The purpose of visualization is insight, not pictures.
Ben Shneiderman
Why Data Vizualization?
During the last months, I have been investigating hard about Data Visualization – one of my biggest lack of deep knowledge… For my surprise topics such as User Experience, User-Centered Design, the “User” is something that fits DataViz so consistently! Everything makes sense, the research phase, the interactive and manipulable graph models, the management of overload information, the hierarchy, the guidelines, the UI coherency, and enormous improvements and legacy that came from marvelous static and historic infographics.
By the way, I posted images on my Linked In to share two astonishing historic infographics, if you did not see them, please read them below:
After learning some historical introduction, I definitely focused my attention on the first 5 steps that Riccardo Mazza recommends. Although the understanding of Dimensions is needed before making any stroke or write any word about the subject.
Dimensions
The more dimensions that are represented in the data – the more confusing it can be to comprehend the information visualization. Thus it’s worth noting that the data with large numbers of dimensions may well benefit from using a highly interactive representation rather than a static one.
Dimensions can be either dependent or independent of each other. It is the dependent dimensions that vary and which we would expect to need to analyze with respect to the independent dimensions.
Riccardo Mazza in his book “Introduction to Information Visualization” defines 5 steps to start thinking in Data Visualization.
- Define the problem
– What does my user need from this?
– How will they work with it?
– Who are my users? (skills, needs, goals of their respective roles)
– What kind of experience do they have with this data in the past? - Define the data to be represented
– Quantitative data? (123)
– Ordinal data? (temporal)
– Categorical data? (Without order) - Define the dimensions required to represent the data
– Univariate analysis – where a single dependent variable is studied against independent variables;
– Bivariate analysis – where two dependent variables are studied against independent variables;
– Trivariate analysis – where three dependent variables are studied against independent variables;
– Multivariate analysis – where more than three dependent variables are studied against independent variables. - Define the structures of the data
– Linear relationships – where data can be shown in linear formats such as tables, vectors, etc.;
– Temporal relationships – where data changes over the passage of time;
– Spatial relationships – data that relates to the real world (such as map data or an office floor plan) this is sometimes also known as a geographical relationship;
– Hierarchical relationships – data that relates to positions in a defined hierarchy (from an office management structure to a simple flowchart);
– Networked relationships – where the data relates to other entities within the same data. - Define the interaction required from the visualization
– Static models – these models are presented “as is” such as maps in a Road Atlas that you keep in a car. They cannot be modified by the user;
– Transformable models – these models enable the user to transform or modify data; They may allow the user to vary parameters for analysis or choose a different form of visual mapping for the data set;
– Manipulable models – these models give the user control over the generation of views. For example; they may allow a user to zoom in or zoom out on a model or to rotate 3-dimensional models in space for viewing from other angles.
Bibliography
- Mazza, Riccardo. Introduction to Information Visualization. 2009.
- Lima, Manuel. Visual Complexity: Mapping Patterns of Information. Princeton Architectural Press, 2013.
