous, etc.):
      • Qualitative and categorial
    • Encodings (Shape, Color, Position, etc.):
      • Shape, position, size, color hue
  • What works well here?
    • Showing a small multiples type view for each team and their transfers
  • What does not work well and what would I improve?
    • Having the totals summary numbers on the side of the charts is a little unorthodox and unintuitive
    • Bubbles seem to be placed arbitrarily without thought to the y-axis, even though the x-axis has meaning
    • Not immediately clear why some players are featured and noted in tooltips versus those that are not
  • What is the data source?  Do I see any problems with how it’s cited/used
    • Seems to be original Guardian data collected about the English Premier League, but not as clearly stated as I’d like to expect
  • Any other comments about what I learned?
    • Example of something pleasing to the eye in terms of color hue and perhaps some flash factor, but perhaps not that functional to explore upon closer examination.
      • Certainly sense for the purposes of the Guardian though in putting out this story and is a technique I’d borrow if I had a use case
      • Good for showing a bigger picture view
    • Probably not worth it in terms of the work it would be taken incrementally as filters are difficult to work and can be computationally expensive, but the nerd in me would have liked to search for the player