发表机构
University of Konstanz（康斯坦茨大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对球形地理空间数据颜色编码研究不足的问题，提出SpheriColor色图生成方法，经评估其性能优于2D色图和HSLuv双锥编码，两种优化策略各有优势。
AI 中文摘要
多变量地理空间数据可视化常依赖多个协调视图，其中颜色可用于关联视图或编码数据属性。通过颜色编码空间位置可揭示非空间可视化中的模式，但多数色图应用聚焦于高维属性编码，针对球形地理空间数据的颜色编码研究却少得多，尽管所有位置都位于球体上。为解决该问题，我们提出SpheriColor，一种以距离保留和色彩空间利用为设计目标的地理空间参考映射色图生成方法。我们将地理空间距离函数投影到感知线性色彩空间，随后采用两种不同的色域约束优化策略。我们使用真实世界和合成数据集开展定量评估，结果显示其性能优于2D色图和HSLuv双锥编码。基于单纯形的优化在距离保留方面表现出色，而基于射线的方法则提供更好的色彩空间利用。
英文摘要
Multivariate geospatial data visualization often relies on multiple coordinated views, where color can be used to either link views or encode data attributes. Encoding spatial locations through color can reveal patterns in non-spatial visualizations, yet most applications of colormaps focus on high-dimensional attribute encodings instead. While 2D colormaps have been studied extensively, color encodings designed for spherical geospatial data have received much less attention, even though all locations lie on a sphere. To address this, we propose SpheriColor, a colormap generation approach for mapping geospatial references guided by the design goals of distance preservation and colorspace exploitation. We project a geospatial distance function into a perceptually linear colorspace, followed by two different gamut-constrained optimization strategies. We perform a quantitative evaluation using both real-world and synthetic datasets, demonstrating superior performance over 2D colormaps and HSLuv double cone encodings. The simplex-based optimization excels at distance preservation, whereas the ray-based approach provides better colorspace exploitation.
Comments4 Pages, 3 Figures, to be published in IEEE Visualization Conference (VIS)
