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==Visual perception and data visualization==

==Visual perception and data visualization==

A human can distinguish differences in line length, shape, orientation, distances, and color (hue) readily without significant processing effort; these are referred to as "[[Pre-attentive processing|pre-attentive attributes]]".  For example, it may require significant time and effort ("attentive processing") to identify the number of times the digit "5" appears in a series of numbers; but if that digit is different in size, orientation, or color, instances of the digit can be noted quickly through pre-attentive processing.<ref name="perceptualedge.com">{{cite web|url=http://www.perceptualedge.com/articles/ie/visual_perception.pdf|title=Steven Few-Tapping the Power of Visual Perception-September 2004|publisher=|access-date=2014-10-08|archive-url=https://web.archive.org/web/20141005080935/http://www.perceptualedge.com/articles/ie/visual_perception.pdf|archive-date=2014-10-05|url-status=live}}</ref>

A human can distinguish differences in line length, shape, orientation, distances, and color (hue) readily without significant processing effort; these are referred to as "[[Pre-attentive processing|pre-attentive attributes]]".  For example, it may require significant time and effort ("attentive processing") to identify the number of times the digit "5" appears in a series of numbers; but if that digit is different in size, orientation, or color, instances of the digit can be noted quickly through pre-attentive processing.<ref name="perceptualedge.com">{{cite web|url=http://www.perceptualedge.com/articles/ie/visual_perception.pdf|title=Steven Few-Tapping the Power of Visual Perception-September 2004|publisher=|access-date=2014-10-08|archive-url=https://web.archive.org/web/20141005080935/http://www.perceptualedge.com/articles/ie/visual_perception.pdf|archive-date=2014-10-05|url-status=live}}</ref>

A human can distinguish differences in line length, shape, orientation, distances, and color (hue) readily without significant processing effort; these are referred to as "pre-attentive attributes".  For example, it may require significant time and effort ("attentive processing") to identify the number of times the digit "5" appears in a series of numbers; but if that digit is different in size, orientation, or color, instances of the digit can be noted quickly through pre-attentive processing.

A human can distinguish differences in line length, shape, orientation, distances, and color (hue) readily without significant processing effort; these are referred to as "pre-attentive attributes".  For example, it may require significant time and effort ("attentive processing") to identify the number of times the digit "5" appears in a series of numbers; but if that digit is different in size, orientation, or color, instances of the digit can be noted quickly through pre-attentive processing.

人类可以很容易地区分线条长度、形状、方向、距离和颜色(色调)的差异，而不需要大量的处理工作; 这些被称为“预注意属性”。例如，识别数字“5”在一系列数字中出现的次数可能需要花费大量的时间和精力(“专注加工”) ; 但如果该数字在大小、方向或颜色上不同，则可以通过预先专注加工快速识别该数字的实例。

人类可以毫不费力地区分线条长度、形状、方向、距离和颜色(色调)的差异，而不需要大量的处理工作; 这些被称为“预注意属性”。例如，识别数字“5”在一系列数字中出现的次数可能需要花费大量的时间和精力(“专注加工”) ; 但如果该数字在大小、方向或颜色上与其它数字不同，则可以通过预注意处理而快速识别该数字。

 

Effective graphics take advantage of pre-attentive processing and attributes and the relative strength of these attributes. For example, since humans can more easily process differences in line length than surface area, it may be more effective to use a bar chart (which takes advantage of line length to show comparison) rather than pie charts (which use surface area to show comparison).

Effective graphics take advantage of pre-attentive processing and attributes and the relative strength of these attributes. For example, since humans can more easily process differences in line length than surface area, it may be more effective to use a bar chart (which takes advantage of line length to show comparison) rather than pie charts (which use surface area to show comparison).

有效的图形利用了预先注意的处理和属性以及这些属性的相对强度。例如，由于人类更容易处理线长度的差异而不是表面积的差异，因此使用柱状图(利用线长度来显示比较)而不是饼状图(利用表面积来显示比较)可能更有效。

有效的图形利用了预注意属性以及这些属性的相对强度。例如，由于人类处理线长差异比处理表面积更容易，因此使用柱状图(利用线长来显示比较)可能比饼状图(利用表面积来显示比较)更有效。

 

=== Human perception/cognition and data visualization ===

=== Human perception/cognition and data visualization ===

 

