26.4. 本章小结#
信息可视化与科学可视化相比，面临更加多样、非结构化的数据类型，需要设计更加新颖、灵活的视觉映射和隐喻，更加关注用户的交互探索和洞见获取。信息可视化是一个需要融合数据科学、可视化、人机交互、认知心理等多学科知识的综合研究领域。
从数据的角度看，信息可视化需要开发数据驱动的可视化技术，探索非结构化、跨媒体数据的有效表示、语义提取和融合分析方法，设计面向可视推理、知识发现的数据变换与映射模型。
从任务的角度看，信息可视化需要关注用户的可视分析需求，研究支持数据探索、假设推理、决策制定的交互机制和评估方法，实现”人-信息-环境”的无缝耦合。
从用户的角度看，信息可视化需要遵循以人为本的设计理念，深入理解用户的感知认知特点、审美情趣、个性化偏好，提供易学易用、美观高效的可视分析工具。
展望未来，大数据时代为信息可视化带来了海量的数据资源，但也带来了数据的异构性、噪声、动态性等诸多挑战。人工智能、沉浸式分析、可解释性等新兴技术，为信息可视化开辟了更广阔的研究空间。可视化研究者应积极拥抱变革，创新思路，探索数据驱动的智能可视分析新范式，用”可视化+X”的思路去拓展信息可视化的边界，提升信息可视化在数字经济时代的应用价值和社会影响。
26.4.1. 习题#
26.4.2. 参考文献#
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Thomas M. J. Fruchterman and Edward M. Reingold. Graph drawing by force-directed placement. Software: Practice and Experience, 21(11):1129–1164, 1991.
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