How did Data Visualization Importance change from Medieval to Modern Times?
By: Saurav Singla
Tags: Artificial Intelligence, Machine Learning, Data Visualization, Data, Data Science

Data Science, Data Visualization

How did Data Visualization Importance change from Medieval to Modern Times?

In data visualization, a lot of the developments and changes are made from old to present times

Source: David Tan, Unsplash

This article would offer an outline of the development of data visualization from old to present times, identifying and highlighting several important developments along the way.

Some time data does not make sense until we can look at it in a graphical or visual form. You can quickly visualize your dataset for yourself. It is an important skill for data scientists and statisticians both.

What do we mean by “visualize” data?

Data visualization is a graphic representation of information and data. It uses graphic elements such as graphs, charts, and maps. It offers an interactive way to view and interpret data trends and patterns.

“finding the artificial memory that best supports our natural means of perception” — Jacques Bertin, 1967 “the use of abstract, non-representational pictures to show numbers” — Edward Tufte, 1983 “categorical and quantitative information encoded, chiefly through position, shape, size, symbol and colour” — Cleveland & McGill, 1985

Historical Context of Data Visualization Design

Data visualization has been in use since the late 17th century. When Rene Descartes developed a two-dimensional coordinate system to display values. Visualization further increased when William Playfair invented the line, field, circle, and pie charts.

Since “data” is such a generic idea, and the process of “visualizing,” it can take several forms. The combined term “data visualization” avoids a single meaning.

Inquiry into the Permanent Causes of the Decline and Fall of Wealthy and Powerful Nations, William Playfair, 1805

Why visualize data?

We, as humans, grasp information through visualization. From a layman’s point of view, data visualization is the graphic representation of the data collected. It allows decision-makers to move more on the evidence visualized and delivered.

It gives the relationship between the data and the images. Data Visualization is important because it makes it easier to see trends and patterns. As a huge amount of data generated, we need to interpret data trends.

Machine learning plays a crucial role in the conduct of predictive modeling, which helps to visualize data.

Data visualization is important not for data scientists, data analysts, and business analysts. But also for data visualization to be understood in any career.

If we are working with advertising, marketing, development, architecture, one needs to interpret the results. This reality illustrates the importance of data visualization.

Data visualization has a lot of uses. At the same time, business needs and objectives, data visualization benefits in many ways to improve data insights.

Getting closer to data

Research conducted in 2013 on the physical response to emotional indices focused on 700 participants. Participants were given two outlines of the body and told to identify parts on their bodies.

They believed that the operation was increasing or decreasing on the grounds of the feeling they were focused on.

Summing up the findings as a spatial density heatmap. We can see how spatial data shows a simple tale about each emotion.

Bodily maps of emotions, Lauri Nummenmaaa, Enrico Glereana, Riitta Harib, Jari K. Hietanend, 2013

Understand the influence

In August 1854, the Cholera epidemic in Soho, London, was one of many such outbreaks in the area. At the time, it was exacerbated by population growth associated with inadequate sanitation and drainage — more than 1 in 10 people affected by cholera.

Dr. John Snow, in collaboration with others, researched people afflicted by cholera to attempt to find a cause of cholera.

Around the time the outbreak of cholera was not well known as a “germ phenomenon,” the Pasteur may not have identified until 1861.

His research described the Broad Street pump as a possible cause, and he advocated for the replacement of the pump handle to stop the epidemic.

Some regard this to be the origin of epidemiology research. The dot diagram, modified here, is a straightforward visualization of the data and how it applies to the Broad Street Pump.

On the mode of the communication of cholera, John Snow, 1854, A redrawing appearing in “The Visual Display of Quantitative Information,” Edward R Tufte, 1983

Discovers patterns

The search terms for “Statistician” and “Data Scientist” illustrate how the rise of the “Data Scientist” has overtaken the “Statistician” by an immense margin. They don’t have the same work, but they have striking similarities.

If you were a statistician searching for a new job, it’s a smart idea to think about “Data Scientist” as a potential career.

Google search term trends, 2004 to 2020

Enable decisions

In January 1986, at the temperature of-1 ° C (31 ° F), even colder than ever before, the NASA space shuttle orbiter Challenger took off. And crashed soon afterward, killing all seven crew members.

The resulting investigation of the worst disaster in Nasa’s history to date. Determined the rubber “O-ring” that sealed the sections of the solid rocket boosters (SRBs). And prevented the leakage of combustion gases.

Engineers that worked for Morton Thiokol, the firm that developed the SRBs. Claimed that the O-rings were not tested at these low temperatures. At a critical conference, two slides produced.

One states SRBS had not checked below 53 ° F. And the other, a list of situations that had seen o-ring failures happening. Such data points highlighted in the blue box.

