# The Value of Visualisation

###### Why create a Visualisation?

- Answer questions (or discover them)
- Make decisions
- See data in context
- Expand memory
- Support graphical calculation
- Find patterns and trends
- Present arguments or tell a story

> A picture is worth a 1000 words

**Record** information

- Blueprints, photographs, seismographs

**Communicate** information to others

- Share and persuade
    - Think of Florence Nightingale using a graph to show that infection was the leading cause of death in hospitals
- Collaborate and revise
    - Think of the London tube map: before it was geographically accurate, now it is only topologically accurate

Analyse data to **support reasoning**

- Find patterns
    - Think of the London Cholera map, how John Snow found out where the infection was coming from
- Discover errors in data
- Expand memory
    - Imagine doing a sum like $34\times 52$ mentally versus with a pen and paper
    - Visualising the sum (column multiplication) can expand your memory
- Develop and assess hypotheses

#### Different Stages of Visualisation

![1645033524.png](img/1645033524.png)

- Data transformation
    - Create a structural model, schema, mapping raw data into data tables
- Visual Mapping
    - Create a visual spatial model, transforming data tables into visual structures
- View Transformations
    - Create views of the Visual Structures by specifying graphical parameters such as position, scaling and clipping.

![1645033689.png](img/1645033689.png)

###### Acquire

- Obtain the data, whether from a file on a disk or network

###### Parse

- Provide some structure for the data’s meaning, and order it into categories

###### Filter

- Remove all but interesting data

###### Mine

- Apply methods from statistics or data mining as a way to discern patterns or place the data in mathematical context
    - Work out mean, standard deviation etc

###### Represent

- Choose a visual model
    - Bar chart, graph, pie chart etc

###### Refine

- Improve the basic representation to make it clearer and more engaging

###### Interact

- Add methods for manipulating the data or controlling what features are visible

##### Interaction is Vital for Exploration

- Engage in a dialogue with your data
- Employ interaction in a more fundamental manner to strengthen the power of visualisation