Answer 1
Annotations to Enhance Graphics
Data visualizations convert complex data into readable and interactive data by providing the features that make the data easy to understand and effective when read. Annotations are among the considerations that make complex data easier to read by providing the users with the necessary support to understand the data in the visualizations. Annotations entail determining the amount of help that the consumers of a visualization need to understand data visualizations (Kirk, 2019). When creating visualizations, one must understand the users to determine if their capabilities support their understanding of the data visualizations. If the users could use some help, the developers determine the right help to enable the readers to understand the visualizations. The screenshot below is from Energy Upgrade California’s website (California Public Utilities Commission and California Energy Commission, 2020). It contains information about the rising temperatures using the historical average to show the current and changing heat waves. While the graphic is clear and has no distractions, annotations would help the readers interact more with the graphic and make it more readable.
The readings along the Y-axis will entail many estimations since the users have different accuracy levels when reading the provided scale. The graphic could use an annotated caption at the edge or along the shaded area showing different temperatures to help readers understand the represented Y-axis’s values and the data. Captions will provide localized values of the temperature value represented at the point the user hovers their mouse. The essence of using annotated captions is to help the readers easily read the temperatures without struggling to align the temperature level with the matching value on the Y-axis. Besides, the captions should have information concerning the years on the X-axis and the city. As a result, the users will find the visualization interactive and friendly, leading to the consumption of its intended information.
References
California Public Utilities Commission and California Energy Commission. (2020). Climate change in California: Facts, effects and solutions. Energy Upgrade California. https://www.energyupgradeca.org/climate-change/.
Kirk, A. (2019). Data visualisation: a handbook for data driven design (2nd ed.). Sage.
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Answer 2
Annotation
Annotation is the crucial components denoted as a drawing, charts, or graphs that provide any extra information or ideas highlighted in the interest of using the disambiguation purposes. A presentation should establish two features of the presented data: Show links within the information that are too complicated to express with words. Make it easy for the audience to immediately absorb the information offered and analyze the consequences from that data. Annotation creates a clear component that turns the boring graphical representation into a meaningful or interesting way of conveying the information (Kirk, 2015). However, adding the accuracy of the data into the charts or graphical representation enables the audience to understand the presentation.
Using this website as a data annotation platform data-to-viz.com/caveat/annotation.html”>https://www.data-to-viz.com/caveat/annotation.html. It contains the dataset of figures of spaghetti charts which elaborate on the groups of the outcomes that have confusing figures that are hard to understand. Hence, every pattern is hard to be elaborated (Sinar, 2015).
Computers can analyze the world via a visual lens or a fresh, enlightened viewpoint in the digital era. Image annotation is one of the most important jobs a computer has. Computer vision, robotic vision, face recognition, and machine learning systems to analyze pictures depend on image annotation (Kirk, 2015). In enhancing the graphical representation above to have a clear understanding, I will highlight the main point such that when I want to elaborate on the characteristics of Amanda, I will increase the intensity of the line of Amanda shown below:
References
Kirk, A. (2015). Data Visualization: a successful design process. Packt publishing LTD. https://www.data-to-viz.com/caveat/annotation.html
Sinar, E. F. (2015). Data visualization. In Big Data at Work (pp. 129-171). Routledge.
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