TY - JOUR
T1 - A brute force method for spatially-enhanced multivariate facet analysis
AU - Robinson, Anthony C.
AU - Quinn, Sterling D.
N1 - Funding Information:
This research was supported by aPenn State Center for Online Innovation in Learning (COIL) Research Initiation Grant.
Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2018/5
Y1 - 2018/5
N2 - 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.
AB - 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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U2 - 10.1016/j.compenvurbsys.2017.12.003
DO - 10.1016/j.compenvurbsys.2017.12.003
M3 - Article
AN - SCOPUS:85040017193
SN - 0198-9715
VL - 69
SP - 28
EP - 38
JO - Computers, Environment and Urban Systems
JF - Computers, Environment and Urban Systems
ER -