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原文摘要

We introduce HexTiles, a hexagonal-tiling based visual encoding design for multivariate geospatial data. Visualizing several geospatial variables at once is difficult because map position is already reserved for geography, and because variables are often reported on different, irregular spatial units whose aggregation is subject to the Modifiable Areal Unit Problem (MAUP). HexTiles bins data of differing spatial discretizations into hierarchical hexagons and encodes up to three variables per tile in a base color, an inner ring, and semantic icons whose count conveys value, letting the visualization designer guide attention to specific variables and their interactions. To make the effects of MAUP visible, we compute a spatially-weighted variance for each variable within each tile and encode it as a confidence value in every channel, so that the variability hidden by aggregation is not lost. Because interactive maps are increasingly viewed in tilted, 3D perspective, HexTiles encodes quantitative values only in channels that perspective does not distort, namely color and icon count. In a user study comparing HexTiles to Square-glyphs, participants rated HexTiles as significantly less demanding and frustrating, and performed more consistently with it. Domain experts in ecology and hydrology further noted its value for communicating regional patterns, and that the confidence encoding signals where aggregated values should be interpreted with care. Our results suggest that, although 3D is commonly discouraged, it can work for multivariate geospatial visualization when restricted to perspective-robust channels.