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Top 5 data visualization errors

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Top 5 data visualization errors

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Learn how to avoid errors made by data visualization software. By checking for the five common errors made by data visualization software, you’ll be on your way to creating data visualizations like a pro.

Learn how to avoid errors made by data visualization software. By checking for the five common errors made by data visualization software, you’ll be on your way to creating data visualizations like a pro.

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Top 5 data visualization errors

  1. 1. TOP 5 DATA VISUALIZATION ERRORS Professor Kristen Sosulski, Ed.D New York University Stern School of Business @sosulski | ks123@nyu.edu | kristensosulski.com 1
  2. 2. Introduction • Building data visualizations is easy. • In fact, you can build beautiful geospatial, categorical, statistical, relational, multivariate, and time series displays with little effort, as long the data is presented in the correct format. • However, it’s always important to study and review the output of your visualizations; the default settings can result in errors of omission and poor scaling. 2Copyright 2016 Kristen Sosulski ks123@nyu.edu @sosulski kristensosulski.com
  3. 3. Learn how to avoid errors made by data visualization software. 3Copyright 2016 Kristen Sosulski ks123@nyu.edu @sosulski kristensosulski.com
  4. 4. Top 5 errors made by software Maps: Excluding AK and HI Poor scaling Excluding the data source Using different shades for bars Encodings without explanation 4Copyright 2016 Kristen Sosulski ks123@nyu.edu @sosulski kristensosulski.com
  5. 5. ERROR 1 5
  6. 6. What’s wrong with this map? 6Copyright 2016 Kristen Sosulski ks123@nyu.edu @sosulski kristensosulski.com
  7. 7. Answer: The map below shows the location of aviation incidents and accidents in the US. However, it only shows the 48 contiguous states. 7
  8. 8. How do we correct this error? • When mapping data points on a geospatial display of the United States, be sure to include all 50 states. • To include Alaska and Hawaii on your map, simply take screenshots of the two states from your original visualization (you may have to zoom out or pan), and paste them near the west coast of the US. 8Copyright 2016 Kristen Sosulski ks123@nyu.edu @sosulski kristensosulski.com
  9. 9. Corrected map by including AK and HI. 9
  10. 10. ERROR 2 10
  11. 11. What’s wrong with this chart? 11
  12. 12. Answer: • The bars represent the number of TEUs by year in China’s ports. The y-axis presents the data in thousands. • The numbers on the scale are difficult to read such as 40200K. • 40200K is simply, 40,200,000 or 40.2 million. 12Copyright 2016 Kristen Sosulski ks123@nyu.edu @sosulski kristensosulski.com
  13. 13. How do we correct this error? • In this case, the y-axis should be set to the highest denomination, which in this case in millions. • I see this mistake often with Tableau generated charts. See the corrected chart on the next slide. 13Copyright 2016 Kristen Sosulski ks123@nyu.edu @sosulski kristensosulski.com
  14. 14. Corrected the chart by setting the y-axis scale to millions. 14
  15. 15. ERROR 3 15
  16. 16. What’s missing from this chart? 16Copyright 2016 Kristen Sosulski ks123@nyu.edu @sosulski kristensosulski.com
  17. 17. Answer: • Omitting a reference to the data source. This makes it impossible to check the validity and integrity of the visual presentation. • Also, the scale is also omitted on this chart. 17
  18. 18. Corrected the chart by adding the data source. 18 Source: NYC Open Data: 311 Calls (2010-2015) Copyright 2016 Kristen Sosulski ks123@nyu.edu @sosulski kristensosulski.com
  19. 19. ERROR 4 19
  20. 20. What’s confusing about this this chart? 20Copyright 2016 Kristen Sosulski ks123@nyu.edu @sosulski kristensosulski.com
  21. 21. Answer: 21 • There are there redundant encodings for the categorical data. • The value of each bar is represented by both a color and a number, in addition to the bar length. • There is no extra information provided by the different colors used. Copyright 2016 Kristen Sosulski ks123@nyu.edu @sosulski kristensosulski.com
  22. 22. How do we correct the error? • Remove the different colors or shading within the same bar chart. • The label describing the bar should make it clear enough what the bar represents.. 22Copyright 2016 Kristen Sosulski ks123@nyu.edu @sosulski kristensosulski.com
  23. 23. Corrected the chart by removing the different shades of green on the bars. 23Copyright 2016 Kristen Sosulski ks123@nyu.edu @sosulski kristensosulski.com
  24. 24. ERROR 5 24
  25. 25. 25 What’s unclear about this map? Copyright 2016 Kristen Sosulski ks123@nyu.edu @sosulski kristensosulski.com
  26. 26. Answer: 26 • There is no description of what the colors, bubbles, and bubble size signify in the chart. • Bubble charts are used to display multivariate data. The size of a bubble represents a quantitative value such as population or quantity, while the color usually is a categorical variable such as region. • The position of the bubble is the intersection of the x and y coordinates. In this case, it is the longitude and latitude.
  27. 27. How can we fix this error? 27 Simply include a legend to explain the color codes and sizes of your bubbles. Copyright 2016 Kristen Sosulski ks123@nyu.edu @sosulski kristensosulski.com
  28. 28. Corrected the error by including a legend. 28
  29. 29. Summary: 5 errors made by data visualization software. 29 Maps: Excluding AK and HI Poor scaling Excluding the data source Using different shades for bars Encodings without explanation Copyright 2016 Kristen Sosulski ks123@nyu.edu @sosulski kristensosulski.com
  30. 30. By checking for these five errors made by data visualization software, you’ll be on your way to creating data visualizations like a pro. 30Copyright 2016 Kristen Sosulski ks123@nyu.edu @sosulski kristensosulski.com
  31. 31. Are there any other errors that you’ve come across in your data visualization work? Do you have any questions? Contact me on twitter @sosulski. You can learn more on my blog at http://kristensosulski.com 31 Questions? Comments? Copyright 2016 Kristen Sosulski ks123@nyu.edu @sosulski kristensosulski.com
  32. 32. Thank you! 32 Professor Kristen Sosulski, Ed.D New York University Stern School of Business @sosulski | ks123@nyu.edu | kristensosulski.com

Notas del editor

  • In this session you will learn strategies for
    telling a story using data. Emphasis will be placed
    on creating readable and interpretable
    presentations.

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