Components of Composition in Data Visualization
Composition concerns settling on cautious choices about the actual properties of, and connections between, each visual property to guarantee the ideal comprehensibility and which means of the strong venture or project (“Kirk”, 2019). The features of composition can be divided between project-level composition and chart-level composition options:
- Project composition: characterizing the design and chain of command of the whole representation project.
- Chart composition: characterizing the shape, size and design decisions for all parts inside your graphs.
Project composition includes two methodologies for molding our thoughts:
Wireframing: Wireframing includes outlining the possible format and size of the multitude of significant substance of your plan thinking across a single-page view (“Kirk”, 2019).
Storyboarding: Storyboarding is something you would attempt with wireframing in case you have a task that will involve various pages, or a wide range of perspectives and you need to build up an undeniable level feel for the general design of content, its route and sequencing (“Kirk”, 2019).
Composition charts are utilized to perceive how a piece of your information analyzes to the entire and can show relative and outright qualities. You can imagine creation over numerous periods with region diagrams, which are basically the same as line graphs, and stacked bar or section outlines when you have a decreased measure of periods (“Gonzalez”, 2020). Chart composition includes three different components:
Chart scales: While considering your chart scales attempt to think how you may utilize these to tell the watcher something significant. This can be accomplished through keen decisions around the most extreme worth reaches and furthermore in the decision of reasonable spans for marking and gridline guides (“Kirk”, 2019).
Chart orientation: Choices about the orientation of your outline and its substance can now and then assist press with trip an additional a level of meaningfulness and which means from your presentation (“Kirk”, 2019).
Chart Value Sorting: Sorting content inside an outline is significant for assisting watchers with finding and think about rapidly the most pertinent substance. Perhaps the most ideal method for thinking about the choices for esteem arranging comes from utilizing the LATCH abbreviation, concocted by Richard Saul Wurman, which represents the five different ways of getting sorted out showcases of information: Location, Alphabet, Time, Category or Hierarchy (“Kirk”, 2019).
All these components play important role in creating visualizations.
Gonzalez, A. (2020, January 23). Data visualization principles. Retrieved November 24, 2021, from https://alexgonzalezc.dev/posts/data-visualization-principles.html.
Kirk, A. (2019). Data Visualisation: A Handbook for Data Driven Design. Thousand Oaks, CA: Sage Publications, Ltd.
The composition is the concept of arranging different data objects in different order. The purpose is to allow users to view and manipulate the data visually while keeping the contents as simple as possible. This type of organization can also be implemented using hierarchical and extensible graphical models. For data visualization, the user is required to select which objects are displayed on different levels of the visual hierarchy and in different ways. In data visualization, it is essential to find the relationships between elements to understand which elements are relevant, and we can use them to inform the design. It is, in fact, the relationship between a pair of elements that is the information. We have found through observation that there is a specific correlation between the variables and a related variable. If we think about it, one way of visualizing the relationships among variables is through a scatter plot (O’connor et al., 2020).
A bubble chart can display the trends within the data and also the average of the past year. The more rows of data, the better. The bubble chart indicates the number of new entries per second created, how the entries have come in, and the average time between successive updates. The more data entries, the better. If the data is in the same format, then the average time between successive reads may be the same for the entire set. However, if the data is different, then the average time may vary. The data should be split into two or three distinct intervals (Becker et al., 2020).
The scatter plot illustrates the relationships among the variables. It also demonstrates the general distribution of the data and the general trend of the data. The scatter plot provides an informative way to visualize the relationships between the data elements, examine the relationships between the attributes, and explore the relationship. One of the advantages of the scatter plot is that it can be easily used to determine the variables’ value. In some cases, we may have to write a code that maps between the input values and the corresponding values in the local variables. The basic idea is to let the R functions read from the local variables, write to them, and write some additional output to the console to indicate the return values (Becker et al., 2020).
Becker, M., Lippel, J., Stuhlsatz, A., & Zielke, T. (2020). Robust dimensionality reduction for data visualization with deep neural networks. Graphical Models, 108, 101060.
O’connor, S., Waite, M., Duce, D., O’Donnell, A., & Ronquillo, C. (2020). Data visualization in health care: The Florence effect. Journal of advanced nursing, 76(7), 1488-1490.