Seasonal Variations in the Popularity of Coastal Spaces in China’s Temperate Monsoon Region Using Large Language Models

Coastal spaces are important settings for recreation, public health, tourism, and human–environment interaction. However, their spatial attractiveness often changes substantially across seasons, especially in regions with strong climatic variability. This study examines the seasonal drivers of coastal spatial popularity in China’s temperate monsoon region using 440,857 geotagged Weibo posts across 13 coastal cities, aggregated into 2733 5 km grid cells. We integrate perception-related cultural ecosystem service (CES) indicators extracted through large language models with built-environment, natural-environment, and socio-economic variables at the 5 km grid level. Fixed-effects models are employed to identify overall and seasonal associations between environmental conditions and spatial popularity. The primary full model explains a substantial proportion of the variation in coastal spatial popularity (R2 = 0.6330). The results show that coastal spatial popularity differs from conventional urban activity patterns. High building density and POI diversity are negatively associated with popularity, while accessibility, attractions, water-related environments, and economic conditions are positively associated with activity intensity. At the same time, substantial seasonal variation is observed. Visual perception remains important throughout the year, while the influence of other factors changes across seasons. For example, aquatic environmental conditions become particularly important in summer, whereas taste-related experiences become more relevant in autumn and recreational services in winter. These findings suggest that the drivers of coastal spatial popularity are not uniform over time but vary under different seasonal contexts. The study provides empirical evidence for seasonally adaptive coastal planning and demonstrates the potential of combining large language models with social media data to analyze perception-related spatial dynamics.