Designing Graphs for Decision-Makers

Datos Bibliográficos

ID22124682
AutoresJeffrey M Zacks (0000-0003-1171-3690, Washington University in St. Louis, autor de correspondencia), Steven Franconeri (0000-0001-5244-9764, Northwestern University), Steven L Franconeri (Northwestern University, Evanston, IL, USA)
Año2020
Volumen7
Número1
Páginas52-63
Fecha de publicación2020-03-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaPolicy Insights from the Behavioral and Brain Sciences (JOURNAL)
Identificadores de la revistaISSN: 2372-7322 • E-ISSN: 2372-7330
EditorialSAGE Publications (PUBLISHER • US)
DOI10.1177/2372732219893712
OpenAlexW3011155372
IdiomaEN
Citas recibidas4
Referencias citadas28

Data graphics can be a powerful aid to decision-making—if they are designed to mesh well with human vision and understanding. Perceiving data values can be more precise for some graphical types, such as a scatterplot, and less precise for others, such as a heatmap. The eye can extract some types of statistics from large arrays in an eyeblink, as quickly as recognizing an object or face. But perceiving some patterns in visualized numbers—particularly comparisons within a dataset—is slow and effortful, unfolding over a series of operations that are guided by attention and previous experience. Effective data graphics map important messages onto visual patterns that are easily extracted, likely to be attended, and as consistent as possible with the audience’s previous experience. User-centered design methods, which rely on iteration and experimentation to improve a design, are critical tools for creating effective data visualizations

Computer graphics · Graphics · Human–computer interaction · Machine learning · Visualization · Aesthetic Perception and Analysis · Computer Science · Data Visualization and Analytics · Visual Attention and Saliency Detection · Artificial Intelligence