Reconfiguring Geovisualization in the Age of Generative AI: Insights from Domain Experts

2026-08-12

AI Summary

Generative artificial intelligence (GenAI) is profoundly reshaping geovisualization practices, yet its deeper implications for accountability, validation mechanisms, and the transformation of professional expertise remain unclear. Through semi-structured interviews with 20 domain experts, this study systematically examines GenAI’s applications and limitations across data processing, ideation, prototyping, and iterative design. It reveals a pivotal shift in core bottlenecks—from technical generation to human judgment and verification—and identifies spatial reasoning, contextual interpretation, and ethical discernment as emerging professional competencies. Building on these insights, the paper proposes a domain-specific governance framework for geovisualization that emphasizes traceability, communication of uncertainty, and robust accountability structures to uphold spatial fidelity, scientific validity, and ethical responsibility.

Abstract

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.

Problem

• Geovisualization
• Generative AI
• Spatial reasoning
• Accountability
• Provenance

Innovation

• Generative AI
• Geovisualization
• Spatial reasoning
• Provenance
• Accountable AI