Interestingness Hotspot Discovery in Spatial Datasets Using a Graph-Based Approach

Abstract

This paper proposes a novel methodology for discovering interestingness hotspots in spatial datasets using a graph-based algorithm. We define interestingness hotspots as contiguous regions in space which are interesting based on a domain expert’s notion of interestingness captured by an interestingness function. In our recent work, we proposed a computational framework which discovers interestingness hotspots in gridded datasets using a 3-step approach which consists of seeding, hotspot growing and post-processing steps. In this work, we extend our framework to discover hotspots in any given spatial dataset. We propose a methodology which firstly creates a neighborhood graph for the given dataset and then identifies seed regions in the graph using the interestingness measure. Next, we grow interestingness hotspots from seed regions by adding neighboring nodes, maximizing the given interestingness function. Finally after all interestingness hotspots are identified, we create a polygon model for each hotspot using an approach that uses Voronoi tessellations and the convex hull of the objects belonging to the hotspot. The proposed methodology is evaluated in a case study for a 2-dimensional earthquake dataset in which we find interestingness hotspots based on variance and correlation interestingness functions.