Many claimed, such as Edward Tufte, that there was a far better possibility of persuading NASA managers. To delay the mission by planning data points.

Yet, for real, we cannot claim that. NASA's‘ go-fever’ was the product of a variety of external and internal corporate challenges to get going.

What may claim is that, by visualizing the results, it is much easier to consider the possible danger of launching at low temperatures.

Visual and Statistical Thinking, Edward Tufte, 1997

Inspire action

Born in 1820, Florence Nightingale was a social reformer, statistician, and pioneer of modern nursing.

Shocked by her experiences of British soldiers dying from preventable diseases in hospitals during the Crimean War between 1853 and 1856. She campaigned for better care.

The Nightingale ‘Rose’ is an example of her published research to display non-statisticians the figures behind the death of a soldier.

Her research has inspired governments to strengthen their military hospital services. In particular by reducing deaths from preventable diseases.

Matters affecting the health, efficiency and hospital administration, British Army Florence Nightingale, 1856

Contextualise information

This visualization displays a flow map of the catastrophic Russian campaign by Napoleon Bonaparte between 1812 and 1813. Drawing by Charles Minard, published in 1869, was a civil engineer and a pioneer in the use of graphics in engineering and statistics.

Edward Tufte classifies this as the best statistical graphic ever made. But that’s unclear.

This does very well to translate a lot of variables into a single image. This shows the size of the army, the geographical location, the route traveled, how groups split off on some days. And, this shows the very cold weather of the retreat.

Good Visualisation

The gap minder is the dynamic bubble plot visualization of countries over time. Famous for Professor Hans Rosling in a compelling TED presentation in 2007.

It’s on YouTube if you haven’t seen it before. This displays the encoding of many variables and is necessary to express the data in a relatable manner to most audiences.

Life Expectancy Bubble chart, Hans Rosling, Gapminder

Bad Visualisation

This graphic indicates an inability to visually convey the results. In reality, it deliberately prevents understanding that is amazing, but not a positive thing.

The graphic is too dense to understand without a detailed examination, and the bar markings increase the clumsiness.

It encodes only one value, and it doesn’t do that well. The x-axis curved and the entire dataset is not represented on one dimension, which thus avoids simple visual comparison.

The focal point of the graphic is the black bar in the center, but the key information is in the graphic on either side. How can the eye scan the graphic?

Life expectancy at birth, Macelo Duhalde

William Playfair

Scottish political economist born 1759. He considered one of the great thinkers of data visualization.

Playfair had many jobs in his career, but the ones that stood out were as a designer of engineering drawings and in his later work as an economist.

During his later research as an economist, Playfair tried to avoid the tabulated details, with which policymakers had shared their information.

Using his training as a cartographer and his knowledge of cartography, he wrote “The Economic and Political Atlas” in 1786. He’s credited with forever altering the way we look at the data. He formalized the bar, the line, and the pie charts.

Jacques Bertin

Jacques Bertin, born in 1918, was a geographer and cartographer. His job Sémiologie Graphique known to be one of the key texts on data visualization.

The research on statistical graphics in terms of prioritizing information encoding forms of ‘visual variables’ was ahead of his day.

Semiologie Graphique, 1967

John W. Tukey

Raised in 1915, he was a legendary American statistician. He has contributed a lot to the field of statistics, but we interested in his approach to data analysis and visualization.

He advocated for the example of an exploratory approach. Analysis of data to draw an inference on a confirmatory approach.

For example, data used to test a hypothesis. In particular, he called for the use of visualization to analyze data. In 1977, he published a book on exploratory data analysis in “Exploratory Data Analysis” and, in doing so, he invented a box pattern.

Boxplot, Exploratory Data Analysis, 1977

Edward Tufte

Raised in 1942, he is a lecturer at Yale University. In 1975, he was invited to teach statistics to a group of student journalists in Princeton. He considered the available statistical graphs to be deficient in visual quality.

This original course became a series of lectures with John W. Tukey. And a self-published book, “The Visual Display of quantitative information” in 1982.

Many may believe Tufte to be the “godfather” of data visualization. The definition of data-ink is essential to the process of redesign.

The data-ink ratio is the proportion of ink used to show real data relative to the amount of ink used in the entire display.

Data Ink Ratio, Edward Tufte, 1983

Conclusion

In the past three decades, the use of graphics has been more popular. In the second half of the 20th century, prominent scholars started to develop the science and art of data visualization and to bring it to greater heights.

Information visualization may offer insight into the weakness of conventional descriptive statistics.

Given the present accelerated proliferation of data, the data visualization approach is an effective and cohesive way to digitally express quantitative content that allows users to access vast quantities of information more efficiently than rapidly.