Abstract
Faceted search is a common approach for helping users query multivariate data. While the method is found widely in contemporary tools, so far there has been little exploration of its potential to incorporate a spatial perspective. In this article we extend multivariate faceted search through the application of a brute force computational process to reveal facet combinations that have spatially-interesting results. We explore the potential utility of spatially-enhanced facet combinations in case study analyses of multivariate spatial data from learners in a massive open online course and multivariate spatial data from restaurant inspections. Spatially-enhanced facet combinations improve on ordinary faceted search by helping analysts understand which combinations have significant spatial footprints. We also show how this method can be integrated into a geovisual analytics system through a simple user interface. Finally, we draw on our case study analyses to highlight important challenges and opportunities for future research.
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