Historical and cultural scenic areas are essential carriers of regional identity and play a critical role in urban renewal and cultural tourism development. However, current evaluations often emphasize cultural and economic value while overlooking the perceptual gap between tourists and conservation planning. Text mining indicates a disconnect between tourists’ perceived value of cultural resources and existing protection frameworks, leading to underutilization and spatial mismatch. This study examines the waterfront historical and cultural scenic area in central Jingzhou. Tourist perceptions were extracted through online text mining, word frequency, and sentiment analyses, and compared with resource values defined in the Jingzhou Historical and Cultural City Protection Plan. By integrating POI and road network data, kernel density estimation and spatial syntax analysis were used to reveal distribution patterns and accessibility mechanisms underlying these mismatches. The findings classify waterfront heritage spaces into four types—high perception/high accessibility, low perception/low accessibility, high perception/low accessibility, and low perception/high accessibility—and identify mismatched areas requiring optimization. Targeted strategies are proposed to address these gaps. The study provides a new methodological approach that combines network text big data with spatial syntax, offering insights for improving the conservation, reuse, and planning of waterfront heritage landscapes.
Tan et al. (Sun,) studied this question.