The power of data visualization is not to convey absolute values of individual data points, but to allow the exploration of relations (increases or decreases in a data value) among them. One approach to highlighting these relations is to explicitly encode the numeric differences (deltas) between data values. Because this approach removes the context of the individual data values, it is important to measure how much of a performance improvement it actually offers, especially across differences in encodings and tasks, to ensure that it is worth adding to a visualization design. Across 3 different tasks, we measured the increase in visual processing efficiency for judging the relations between pairs of data values, from when only the values were shown, to when the deltas between the values were explicitly encoded, across position and length visual feature encodings (and slope encodings in Experiments 1 & 2). In Experiment 1, the participant's task was to locate a pair of data values with a given relation (e.g., Find the 'small bar to the left of a tall bar' pair) among pairs of the opposite relation, and we measured processing efficiency from the increase in response times as the number of pairs increased. In Experiment 2, the task was to judge which of two relation types was more prevalent in a briefly presented display of 10 data pairs (e.g., Are there more 'small bar to the left of a tall bar' pairs or more 'tall bar to the left of a small bar' pairs?). In the final experiment, the task was to estimate the average delta within a briefly presented display of 6 data pairs (e.g., What is the average bar height difference across all 'small bar to the left of a tall bar' pairs?). Across all three experiments, visual processing of relations between data value pairs was significantly better when directly encoded as deltas rather than implicitly between individual data points, and varied substantially depending on the task (improvement ranged from 25% to 95%). Considering the ubiquity of bar charts and dot plots, relation perception for individual data values is highly inefficient, and confirms the need for alternative designs that provide not only absolute values, but also direct encoding of critical relationships between those values.

中文翻译：

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数据增量直接编码对数据对关系感知的好处的度量。

数据可视化的功能不是传达单个数据点的绝对值，而是允许探索它们之间的关系（数据值的增加或减少）。强调这些关系的一种方法是显式编码数据值之间的数字差异（增量）。由于此方法删除了各个数据值的上下文，因此，重要的是衡量其实际提供的性能改进（尤其是跨越编码和任务的不同），以确保值得将其添加到可视化设计中。在3个不同的任务中，我们测量了视觉处理效率的提高，以便判断数据值对之间的关​​系，从仅显示值到明确编码值之间的差值，跨位置和长度的视觉特征编码（以及实验1和2中的坡度编码）。在实验1中，参与者的任务是在成对的相反关系中找到一对具有给定关系的数据值（例如，找到“高条左边的小条”），并从响应时间随线对数量的增加而增加。在实验2中，任务是判断在简短呈现的10个数据对的显示中，两种关系类型中的哪一种更为普遍（例如，在高条形图对的左边有更多的“小条形图”，还是在高条形图对中有更多的“高条形图”）？小酒吧的左边？”。在最后的实验中，任务是估算简短显示的6个数据对显示中的平均增量（例如，所有“高条左侧的小条”对之间的平均条高差是多少？）。在所有三个实验中，直接将值编码为增量而不是隐式地在各个数据点之间进行编码时，数据值对之间关系的可视化处理要好得多，并且视任务的不同而有所不同（改进范围为25％至95％）。考虑到条形图和点图的普遍性，对单个数据值的关系感知效率非常低，并且确认了对不仅提供绝对值而且还直接编码这些值之间的关键关系的替代设计的需求。当直接将值编码为增量而不是隐式地在各个数据点之间进行编码时，数据值对之间关系的可视化处理要好得多，并且视任务的不同而有所不同（改进范围为25％至95％）。考虑到条形图和点图的普遍性，对单个数据值的关系感知效率非常低，并确认了对不仅提供绝对值而且还直接编码这些值之间的关键关系的替代设计的需求。当直接将值编码为增量而不是隐式地在各个数据点之间进行编码时，数据值对之间关系的可视化处理要好得多，并且视任务的不同而有所不同（改进范围为25％至95％）。考虑到条形图和点图的普遍性，对单个数据值的关系感知效率非常低，并且确认了对不仅提供绝对值而且还直接编码这些值之间的关键关系的替代设计的需求。