![]() #> ok_blue ok_vermillion ok_redpurple ok_grey We must be conscious of this fact and respect our perceptual boundaries.#> ok_orange ok_skyblue ok_bluegreen ok_yellow Visual illusions remind us of the limitations of our visual perception system. Check out the following slideshow of some famous visual illusions (Only available online). Visual illusions, on the other hand, mess with these assumptions or exploit limitations in our visual processing system with surprising results. ![]() Most of the time, these assumptions hold and our brain’s construction of reality is good enough for us to survive. However, because of the complexity involved in converting light to vision, our brains use some short-cuts or assumptions to ensure that we can perceive vision as accurately and quickly as possible. The brain uses its enormous power to turn these signals into what we perceive as sight. What we perceive is our brain’s interpretation of light entering our eye and triggering electrical impulses from the cones and rods in our eyes. Our eyes and brain do not operate like a video recorder. ![]() Visual illusions demonstrate the sensitive nature of our powerful visual perception system. The following video by Lotto ( 2009) discusses the surprising ways in which optical illusions help us to understand human vision (Only available online). Be aware of the most common colour rules and considerations related to data visualisation and apply good colour sense to design accurate and impactful visualisations.Identify common natural and cultural colour associations and be sensitive to these associations when designing data visualisations.Define colour blindness, identify the most common types and apply colour blind friendly colour schemes to your data visualisations.Explain the hexadecimal (hex) colour code system.Define colour as perceived by the human visual perception system, and the RGB (red, green, blue) and HSV (hue, saturation and value) colour models.Rank the accuracy of different data visualisation features used to represent quantitative variables for comparative purposes.Identify common data visualisation features and the types of variables they are used to represent.Define and differentiate between change and inattentional blindness and explain their implications on data visualisation design.Define the Gestalt laws of proximity, similarity, connectedness, continuity, symmetry, closure, figure-ground and common fate and explain how they inform data visualisation design. ![]() Explain the concept of preattentive processing and identify common features that are preattentively processed.Outline the three stages of Ware’s visual information processing model and its implications for designing data visualisations.Discuss how visual illusions provide insight into visual perception and its limitations.The learning objectives for this chapter are as follows: 7.5.3 Case Study - The City of Melbourne’s Urban Forest.6.3 Multivariate Data Visualisation Strategies.5.18.5 One Quantitative and One Qualitative Variable.5.14.5 Coxcomb Diagram (Polar Area Diagram).5.14 Qualitative Univariate Visualisations. ![]() 3.13.11 Try to avoid colour scales that use red and green.3.13.10 Non-data elements should not compete with the data.3.13.9 Use colour scales to encode important information.3.13.8 Saturated colours can be used for small data points.3.13.7 Reserve bright colours to highlight important information.3.13.5 Define objects with equiluminous colour using thin borders.3.13.2 Use colour to differentiate important features.3.4.3 Change and Inattentional Blindness.3.3 Our Visual Information Processing System.2.5 Well Known Data Visualisation Storytelling Sites.2.3 Storytelling and Data Visualisation.1.9.4 Privacy and Sensitive Information.1.7 Publication Ready Data Visualisations.1.5.3 Focusing, justifying and choosing methods.1.5.2 Identifying a targeted audience and a data visualisation design objective. ![]()
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