Visualisation Appraisal

Introduction

Data refers to a set of facts collected with a particular reason in a raw format which then has to be summarised, organised and analysed in pursuit to extract meaning from it. After the data is analysed, it is often presented in a particular way depending on its intended use (Cleveland, and McGill, 1984). This essay is a construction of two visual presentations seeking to compare and contrast them on the premises of their suitability and appeal to the eye. The first visual presentation represents “The Evolution of FIFA World Cup Prize Money” which typically presents her information in a precise and formative way while harnessing visualization principal. The second presentation depicts “The prospects of Microsoft Word in the Wiki-based World” and is a typical example of a wrongly presented chart without prior consideration of visual aids and eye-appeals. Deliberately, both the charts are chosen for two diversified fields to substantiate them distinctly and more precisely as to how they are visually a good or poor in the graphical representation of data.

Data visualization can be described as the representation of data by use of visual aids. Therefore, it can take shape of a bar chart, column chart, Scatter Plot, pie chart, heat map or anything equivalent that meant to make the data accessible to understand. Data visualisation in one of the most essential parts of data science and some scholars describe it as the art of turning raw numerical or nominal data into a visual representation that makes people understand its significance at a glance (Chambers, 2017). It allows us to instantly scan through massive amounts of data and derive meaningful patterns from it. These patterns help learners or consumers of data to derive insights for making decisions based on those insights. Clear data representation leads to conception of an idea which can form narrative that help create data store. The positive significance of data visualization can be theorized in Hans Rosling’s famous quotation that “Most of us need to listen to the music to understand how beautiful it is. But often that’s how we present statistics: we dispense the note we don’t play the tune” (Wilkinson, 1993).

Data can be visualized through different shapes whenever is needed to make the data memorable, attractive, beautiful, and present a precise meaning, but all these are limited to less quantity data. When large amounts of data are involved; the application of visual aids; becomes ineffective. Besides, it is not necessary to exaggerate and overstress on a particular fact to make it over sizzling to seek the attention of readers, because it is essential to understand that the readers prefer to read an article to attain knowledge about the event (Bertin, Berg, and Wainer, 1983). The advent of new techniques which can analyse large volumes of data within a fraction of a second have significantly induced efficiency in the domain of data presentation in the contemporary world of large data volumes and decisions executed at a faster rate (Wainer, 1990)

Critical Thinking

There is no shortage of principles and rules governing data visualization. This appraisal harnesses Edward Tufte’s six principles; and William Cleveland’s book “The Elements of Graphing Data” in an attempt to explore the element of a good graphical presentation.

Good Visualisation of Data

The bar chart below (Figure 1) is shows a comparison FIFA World Cup Prize Amount in where the event was held and when. It is a good example of the use of data visualization.

The factors which make this graph a good visualisation example are summarised as the following:

• Type of the chart: As the main purpose for presenting this data was the comparison of different values over a timeline, which is the World Cup Prize Money in this case, bar chart was good type of chart for this presentation and made it easy to convey the story that the researcher intended to show.
• Labelling: The chart title is concise and descriptive, and labelled in a comprehensive and easy way to interpret by the reader. The researcher used the flags icons to indicate the host country to enrich the visualisation. Besides; there is no use any acronyms or not ambiguous terms.
• The prize amount uses an international standard of currency denomination, which is US dollars: Throughout the chart there is no use of a different currency that varies with respect to the different countries where the events were hosted. As such, uniformity in currency is maintained though the chart, though the event was held in different countries such as Spain, Russia, and France.
• Data sort: The data is sorted based on timeline which gives more value to the chart. The variation in the amount can be clearly inferred from the chart. Precisely, during 1982 the FIFA World Cup was held in Spain, whereas in 2018 it was held in Russia and the prize amount was $20 million and $791 million in 1982 and 2018 respectively. As such the pattern of the entire event has not been tampered with any hidden motives and the variation in the data has been clearly justified.
• Colouring: the chart has one colour for the same dataset, which help the reader to grasp the data and make it easily to compare without distracting by finding the meaning of using different colours (Grady, 2006).
• The Design: The researcher made the design of the graph simple, clear and informative without adding anything that might not give a value to the reader. There is no usage of borders lines; no exaggeration of colours, redundant labels and bolding.
• Bars space: The spaces between bars are not big and it is the same for all bars.
• Scales: the length of the bars reflects the exact change of the data and shows the correct difference.
• Further, the chart has not been deviated and it emphasises on the single dimension, which is focused on the describing the variation in the price amount from the very beginning of the article to the conclusion.
• There is no ambiguity or any unintended context throughout the flow of article.

