# 1. Visualization Definition

A representation of numbers and/or text and their interrelationships with symbols, text, and numbers.

# 2. Visualization Categories

There are three broad and overlapping categories of visualization:

1. Data Visualization,
2. Scientific Visualization, and
3. Information Visualization.

There is not a consistent usage of these terms. Part of the issue is that scientific or information visualization practitioners use data, so the visuals associated with each one may reasonably be called a "data visualization". In addition, one may be tempted to refer to visualizations created by scientists as scientific visualizations, but this is not always the case.

Roughly, there are three communities and what is involved in each can be inferred from the table of contents for the books and the software used by practitioners in each community of the References for this course.

1. Data Visualization - Statisticians/Measurements
2. Scientific Visualization (SciVis) - Scientists/Simulation Data
3. Information Visualization (InfoVis) - Economics & Statisticians/Measurements

In this course, we'll cover all three, but the most time will be spent on SciVis.

## 2.1. Data Visualization

From [1]

Data visualization or data visualisation is a modern branch of descriptive statistics. It involves the creation and study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information."

Note the emphasis on descriptive statistics. Example: https://plot.ly/r/

## 2.2. Scientific (SciVis)

From [2]

Scientific visualization (also spelled scientific visualisation) is an interdisciplinary branch of science according to Friendly (2008) "primarily concerned with the visualization of three-dimensional phenomena (architectural, meteorological, medical, biological, etc.), where the emphasis is on realistic renderings of volumes, surfaces, illumination sources, and so forth, perhaps with a dynamic (time) component.

From [3]

Scientific visualization is specifically concerned with the type of data that has a well defined representation in 2D or 3D space. Data that comes from simulation meshes and scanner data is well suited for this type of analysis.

Examples: http://www.paraview.org/gallery/

## 2.3. Information (InfoVis)

From [4]:

Information visualization or information visualisation is the study of (interactive) visual representations of abstract data to reinforce human cognition. The abstract data include both numerical and non-numerical data, such as text and geographic information. However, information visualization differs from scientific visualization: "it’s infovis [information visualization] when the spatial representation is chosen, and it’s scivis [scientific visualization] when the spatial representation is given".

## 2.4. SciVis vs. InfoViz

Content-wise InfoVis and SciVis can be distinguished according to the data with which they deal. SciVis is typically applied to data with an intrinsic spatial layout (e.g., a ﬂow simulation in 3D space), whereas InfoVis deals with data that has no pre-deﬁned spatialization (e.g., graphs of web links). Similarly, SciVis data tends to be continuous, whereas InfoVis data tends to be discrete in nature.

# 3. Data Terminology

• Data - Plural of datum - an individual piece of information.
• Metadata - Data about data. A file name may be considered metadata. If the file is named SunspotNumber.dat, the metadata tells you information that may be useful for interpreting or displaying the information inside of the file. A common pattern used by scientists is to have a file named README in the same directory as the data set or, if the data files are ASCII, to include descriptive text near the top of each file.
• Data Type - In this course, it will mean the mathematical structure of the data that is used to make a decision on how to display the data (e.g., scalar time series, image, 2-D vector field, spectrogram).
• Visualization Type - Scatter plot, time series, vector field, 3-D volume, spectrogram, histogram, periodogram, etc. See also [5] and the chart menu for MS Excel.
• Attributes - Metadata is used to describe a collection of data (usually numbers) at a high level. Each number (or collection of numbers) may have one or more associated attributes. For example, the data listed below are the numbers 0.1 and 0.3. These numbers have attributes A or B (perhaps to indicate who made the measurements). The time stamps may be considered to be attributes or data. What is considered as an attribute and what is data is often debatable. An informal definition to use is an attribute is something that you would use in a label or annotation on a scientific plot. In the example below, you may use a color to indicate if the number was measured by person A or B.
2001-01-02 0.1 A
2001-01-03 0.3 B


# 4. Data Types

Data may be measured, calculated, or the output from a simulation. The most common types are:

• Scalar
• Vector
• Matrix
• Tensor

Each number can have one or more attributes associated with it. For example, list of scalars each with an associated list of scalar time stamps or positions (or both).

# 5. Problems

## 5.1. Visualization Types

Find an example of three visualization types (DataViz, SciViz, InfoViz) on a web page or in a paper.