Data Visualization Based on Gestalt Theory

As might you know our brain had the most data intake as compared to other orgasms. The optic nerves deliver data to the brain at incredible speeds, which the brain then analyses and adjusts to the surroundings. Our brain recognises forms and colors, analyses and recognises boundaries effectively, and adapts to specific patterns. In this regard, visualising data is far more appealing to people than statistics and context.

Understanding perception is essential for improving the quality and amount of visualisation (Ware, 2000). The importance of perception has led to numerous studies by researchers and scientists in this field and one of the most significant is the Gestalt theory, which presented a new notion of perception known as the six rules of visualisation that govern the human perception of various things and surroundings (Koffka, 1935).

What is Gestalt theory?

Gestalt is a German term that translates to "pattern". Gestalt psychologists emphasise "the whole is greater than the sum of its parts" (STERNBERG, MIO and J.STERNBERG, 2012), and they have sought to anticipate the interpretation of the senses, which is commonly referred to as the "Gestalt laws" (Todorovic, 2008).

In its most basic form, Gestalt theory claims that when faced with complicated visuals or designs, the human brain will unconsciously attempt to simplify and organize the arrangement of components in an organized system that produces a whole. Thus, our brains are designed to see structures and patterns to understand better the environment we live in.

The six fundamental Gestalt rules of visualisation are as follows:

1. Closure: According to this law, the human brain tends to view

everything in the whole shape; therefore, we naturally fill in the gaps to grasp the entire shape.

2. Common Proximately: Closer elements to one other are considered as a group element, according to the low of Common Proximately.

3. Similarity: According to this law, comparable items are visually grouped regardless of their position.

4. Symmetry: This rule is also known as "prgnaz", a German term that means "excellent

figure." The mind prefers to put together items that are symmetrical to one other.

5. Common Fat: The law of Fat which is recently embedded in the Gestalt laws, states that objects moving together are perceived as a group.

6. Continuation: The law of continuity points that the mind will tend to follow the

smoothest path when viewing lines, regardless of how the lines were drawn.

In this article, I attempt to create a dashboard based on Gestalt principles in order to demonstrate Covid-19 death vs Mortality.

Project:

In this study, I aimed to visualize the influence of vaccination on covid-19 death rates and provide an overview of the current coronavirus situation.

Actually, I completed several excellent Tableau courses provided by Matt Francis on LinkedIn, and I'd like to share my learning of tableau and Gestalt theory in this attempt.

Finding the datasets and Cleaning data was the first part of my visualisation, and I chose large

datasets from Kaggle, which needed to reduce some columns and extra information to handle

more easily datasets.

You can find source of the datasets below:

- Covid-19 World Vaccination Progress Data

  The initial Dashboard displayed an overview of covid-19 static across many countries over time, from the founding of virions until the present, with top countries in vaccination and death level.

The second Dashboard is more focused on the impact of vaccination on the death of covid-19. As can be seen in the Dashboard, the vaccination pretty much reduces the death level, specifically after two doses and fully vaccination.

How is Gestalt theory employed?

The dashboards are effective at bringing graphs closer together so that each subject may be more easily classified. Elements that have a boundary around them are perceived to be more related (Koffka, 1935).

Three colors are employed in the first Dashboard (Green, Red, White) to highlight distinct payment elements, making it easier for the viewer to grasp and label. Features that appear to be similar are perceived to be more related. "Similar" can be in shape, orientation, size, or color(Koffka, 1935)

More specific information is mentioned in the below picture.

In conclusion, despite the aim of this attempt to practice and get insight into the tableau and

visualization, I want to notice simple line graphs of data illustrated that vaccination significantly

decreases the death rate of coronaviruses worldwide. However, vaccinations did not affect the death rate at first because not many people were vaccinated, and the vaccine needed time to work.

Here some of my visualizations is embedded or you can find my work at public tableau in the blow link. Thank you for taking the time to look through my work.

https://public.tableau.com/shared/4ZFRT5Q2P?:display_count=n&:origin=viz_share_link