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• W4223602012 startingPage "127570" @default.
• W4223602012 abstract "Quantifying recreational visits to national forest parks (NFPs) and deciphering associating driving factors is significant for NFP protection, management, development, and planning; however, previous studies have primarily conducted questionnaire-based surveys or semi-structured individual interviews to gather limited data, while few have employed big data to effectively and efficiently estimate NFP visits and further explored correlated factors. This study used location-based service data from social media to quantify recreational NFP visits, and search for any associations with spatio-physical park factors selected based on previous constructive studies and NFP visitor perceptions of travel experiences posted on social media. A spatial error model with maximum likelihood estimation (SEM-MLE) was applied to investigate the significance. Sixty-eight (68) NFPs located in the Yangtze River Delta urban agglomeration (YRDUA) were selected as experimental cases. The results indicated that: (i) The number of NFP check-in visits was unevenly distributed across the YRDUA; (ii) NFP size and biodiversity (i.e., the number of vegetation and animal species) were not correlated with check-ins; (iii) Three NFP attributes (entrance fee, percent vegetation cover, and the presence of recreational water activities), one accessibility variable (potential visitors to NFP), and one transport variable (driving time to train station), had a significant influence on NFP check-ins, collectively explaining 49.69% of the observed variation. These findings can help NFP managers and planners understand the internal and external factors influencing recreational visits, thus more strategically informing nature-based tourism design and promotion campaigns." @default.
• W4223602012 created "2022-04-15" @default.
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• W4223602012 date "2022-06-01" @default.
• W4223602012 modified "2023-10-16" @default.
• W4223602012 title "What are the factors influencing recreational visits to national forest parks in China? Experiments using crowdsourced geospatial data" @default.
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• W4223602012 doi "https://doi.org/10.1016/j.ufug.2022.127570" @default.
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