Diagnosing the innovation atmosphere of industrial parks through urban spatial perception: a multimodal large language model approach.

The innovation atmosphere of industrial parks, a crucial indicator of urban spatial vitality and regional economic dynamism, is difficult to assess using traditional, experience-driven methods. To overcome these limitations, this study proposes a novel, data-driven framework for urban spatial perception using Multimodal Large Language Models (MLLMs). Focusing on typical industrial parks in Wuhan, China, we harnessed MLLMs to interpret multi-source urban data, validating their diagnostic accuracy against expert evaluations. Subsequently, we simulated the diverse cognitive perspectives of four key stakeholder groups to diagnose the innovation atmosphere, diagnose the innovation atmosphere, quantifying the subjective spatial perceptions of different user groups and reflecting a nuanced understanding of human-environment interactions. The principal findings are: (1) The diagnostic assessments from the Gemini-2.5-pro model demonstrated a significant correlation (r = 0.890, p < 0.001) with the expert judgment baseline, affirming the high feasibility of this data-driven approach. (2) The MLLM framework effectively quantified perceptual heterogeneity among simulated stakeholders, offering deep insights into the varied dimensions of the parks' city image and perceived quality. (3) Spatial analysis revealed a consistent overall assessment of the innovation atmosphere across different perspectives, with parks in the southeastern and northwestern regions exhibiting higher spatial vitality. This research contributes an objective and automated tool for diagnosing the innovation atmosphere, a key facet of urban spatial perception. Crucially, the proposed framework provides robust empirical support for big data-driven strategies in urban planning, enabling the refined management of innovation spaces to be more productive, collaborative, and sustainable.