Question

Introduction

Information design is a field that studies the way information should be displayed, which is based on the nature of the information that is being presented, and the way humans perceive that information. For instance, the data may provide a lot of information, but in order for us to understand it, it should be presented in a simple and clear way. Information design includes more than data visualization, for example, the design of indoor navigation in a mall.

listed below some principles that can help display any type of information:

• Show the data
• Induce the viewer to think about the substance rather than about methodology, graphic design, the technology...or something else
• Avoid distorting what the data have to say
• Present many numbers in a small space
• Make large data sets coherent
• Encourage the eye to compare different pieces of data
• Reveal the data at several levels of detail, from a broad overview to the fine structure
• Serve a reasonably clear purpose: description, exploration, tabulation, or decoration
• Be closely integrated with the statistical and verbal descriptions of a data set."

Directions

Pick three principles. Explain the three principles selected and explain how you understand the meaning of each principle, and what it would mean for visualization practitioners. Find examples of business data visualizations to illustrate each of the selected principles. Explain how the examples illustrate the principle.

Answer 1

The three principles which are picked:

1.Show the data

2.Serve a reasonably clear purpose: description, exploration, tabulation, or decoration

3.Reveal the data at several levels of detail, from a broad overview to the fine structure

Explanation:;

1.Show the data:

Showing the data in different formats plays major role in describing the ideas.Various ways of showing the data are

1.Graphical representation

2.Excel

3.Symbolic Representation

4.In form of instructions/ Guidelines

2.Serve a reasonably clear purpose: description, exploration, tabulation, or decoration

The data defined form of description than in single word is the best way to define the data.The data representation in tabulation is more understandable.

Tabulation is a systematic & logical presentation of numeric data in rows and columns, to facilitate comparison and statistical analysis. It facilitates comparison by bringing related information close to each other and helps in further statistical analysis and interpretation.

To put it in other words, the method of placing organised data into a tabular form is called as tabulation. It may be complex, double or simple depending upon the nature of categorisation.

Also Check: Tabular Presentation of Data

5 Major Objectives Of Tabulation:

(1) To Simplify the Complex Data

• It reduces the bulk of information i.e. raw data in a simplified and meaningful form so that it could be easily by a common man in less time.

(2) To Bring Out Essential Features of the Data

• It brings out the chief/main characteristics of data.
• It presents facts clearly and precisely without textual explanation.

(3) To Facilitate Comparison

• Presentation of data in row & column is helpful in simultaneous detailed comparison on the basis of several parameters.

(4) To Facilitate Statistical Analysis

• Tables serve as the best source of organised data for further statistical analysis.
• The task of computing average, dispersion, correlation, etc. becomes much easier if data is presented in the form of a table.

Describing and documenting data is essential in ensuring that the researcher, and others who may need to use the data, can make sense of the data and understand the processes that have been followed in the collection, processing, and analysis of the data.

Research data is any physical and/or digital materials that are collected, observed, or created in research activity for purposes of analysis to produce original research results or creative works.

Research data can be generated for different purposes and through different processes, and can be divided into different categories such as numerical, descriptive or visual. Moreover, data may be raw or analysed, experimental or observational, confidential or publicly accessible. Research data can include laboratory notebooks, field notebooks, primary research data, questionnaires, audiotapes, videotapes, models, photographs, films and test responses.

3.Reveal the data at several levels of detail, from a broad overview to the fine structure

It is generally understood that a specific characteristic (feature/column) of structured data can be broken down into one of four levels of data. The levels are:

• The nominal level
• The ordinal level
• The interval level
• The ratio level

As we move down the list, we gain more structure and, therefore, more returns from our analysis. Each level comes with its own accepted practice in measuring the center of the data. We usually think of the mean/average as being an acceptable form of center, however, this is only true for a specific type of data.

Nominal Data
Nominal data is named data which can be separated into discrete categories which do not overlap. A common example of nominal data is gender; male and female. Other examples include eye colour and hair colour. An easy way to remember this type of data is that nominal sounds like named, nominal = named.

Ordinal Data
Ordinal data is data which is placed into some kind of order or scale. (Again, this is easy to remember because ordinal sounds like order). An example of ordinal data is rating happiness on a scale of 1-10.

In scale data there is no standardised value for the difference from one score to the next. This can be explained in terms of positions in a race (1^st, 2^nd, 3^rd etc). This is ordinal data because the runners are placed in order of who completed the race in the fastest time to the slowest time, but there is no standardised difference in time between the scores. For example the difference in time between the runners in first place and second place is by no means the same as the difference in time between the runners in second and third place.

Interval Data
Interval data is data which comes in the form of a numerical value where the difference between points is standardised and meaningful. The most common example of interval data is temperature, the difference in temperature between 10-20 degrees is the same as the difference in temperature between 20-30 degrees.

Ratio Data
Ratio data is much like interval data – it must be numerical values where the difference between points is standardised and meaningful. However, in order for data to be considered ratio data it must have a true zero, meaning it is not possible to have negative values in ratio data. An example of ratio data is measurements of height be that centimetres, metres, inches or feet. It is not possible to have a negative height. When comparing this to temperature it is easy to consider the difference between interval and ratio (which may be a little confusing at first!), as it is possible for the temperature to be -10 degrees, but nothing can be – 10 inches tall.