  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 provide a confidence
value for each tile, which can be used to interpret the variability of the data
within the corresponding geospatial area, an information that can be lost in
geospatial visualizations. To validate our approach, we gather quantitative and
qualitative feedback from a user study and document domain expert feedback from
ecologists and hydrologists experienced in designing geospatial visualizations.