Bad Visualisation of Data

Pie Chart is simple method of representing simple data, but commonly, angles are more difficult to compare than lengths. This is why; pie charts are considered appropriate in demonstrating noticeable differences in proportions for a few data. It must be used with caution as this visual representation could mislead the audience easily. According to Tufte, Goeler, and Benson, (1990), pie charts are not effective in comparing variables within spatial disarray and those within the pies. The pie chart below (Figure 2) shows the features of “Microsoft Word” software by versions.

There are many variables which make this chart congested and thus ineffective in representing the intended deliverables. The weaknesses in the above presentation are such as:

• Type of Chart: the pie chart trying to illustrate the number of new features added for each version during time period. The purpose of representing this data in a pie chart is not justifiable as it cannot be used to analyse trends in data set during a specific time period. Besides, the pie chart above represents the composition of data where the total summation of all parts should add up to 100%, while the data of Microsoft Words features should not be because each segment is totally different features and they are not forming one unit.
• Number of fractions: pie chart should not illustrate a lot of variables, because that would make many small portions, which are difficult to be distinguished well by the reader.
• 3D effects: 3D pie chart makes the judgment to the size of the fractions more difficult because of the false perspective the rear portions appear too small.
• Labelling: The pie chart above has no labels to show the values of each slice. So it does not show any values and does not give the reader critical information about the number of features in version, which makes it incomprehensive.

Large quantities of data cannot be presented effectively using a pie chart. This therefore requires a different method. According to Kirk, (2016), data presented on the pie chart can be equally presented better through other techniques. Suggestively, a line bar graph or bar graphs can instead be utilized since they are clearer, quicker and easier to interpret.

Conclusion

Data visualization can be presented in many different ways in pursuit to lay foundation for easy comparisons, relationships and co-relationships. The selection of the presentation method largely depends on the data whether it is relational, comparative or time-based Thus, visualizing data using the wrong technique will consequently result into confusing and misleading on the part of the data consumer. The above examples (bar graph and pie chart) have been harnessed in this task to demonstrate how the nature of data to be presented is vital in the selection of data visualization method (Ellison, 1994). The essence of data visualization is to present complex information provide in a simple manner that can be easily perceived and understood; alongside providing visually-appealing demonstrating that are not only catchy to the eye but also easy to interpret.

In the realm of empirical research, the knowledge on data collection, analysis, and presentation is instrumental. This knowledge helps in making research easily understood by the consumers of the research findings. The ability to statistically present data in a professional way is a normative subject in the field of research. The research can apply tools texts, graphs or tables to present their findings. Such tools help readers comprehend the research content, captivate their study interest and efficiently narrow down complex information on paper in compressed form. Mastery of understanding of various data presentation methods help to understand wrongly presented and deceptive data (Kinross, 1990).

References

• Bertin, J., Berg, W.J. and Wainer, H., 1983. Semiology of graphics: diagrams, networks, maps (Vol. 1, No. 0). Madison: University of Wisconsin press.
• Chambers, J.M., 2017. Graphical Methods for Data Analysis: 0. Chapman and Hall/CRC.
• Cleveland, W.S. and McGill, R., 1984. Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387), pp.531-554.
• Ellison, A.M., 1994. Right between the eyes--Visualizing Data by William S. Cleveland. BioScience, 44(9), p.622.
• Grady, J., 2006. Edward Tufte and the promise of a visual social science. Visual cultures of science: Rethinking representational practices in knowledge building and science communication, pp.222-65.
• Kinross, R., 1990. Richness against flatness: Edward Tufte's envisioning information. Information Design Journal, 6(3), pp.221-228.
• Kirk, A., 2016. Data visualisation: a handbook for data driven design. Sage. Tufte, E.R., Goeler, N.H. and Benson, R., 1990. Envisioning information (Vol. 126). Cheshire, CT: Graphics press.
• Wainer, H., 1990. Graphical Visions from William Playfair to John Tukey. Statistical Science, pp.340-346.
• Wilkinson, L., 1993. Comment on “A Model for Studying Display Methods of Statistical Graphics”. Journal of Computational and Graphical Statistics, 2(4), pp.355-360.