  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.
