Spatiotemporal characteristics of visitor landscape preferences in natural protected areas based on multi-source heterogeneous data
[Objectives]The spatiotemporal characteristics in visitors'landscape preferences reflected the authentic shifts in visitor demands during the process of socio-economic development,which also offered valuable references for optimizing the landscape quality of natural protected areas.[Methods]In this study,the Nanyue Scenic Area was taken as a study case,and the research employed a comprehensive approach integrating natural language processing,deep learning,and GIS technology.A deep fusion method was proposed,utilizing"semantic,visual,and behavioral"multi-source heterogeneous data derived from social media texts,geotagged photos,and mobile trajectory data.Eight distinct types of visitor landscape preferences were identified,and a quantitative analysis of these preferences was conducted across eight temporal and spatial dimensions.[Results]The study found that:(1)Visitors exhibited a preference for facilities,relics,and activities,with substantial potential for the development of biological landscapes.In high-altitude areas,visitors displayed a heightened fondness for landforms and weather landscapes.(2)Discrepancies existed between landscape preferences and actual resource supply,shedding light on the imbalance between planning initiatives and visitor demands.(3)Visitors'travel times correlated with traditional Lunar Calendar festivals,and there were significant differences across various periods within a single day.(4)Over the years,there has been a consistent increase in visitors'preference for natural landscapes.This trend was particularly pronounced during the COVID-19 pandemic,underscoring visitors'heightened desire to connect with nature.[Conclusions]These findings will serve as valuable references for landscape planning and the enhancement of natural protected areas,contributing to the sustainable development of scenic areas.
natural protected areasmulti-source datalandscape preferencesspatiotemporal analysisNanyue Scenic Area
万方数据
维普
