Exploration of interesting dense regions on spatial data

Nowadays, spatial data are ubiquitous in various fields of science, such as transportation and smart city management. A recent research direction in analyzing spatial data is to provide means for “exploratory analysis” of such data where users are guided towards interesting options in consecutive analysis iterations. Typically, the guidance component learns user’s preferences using his/her explicit feedback, e.g., picking a spatial point of interest (POI) or selecting a region of interest. However, it is often the case that users 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 user’s interaction) 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 users, in order to automatically find interesting spatial highlights. Our approach involves a polygon-based abstraction layer for capturing preferences. Using these IDRs, we highlight POIs to guide users in the analysis process. We discuss the efficiency and effectiveness of our approach through realistic examples and experiments on Airbnb and Yelp datasets.