Good Visualization Vs Bad Visualization

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As part 2 of the Data Visualization project, we were tasked to analyze good and bad form of visualization. To visualize the data in the most meaningful way, that can help journalist make decisions when integrating strategies and tools to produce and distribute immersive media content. The data can serve as primary guidance here.

With Immersive, non-linear storytelling techniques have many facets, visualization will help them make judicious when deciding how they want to implement it in a newsroom. Visualization can provide insight and guidance in selecting suitable stack of technologies.

kelowna-data-visualization-hiilite-venngageThe range band for the current dataset is recorded with time stamp for different sessions Participants, over the course of the session were shifted between three stations and sessions were scheduled over three 2.5 hours session.

It makes sense to choose a visualization type that can represent the range of lows and highs of Attention, Relaxation and Heart-rate per min for each sentiment.

 

 

Line Graph

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Line Graph is best suited to reveal the trends or progress over time, show acceleration (or decelerations) of sentiments recorded in this case. We have a continuous data set of timestamp and by using Line Graph, we can display how data changes over time for each line representing Attentions, Relaxation and Heart-rate.

Line Graph is a good candidate, simple and intuitive for comparing one or many value sets, and easily able to show the low and high values of sentiments expressed while watching the story videos. “This is especially true when there are multiple emotions to compare” Says Ritchie S. King,author of Visual Storytelling with D3: an introduction to Data Visualization. Further, he adds, “it is a better option because it provides more help to our eyes when we try to figure out how a value is developing over time”.

Scatterplot

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Scatterplot  rendering will help understand the distribution of your data, to understand outliers, the normal tendency, and the range of emotions in your values. They display relationships in how data changes over a period of time. when visualizing a time series.

Both line graph and scatter plot are good candidate to visualize how a data set performed during a specific time period. To outline the trends of power/intensity between different VR devices, scatterplot will depict the distance and tension between these and understand the relationship between value sets. Scatterplot and Line graphs also suited to showing how one variable relates to one or numerous different variables.

Stacked Column Chart

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Stacked charts handle part-to-whole relationships. This is when you are comparing data to itself rather than seeing a total – often as percentages. In this case where attention and relaxation are expressed in terms of percentage. Heart-rate can be one column set comprising 4 sentiments captured.

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In the above data, 44% of Open mindedness, 57% Fascination, 50% Stimulation and 44% Intensity signifies the differences between sentiments for Attention.

The numbers we are working with are relative only to our total. part-to-whole relationship and for this, we use a stacked bar graph. With proper spacing, we see each quarter clearly and the color coding shows that overall.

Bad Visualization

Data visualization guru Edward Tufte famously declared “pie charts are bad and that the only thing worse than one pie chart is lots of them.” Pie charts are also  not the best data visualization type to make precise comparisons. There are plenty of instances where pie chart should not be used. First off, pie charts portray a stagnate time frame. Therefore, trending data is off the table with pie charts.

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When showing single part-to-whole relationships, pie charts are the simplest way.  but when you have too much data, columns become very thin and ugly. This also leaves little room to properly label your chart. Reasons to best avoid pie chart as follows:

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  • Pie charts portray a stagnate time frame
  • Cannot represent trending data
  • Difficult to make precise comparison. In some cases, its difficult to clearly distinguish between different data points.

 

 

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