A multiscale and multiperspective quantifying framework for spatial patterns and influencing mechanisms of geographical indications

Geographical indications (GIs) embody distinctive regional elements characterized by widespread spatial distribution, serving as vital agricultural intellectual properties and significant tourism assets. This case study introduces a multiscale and multi-perspective quantification framework for Shandong Province, focusing on spatial agglomeration, association, and heterogeneity. This approach quantitatively explores GIs’ spatial patterns and influencing mechanisms, analyzing both at the monomer and regional scales, considering static and dynamic perspectives. Leveraging methods such as kernel density analysis, Moran’s I index, Geary’s C index, Internet hot word analysis, and Geodetector, the coastal municipalities, prosperous urban areas, and zones with high-quality arable soils were identified as spatial agglomeration areas for GIs. The study revealed a strong correlation between the spatial distribution pattern of socioeconomic factors and GIs’ spatial density from a static perspective. However, diminished spatial autocorrelation was observed for GIs’ public perception from a dynamic standpoint, indicating a reduced influence of Internet connectivity. The spatial heterogeneity in GIs is determined by multiple interconnected factors, displaying an enhancement effect between any two influencing factors. Among the 15 influencing factors analyzed, urbanization rate; gross value of agricultural, forestry, animal husbandry, and fishery production; and per capita gross domestic product significantly contribute to spatial heterogeneity effects at both urbanization rate, gross value of agriculture, forestry, animal husbandry, fishery production, and per capita gross domestic product significantly contributed to spatial heterogeneity effects at regional and monomer scales. The results suggest that GIs are likely to flourish with regional economic growth. The analytical framework and quantification methods support exploring and sustainably utilizing GIs, improving output quality, and modernizing GIs’ production, ultimately contributing to regional agriculture’s high-quality and sustainable development.