生成式AI时代的地理可视化重构：领域专家的见解
Reconfiguring Geovisualization in the Age of Generative AI: Insights from Domain Experts
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中文总结 AI 辅助
本研究通过访谈20位地理可视化专家，探究生成式AI对地理可视化实践的影响，发现其拓展了相关能力但转移了瓶颈，提出需领域特定方法实现负责任应用，为相关实践、教育等提供启示。
中文摘要 AI 辅助
生成式AI正日益融入地理可视化领域，但其对专业实践的更广泛影响尚未得到充分理解。为探究这些影响，我们对20位地理可视化专家开展了半结构化访谈，访谈围绕数据、构思、原型制作与迭代这四大分析领域展开，同时鼓励参与者反思超出这些活动范畴的问题。研究结果显示，生成式AI拓展了地理可视化的能力，尤其在数据处理、创意探索及快速原型制作方面表现突出，但并未简单消除现有限制；相反，关键瓶颈正从生产环节转向判断与验证环节。随着常规技术任务愈发自动化，专业价值日益依赖空间推理、情境解读、审美与伦理判断，以及评估AI生成成果是否适合使用的能力。与此同时，生成式AI带来了关于来源可追溯性、可解释性与问责制的新挑战，引发了责任应如何在模型、开发者、从业者、机构及用户间分配的疑问。这些转变在地理可视化领域尤为显著，因为空间表征受地理现实约束，必须平衡科学有效性、视觉表达与技术实现。因此，我们认为地理可视化领域负责任的生成式AI应用需要针对空间验证、来源可追溯性、不确定性沟通、人类监督及负责任使用的领域特定方法。本研究提供了基于专家视角的见解，阐明生成式AI如何将地理可视化重构为空间知识生产的实践，同时明确了其对未来专业实践、教育、系统设计与治理的启示。
英文摘要
GenAI is increasingly integrated into geovisualization, yet its broader implications for professional practice are insufficiently understood. To examine these implications, we conducted semi-structured interviews with 20 geovisualization experts. The interviews were structured around four broad analytical domains: Data, Ideation, Prototyping, and Iteration, while also encouraging participants to reflect on issues that extend beyond these activities. Our findings show that GenAI expands the capabilities of geovisualization, particularly in terms of data handling, creative exploration, and rapid prototyping, but does not simply remove existing constraints. Instead, key bottlenecks are shifting from production to judgment and verification. As routine technical tasks become more automated, professional value increasingly depends on spatial reasoning, contextual interpretation, aesthetic and ethical judgment, and the ability to assess whether AI-generated outputs are appropriate for use. At the same time, GenAI introduces new challenges regarding provenance, interpretability, and accountability, raising questions about how responsibility should be distributed across models, developers, practitioners, institutions, and users. These shifts are particularly significant in geovisualization because spatial representations are constrained by geographic reality and must balance scientific validity, visual expression, and technical implementation. We therefore argue that responsible GenAI in geovisualization requires domain-specific approaches to spatial validation, provenance, uncertainty communication, human oversight, and accountable use. This study provides an expert-grounded perspective on how GenAI is reconfiguring geovisualization as a practice of spatial knowledge production. It also identifies implications for future professional practice, education, system design, and governance.
