HexTiles and Semantic Icons for MAUP-Aware Multivariate Geospatial Visualizations
Yuya Kawakami, Sarah Yuniar, Kwan-Liu Ma

TL;DR
This paper introduces HexTiles, a hexagonal tiling visualization method for multivariate geospatial data that incorporates semantic icons and confidence encoding to improve interpretability and address MAUP effects.
Contribution
HexTiles is a novel multivariate geospatial visualization design that uses semantic icons and confidence encoding to enhance interpretability and mitigate MAUP effects.
Findings
Positive user study feedback on HexTiles effectiveness
Domain experts found HexTiles improved data interpretation
HexTiles effectively visualizes data variability within geospatial areas
Abstract
We introduce HexTiles, a domain-agnostic hexagonal-tiling based visual encoding design for multivariate geospatial data. Multivariate geospatial data have presented a challenge due to the graph schema associated with geospatial maps, on which most geospatial data is presented. With HexTiles, we design a multivariate geospatial visualization design that leverages semantic icons to (1) simplify the process of interpreting interactions between multivariate geospatial data, and (2) put the visualization designer in the driver's seat to guide user attention to specific variables and interactions. Additionally with HexTiles, we attempt to explicitly mitigate effects of the Modifiable Areal Unit Problem (MAUP) for interpreting geospatial data, by proposing a confidence encoding for each of the information channels in HexTiles. We calculate weighted variances of the variables in each HexTile to…
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