Nowadays, spatial data are ubiquitous in various fields of science, such as transportation and the social Web. A recent research direction in analyzing spatial data is to provide means for "exploratory analysis" of such data where analysts are guided towards interesting options in consecutive analysis iterations. Typically, the guidance component learns analyst's preferences using her explicit feedback, e.g., picking a spatial point or selecting a region of interest. However, it is often the case that analysts forget or don't feel necessary to explicitly express their feedback in what they find interesting. Our approach captures implicit feedback on spatial data. The approach consists of observing mouse moves (as a means of analyst's interaction) and also the explicit analyst's interaction with data points in order to discover interesting spatial regions with dense mouse hovers. In this paper, we define, formalize and explore Interesting Dense Regions (IDRs) which capture preferences of analysts, in order to automatically find interesting spatial highlights. Our approach involves a polygon-based abstraction layer for capturing preferences. Using these IDRs, we highlight points to guide analysts in the analysis process. We discuss the efficiency and effectiveness of our approach through realistic examples and experiments on Airbnb and Yelp datasets.
翻译:目前,空间数据在交通和社交网络等不同科学领域普遍存在。分析空间数据的最新研究方向是提供“探索性分析”手段,使分析师在连续的分析迭代中向有趣的选项提供指导。通常,指导部分利用分析员的明确反馈学习分析员的偏好,例如,选择空间点或选择感兴趣的区域。然而,分析师往往忘记或不认为有必要在其发现感兴趣的方面明确表达他们的反馈。我们的方法收集了空间数据的隐含反馈。这种方法包括观察鼠标移动(作为分析师互动的一种手段)以及明确的分析师与数据点的互动,以便发现鼠标悬浮的有趣的空间区域。在本文中,我们定义、正式化和探索吸引分析员偏好之处的有趣的登斯区域(IDRs),以便自动找到有趣的空间亮点。我们的方法涉及一个基于多边的抽象层以获取偏好之处。我们利用这些 IDRs,我们强调指导分析过程的分析员的要点。我们通过现实的范例和Airb实验来讨论我们的方法的效率和有效性。