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The purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point of interest (POI) visitation behaviour and compare the most prominent clustering approaches to identify POIs in various application scenarios.

The study, first, extracts photo metadata from Flickr, such as upload time, location and user. Then, photo uploads are assigned to latent POIs by density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering algorithms. Finally, association rule analysis (FP-growth algorithm) and sequential pattern mining (generalised sequential pattern algorithm) are used to identify tourists’ behavioural patterns.

The approach has been demonstrated for the city of Munich, extracting 13,545 photos for the year 2015. POIs, identified by DBSCAN and k-means clustering, could be meaningfully assigned to well-known POIs. By doing so, both techniques show specific advantages for different usage scenarios. Association rule analysis revealed strong rules (support: 1.0-4.6 per cent; lift: 1.4-32.1 per cent), and sequential pattern mining identified relevant frequent visitation sequences (support: 0.6-1.7 per cent).

As a theoretic contribution, this study comparatively analyses the suitability of different clustering techniques to appropriately identify POIs based on photo upload data as an input to association rule analysis and sequential pattern mining as an alternative but also complementary techniques to analyse tourists’ spatial behaviour.

From a practical perspective, the study highlights that big data sources, such as Flickr, show the potential to effectively substitute traditional data sources for analysing tourists’ spatial behaviour and movement patterns within a destination. Especially, the approach offers the advantage of being fully automatic and executable in a real-time environment.

The study presents an approach to identify POIs by clustering photo uploads on social media platforms and to analyse tourists’ spatial behaviour by association rule analysis and sequential pattern mining. The study gains novel insights into the suitability of different clustering techniques to identify POIs in different application scenarios.

本论文旨在分析图片分享平台Flickr对截取游客空间动线信息和景点（POI）游览行为的适用性, 并且对比最知名的几种聚类分析手段, 以确定不同情况下的POI。

本论文首先从Flickr上摘录下图片大数据, 比如上传时间、地点、用户等。其次, 本论文使用DBSCAN和k-means聚类分析参数来将上传图片分配给POI隐性变量。最后, 本论文采用关联规则挖掘分析（FP-growth参数）和序列样式勘探分析（GSP参数）以确认游客行为模式。

本论文以慕尼黑城市为样本, 截取2015年13,545张图片。POIs由DBSCAN和k-means聚类分析将其分配到有名的POIs。由此, 本论文证明了两种技术对不同用法的各自优势。关联规则挖掘分析显示了显著联系（support：1%−4.6%；lift：1.4%−32.1%）, 序列样式勘探分析确立了相关频率游览次序（support：0.6%−1.7%。

本论文的理论贡献在于, 根据图片数据, 通过对比分析不同聚类分析技术对确立POIs, 并且证明关联规则挖掘分析和序列样式勘探分析各有千秋又互相补充的分析技术以确立游客空间行为。

本论文的现实意义在于, 强调了大数据的来源, 比如Flickr,证明了其对于有效代替传统数据的潜力, 以分析在游客在一个旅游目的地的空间行为和动线模式。特别是这种方法实现了实时自动可操作性等优势。

本论文展示了一种方法, 这种方法通过聚类分析社交媒体上的上传图片以确立POIs, 以及通过关联规则挖掘分析和序列样式勘探分析来分析游客空间行为。本论文对于不同聚类分析以确立不同适用情况下的POIs的确立提出了独到见解。

There is a limited understanding of experience revelation in tourism. This study aims to fill the gap by investigating the influence of review platforms’ characteristics, i.e. time-dimension and interactivity, on this issue to generate a holistic view of customer experience.

This study analysed data from Google Reviews, TripAdvisors and Twitter, consisting of 41,914 records within a three-year span, about Komodo National Park, Indonesia. An explanatory sequential mixed method was performed, adopting quantitative sentiment analysis with a naïve algorithm, opinion lexicon and Latent Dirichlet Allocation for topic modelling, followed by a qualitative analysis.

The findings support the proposed interaction between the characteristics of the platforms and the extent of customer experience shared through the platforms. Further elaboration of the data brought up five propositions on the relationship between the time dimension and interactivity characteristics of the review platforms and experience sharing on the platforms.

This study presents an original and initial effort to gather a holistic view on customer experience. It brings valuable implications to the theory and practice of customer experience management, especially in the tourism sector.

目前文献对旅游体验启示的认识有限。 本研究通过调查评论平台的特征（即时间维度和交互性）对此问题的影响来填补空白, 以生成客户体验的整体视图。

本研究分析了来自谷歌评论、TripAdvisors 和 Twitter 的数据, 包括三年内关于印度尼西亚科莫多国家公园的 41,914 条记录。 运用了解释性顺序混合方法, 采用朴素算法、意见词典和隐含狄利克雷分布进行主题建模的定量情感分析, 然后进行定性分析。

调查结果支持所提出的平台特征与通过平台共享的客户体验程度之间的相互作用。 对数据的进一步阐述, 提出了评论平台的时间维度和交互特征与平台经验分享之间关系的五个命题。

本研究通过一项原创和初步的努力收集了关于客户体验的整体观点。 它为客户体验管理的理论和实践带来了宝贵的启示, 尤其是在旅游领域。