Almost all data visualizations are created for human consumption. Knowledge of human perception and cognition is necessary when designing intuitive visualizations.<ref name=":0">{{Cite web|title = Data Visualization for Human Perception|url = https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/data-visualization-for-human-perception|website = The Interaction Design Foundation|accessdate = 2015-11-23|archive-url = https://web.archive.org/web/20151123151958/https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/data-visualization-for-human-perception|archive-date = 2015-11-23|url-status = live}}</ref> Cognition refers to processes in human beings like perception, attention, learning, memory, thought, concept formation, reading, and problem solving.<ref>{{Cite web|url = https://www.sfu.ca/gis/geog_x55/web355/icons/11_lec_vweb.pdf|title = Visualization|date = |accessdate = 2015-11-22|website = SFU|publisher = SFU lecture|last = |first = |archive-url = https://web.archive.org/web/20160122203157/http://www.sfu.ca/gis/geog_x55/web355/icons/11_lec_vweb.pdf|archive-date = 2016-01-22|url-status = dead}}</ref> Human visual processing is efficient in detecting changes and making comparisons between quantities, sizes, shapes and variations in lightness. When properties of symbolic data are mapped to visual properties, humans can browse through large amounts of data efficiently. It is estimated that 2/3 of the brain's neurons can be involved in visual processing. Proper visualization provides a different approach to show potential connections, relationships, etc. which are not as obvious in non-visualized quantitative data. Visualization can become a means of [[data exploration]].

Almost all data visualizations are created for human consumption. Knowledge of human perception and cognition is necessary when designing intuitive visualizations.<ref name=":0">{{Cite web|title = Data Visualization for Human Perception|url = https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/data-visualization-for-human-perception|website = The Interaction Design Foundation|accessdate = 2015-11-23|archive-url = https://web.archive.org/web/20151123151958/https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/data-visualization-for-human-perception|archive-date = 2015-11-23|url-status = live}}</ref> Cognition refers to processes in human beings like perception, attention, learning, memory, thought, concept formation, reading, and problem solving.<ref>{{Cite web|url = https://www.sfu.ca/gis/geog_x55/web355/icons/11_lec_vweb.pdf|title = Visualization|date = |accessdate = 2015-11-22|website = SFU|publisher = SFU lecture|last = |first = |archive-url = https://web.archive.org/web/20160122203157/http://www.sfu.ca/gis/geog_x55/web355/icons/11_lec_vweb.pdf|archive-date = 2016-01-22|url-status = dead}}</ref> Human visual processing is efficient in detecting changes and making comparisons between quantities, sizes, shapes and variations in lightness. When properties of symbolic data are mapped to visual properties, humans can browse through large amounts of data efficiently. It is estimated that 2/3 of the brain's neurons can be involved in visual processing. Proper visualization provides a different approach to show potential connections, relationships, etc. which are not as obvious in non-visualized quantitative data. Visualization can become a means of [[data exploration]].

Almost all data visualizations are created for human consumption. Knowledge of human perception and cognition is necessary when designing intuitive visualizations. Cognition refers to processes in human beings like perception, attention, learning, memory, thought, concept formation, reading, and problem solving. Human visual processing is efficient in detecting changes and making comparisons between quantities, sizes, shapes and variations in lightness. When properties of symbolic data are mapped to visual properties, humans can browse through large amounts of data efficiently. It is estimated that 2/3 of the brain's neurons can be involved in visual processing. Proper visualization provides a different approach to show potential connections, relationships, etc. which are not as obvious in non-visualized quantitative data. Visualization can become a means of data exploration.

Almost all data visualizations are created for human consumption. Knowledge of human perception and cognition is necessary when designing intuitive visualizations. Cognition refers to processes in human beings like perception, attention, learning, memory, thought, concept formation, reading, and problem solving. Human visual processing is efficient in detecting changes and making comparisons between quantities, sizes, shapes and variations in lightness. When properties of symbolic data are mapped to visual properties, humans can browse through large amounts of data efficiently. It is estimated that 2/3 of the brain's neurons can be involved in visual processing. Proper visualization provides a different approach to show potential connections, relationships, etc. which are not as obvious in non-visualized quantitative data. Visualization can become a means of data exploration.

几乎所有的数据可视化都是为人类消费而创建的。设计直觉可视化时，必须具备人类感知和认知的知识。认知是指人类的感知、注意、学习、记忆、思维、概念形成、阅读和解决问题等过程。人类的视觉处理在检测变化和比较数量、大小、形状和亮度变化方面是有效的。当符号数据的属性映射到可视属性时，人类可以高效地浏览大量数据。据估计，2 / 3的大脑神经元可以参与视觉处理。正确的可视化提供了一种不同的方法来显示潜在的联系、关系等。这在非可视化定量数据中并不明显。可视化可以成为数据探索的一种手段。

几乎所有的数据可视化都是为人类消费而创建的。设计直觉可视化时，必须具备人类感知和认知的知识。认知是指人类的感知、注意、学习、记忆、思维、概念形成、阅读和解决问题等过程。人类的视觉处理在检测变化和比较数量、大小、形状和亮度变化方面是有效的。当符号数据的属性映射到可视属性时，人类可以高效地浏览大量数据。据估计，2/3的大脑神经元可以参与视觉处理。恰当的可视化提供了一种不同的方法来显示潜在的联系、关系等，这些在非可视化的定量数据中是不那么明显的。可视化可以成为数据探索的一种手段。

 

 

== History of data visualization ==

== History of data visualization ==