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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。

The purpose of this study is to examine hotel guests’ attitudes toward guest room technologies (GRTs) and determine whether hotel guests’ characteristics and attitudes regarding GRTs vary according to hotel guest typologies.

The data were gathered from a sample of 508 hotel guests who had stayed in a hotel in the past 12 months via a self-administered survey on Qualtrics survey software. The analysis of the study consisted of two main research steps: identification of cluster groups via the K-means cluster analysis algorithm and discriminant analysis; and performing a series of chi-square analyses to determine whether hotel guests’ characteristics and attitudes vary according to obtained hotel guest typologies.

Results indicated significant attitudinal (e.g. internet payment preference) and demographic (e.g. age) differences among the obtained hotel guest typologies regarding their attitudes toward GRTs.

The results provide valuable guidance and a pragmatic approach for those hotel managers that aim to generate tailored marketing strategies for guest segments that are interested in GRTs.

This study concentrates on GRTs with a market segmentation approach by using advanced statistical procedures. It contributes to the body of related research literature by offering empirical evidence where the study evaluates the impact of the availability of new GRTs on guest decision-making based on the principles of the theory of planned behavior. Practitioners will be able to use the presented findings to generate marketing and pricing strategies with respect to the technological needs and wants of each typology.

本论文主要研究酒店顾客对客房科技（GRTs）的态度以及检验顾客特点和背景情况对GRTs的态度是否有不同的影响。

研究样本包括508位在过去12个月内消费过的酒店顾客, 样本通过在线自助式问卷来采集。本论文的分析步骤分为两步：（1）通过K-means聚类分析和判别分析以确立群组, 以及（2）一系列Chi-square分析以判定酒店顾客特点和态度是否根据获得的顾客背景情况而有差别。

研究结果表明在获得的酒店顾客背景情况中, 态度型因子（比如网络支付喜好）和人口统计类型因子（比如年龄）对于酒店顾客GRTs态度有显著差异。

研究结果对酒店经营者针对不同细分市场对GRTs的喜好来制定个性化营销战略有着珍贵指导和实践的启示意义。

本论文主要通过一系列高级统计操作来研究GRTs以及市场细分方法。研究结果对相关文献有着显著价值, 对以计划行为理论 （Theory of Planned Behavior）为基础, 检验新型GRTs对顾客决策行为的影响提供了实践研究。行业实践者能够借鉴研究结论来制定与科技需求和细分市场需求相关的营销和定价战略。

酒店技术；客房技术；房间内部技术；市场划分；付款意愿；决策

纸张类型–研究论文