Knowledge Structure, Technical Pathways, and Spatial Decision-Making Challenges in the Digital-Intelligent Transformation of Territorial Spatial Governance: An Artificial Intelligence Perspective

Abstract

As artificial intelligence (AI) becomes increasingly integrated into spatial planning and governance, a central challenge is translating AI-enabled technical capabilities into institutionally usable spatial decision-making capacity. Taking territorial spatial governance (TSG) in China as a context-specific governance setting, this study combines bibliometric analysis and structured review to examine its knowledge evolution, technical pathways, and spatial decision-making constraints. The results show that: (1) research has progressively shifted from digital support and spatial monitoring toward intelligent analysis and governance-oriented applications; (2) four recurrent pathways of AI embedding are synthesized from the literature—sensing and identification, analytical simulation, decision support, and platform coordination; (3) the translation of these technical capabilities into spatial decision-making capacity is constrained by heterogeneous data foundations, limited model interpretability and transferability, institutional misalignment, and uneven organizational implementation; and (4) spatial decision-making capacity emerges through the interaction of technical pathways and constraint mechanisms under the multi-scale, differentiated, and rule-bound spatial logic of TSG. These findings provide an analytical basis for understanding how AI-enabled technical capabilities are translated from technical feasibility into governance usability under institutionally and spatially specific conditions.