SCU MSIS 2629 Spring Self Study Project
This is a data visualization effectiveness evaluation on climate change caused by human activity.
According to Stephan Few's paper:
Stephen Few (2017): Data Visualization Effectiveness Profile; Perceptual Edge. Visual Business Intelligence Newsletter; (https://www.perceptualedge.com/articles/visual_business_intelligence/data_visualization_effectiveness_profile.pdf)
Stephen Few is a consultant, educator, and author who has over 25 years of experience in information technology and focuses on practical uses of data visualization to explore, analyze, and present quantitative business information.
I come up with the following data visualization framework.
- Informative
- Usefulness
- Completeness
- Perceptibility
- Truthfulness
- Intuitiveness
- Emotive
- Aesthetics
- Engagement
Usefulness This first criterion is one that can only be determined in light of the audience’s needs. A data visualization is of little value if it helps people understand something that doesn’t matter to them.
Completeness An effective data visualization includes all of the information that’s needed to produce the intended level of understanding, but not more. This involves the right information and the right amount of it. This also means that all of the context that’s needed to understand the information has been provided as well.
Perceptibility The information must be displayed in a manner that the human eye and brain can perceive with minimal effort and appropriate precision. This involves selecting the type of graph that displays the information most effectively and designing it in a way that presents the information as clearly as possible.
Truthfulness By truthfulness, I mean the degree to which a data visualization is accurate and valid. Accuracy is a measure of reliability and appropriate precision. Validity indicates how well something represents what it claims.
Intuitiveness A data visualization is intuitive to the degree that it is familiar and easily understood. There are times when an unfamiliar form of display is preferable because it communicates the intended message more clearly than any familiar form of display could do.
Aesthetics The aesthetic quality of a data visualization can range from ugly (a display that no one would choose to view for long) to beautiful (one that would invite even those who care little for the information to take perhaps delight in learning about it).
Engagement By engagement, I mean a quality that can be achieved by various means, including but not limited to aesthetics, that invites the audience to examine the information.
This graph is an interactive gif useful for educating audience about how human causes the climate change. The graph consists of all the information (human factors, natural factors, and observed temperature changes) needed to demonstrate its intended meaning that human does cause global warming. It is clearly shown that the human factors are highly correlated with the observed temperature changes The perceptibility is good as the information displayed is easy on the human eye and brain. The caption summarizes effect of all factors on temperature and provides intuitive context for the viewers. It is also aesthetically pleasing with distinctive color on a white background. The gif format catches the users' eyes and stimulates viewers interest to scroll down to read more.
This graph is quite useful and informational as it clearly and accurately rerpesents the effect of both natural and human factors on observed temperature changes from 1950-2017. The graph is very intuitive for the viewers as it consists of all the visual elements of a typical scientific graph: a title, x and y axis, lines, and legends. From the graph we can clearly see that the human factors (greenhouse gases) best fits the observed dots and easily conclude that human are the major cause of the climate change. The vibrant color scheme on a plain background catches the viewers eye and engages the audience.
This is an interesting graph that shows the global climate change on every continent on land and on ocean. The world map on the background draws familiarity of the viewers and provides intuition for the viewers that they are looking at the temperature change for each continent. There are seven panel graphs, each showing the temperature anomaly from 1900 to 2000s. However, there is no legend indicating which color (blue or pink) represents the human or natural factors and what is the difference between the solid black lines and dotted lines. The style and color are consistent for all panel graphs and are thus pleasing to look at.
This graph is quite useful and informational as it clearly and accurately represents the effect of both natural and human factors on observed temperature changes from 1900-2000s. The graph displays all the visual elements of a typical scientific graph: a title, x and y axis, lines, and legends, which is familiar and intuitive for the viewers. Instead of using the best fitted line, it uses blue and green shading to show the effect of natural factors and natural and human factors on temperature change. The gap between the two shading indicates the effect of human factors on climate change. We can clear see that the natural factors alone do not change the temperature much and thus conclude that human are responsible for global warming. The low saturation of color on a plain background is eye-pleasing.
This graph is informational but overwhelming. Like the typical graph it has all the visual elements but it is multi-dimensional, with a stripe on the top labelling the historical periods and a caption at the bottom showing millions or thousands of year before present. The left x-axis is the temperature in Celsius and right x-axis is the temperature measured in Fahrenheit. Despite its overcrowding features, the line graph clearly demonstrates that the temperature on earth today is not higher compared to millions of years ago. Therefore, the audience can intuitively conclude that human does not cause global warming. The design of this graph is quite clean. The sharp contrast between yellow and green, red and blue attracts attention of the viewers and are easy on the eye.
According to the visualization framework, the first graph, Human Cause Climate Change 1, is the best one as it achieves all aspect of an effective visualization: useful, complete, perceptive, truthful, intuitive, aesthetically pleasing, and engaging. The gif format is particularly brilliant as it is engaging and memorable for the viewers and is calling for actions.
This is a Tableau replication of
Ed Hawkins (2018): Warming Stripes. Climate Lab Book3; Data: (https://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/download.html)
which features the following visualization:
