A clustering-based visualization of colocation patterns

Extraction of interesting colocations in geo-referenced data is one of the major tasks in spatial pattern mining. The goal is to find sets of spatial object-types with instances located in the same neighborhood. In this context, the main drawback is the visualization and interpretation of extracted patterns by domain experts. Indeed, common textual representation of colocations loses important spatial information such as the position, the orientation or the spatial distribution of the patterns. To overcome this problem, we propose a new clustering-based visualization technique deeply integrated in the colocation mining algorithm. This new simple, concise and intuitive cartographic visualization considers both spatial information and expert practices. This proposition has been integrated in a Geographic Information System and experimented on a real-world geological data set. Domain experts confirm the added-value of this visualization approach.