Point sets in 2D with multiple classes are a common type of data. A canonical visualization design for them are scatterplots, which do not scale to large collections of points. For these larger data sets, binned aggregation (or binning) is often used to summarize the data, with many possible design alternatives for creating effective visual representations of these summaries. There are a wide range of designs to show summaries of 2D multi-class point data, each capable of supporting different analysis tasks. In this paper, we explore the space of visual designs for such data, and provide design guidelines for different analysis scenarios. To support these guidelines, we compile a set of abstract tasks and ground them in concrete examples using multiple sample datasets. We then assess designs, and survey a range of design decisions, considering their appropriateness to the tasks. In addition, we provide a web-based implementation to experiment with design choices, supporting the validation of designs based on task needs.

中文翻译：

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多类散点图分箱聚合的视觉设计

具有多个类的二维点集是一种常见的数据类型。它们的规范可视化设计是散点图，它不会缩放到大量点。对于这些较大的数据集，分箱聚合（或分箱）通常用于汇总数据，并提供许多可能的设计替代方案来创建这些汇总的有效视觉表示。有多种设计可以显示二维多类点数据的摘要，每种设计都能够支持不同的分析任务。在本文中，我们探索了此类数据的可视化设计空间，并为不同的分析场景提供了设计指南。为了支持这些准则，我们编译了一组抽象任务，并使用多个样本数据集将它们放在具体示例中。然后我们评估设计，并调查一系列设计决策，考虑他们对任务的适当性。此外，我们提供基于 Web 的实现来试验设计选择，支持基于任务需求的设计验证。