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این مقاله علمی پژوهشی (ISI) به زبان انگلیسی از نشریه الزویر مربوط به سال ۲۰۲۲ دارای ۱۲ صفحه انگلیسی با فرمت PDF می باشد در ادامه این صفحه لینک دانلود رایگان مقاله انگلیسی و بخشی از ترجمه فارسی مقاله موجود می باشد.
کد محصول: M1231
سال نشر: ۲۰۲۲
نام ناشر (پایگاه داده): الزویر
نام مجله: Tourism Management
نوع مقاله: علمی پژوهشی (Research articles)
تعداد صفحه انگلیسی: ۱۲ صفحه PDF
عنوان کامل فارسی:
مقاله انگلیسی ۲۰۲۲ : استفاده از داده‌های مسیر گردشگری برای برنامه‌ریزی و مدیریت کارآمد مقصد: یک رویکرد اکتشافی جدید
عنوان کامل انگلیسی:
Leveraging tourist trajectory data for effective destination planning and management: A new heuristic approach
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Abstract
Understanding tourist movements provides insights for destination planning, service design and marketing. The key challenge is to develop a tool that can capture the value in the tourist mobility data. This study presents a new heuristic approach that combines adaptive spatial clustering with frequent pattern mining to improve the performance and efficiency of trajectory data analytics. The aim is to fully leverage the semantic information in the tourist data and the duration that tourists stay at an attraction. Anonymous mobile positioning data from 741 tourists to one of China’s leading destinations are used to illustrate the application of the new analytical approach. The results reveal a four-level destination spatial structure ranging from core to peripheral areas. The findings provide practical implications for facilitating intra-destination cooperation and optimizing destination resource allocation and service design.
Keywords: Tourist mobility, Frequent pattern mining, Time-space, Core-peripheral, Data mining, Intra-destination cooperation
۱.Introduction
Tourist mobility in a destination is limited to two essential resources, i.e., space and time (Grinberger & Shoval, 2019). When organizing trips, tourists must allocate time for visiting attractions and the transport between different locations (Neutens et al., 2011). An increase in time spent on one activity (e.g., transport) means less time for another activity (e.g., enjoying a rare exhibition in the museum). The spatio-temporal distribution of tourists reflects the composition of a network of attractions within a destination. The core-periphery model of attractions provides a systematic understanding of the geographical distribution of attractions in the destination (Prideaux, 2002). A primary attraction can attract more tourists than smaller ones and a cluster of attractions can attract more tourists who stay longer than isolated attractions (Lue et al., 1993). The spatial structure of a destination is constantly shaped and reshaped by tourist movement. Mapping tourist movements provides an understanding of the structure of the destination and the interconnections among destinations (Park et al., 2020)…
۶.Conclusions
In today’s digital era, mass spatio-temporal trajectory data can be recorded accurately and retrieved conveniently, which presents great opportunities for tourism research (Shoval et al., 2020; Shoval et al., 2018). This study attempts to advance the ways of leveraging such data to generate insights into tourist mobility. A two-stage heuristic approach involving adaptive DBSCAN, BrowseRank algorithm, convex hull algorithm, and DFS strategy is proposed to mine the frequent pattern of tourist trajectories. We illustrate the application of our proposed approach using mobile positioning data captured from real tourists and demonstrate the practical applications of the study findings…
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