A holistic semantic similarity measure for viewports in interactive maps

In recent years, geographic information has entered the mainstream, deeply altering the pre-existing patterns of its production, distribution, and consumption. Through web mapping, millions of online users utilise spatial data in interactive digital maps. The typical unit of visualisation of geo-data is a viewport, defined as a bi-dimensional image of a map, fixed at a given scale, in a rectangular frame. In a viewport, the user performs analytical tasks, observing individual map features, or drawing high-level judgements about the objects in the viewport as a whole. Current geographic information retrieval (GIR) systems aim at facilitating analytical tasks, and little emphasis is put on the retrieval and indexing of visualised units, i.e. viewports. In this paper we outline a holistic, viewport-based GIR system, offering an alternative approach to feature-based GIR. Such a system indexes viewports, rather than individual map features, extracting descriptors of their high-level, overall semantics in a vector space model. This approach allows for efficient comparison, classification, clustering, and indexing of viewports. A case study describes in detail how our GIR system models viewports representing geographical locations in Ireland. The results indicate advantages and limitations of the viewport-based approach, which allows for a novel exploration of geographic data, using holistic semantics.