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The paper aims to comprehensively use the knowledge of tourist spatial behaviour to improve World Heritage Site (WHS) management. Efficient heritage management can be achieved if critical aspects such as tourist spatial activities were better and comprehensively understood, primarily at the micro-level. Inaccurate information on these essential aspects will potentially cause problems in managing a WHS and compromises its Outstanding Universal Value. This study aims to extend the knowledge of tourist spatial behaviour by including the non-spatial and spatial characteristics of the tourists to better manage a WHS.

This study uses a trip diary and a GPS tracking app with 384 free independent tourists as the study respondents. Melaka WHS was chosen as the study area. The data were complemented by an aggregative analysis method to extract different discrete patterns based on individual itineraries.

This paper indicates a noticeable relationship between individuals’ non-spatial and spatial characteristics. It suggests that integrating these two characteristics can provide more comprehensive knowledge of tourist behaviour.

This paper provides a different perspective to improving WHS management by determining the operative tools to develop an effective visitor management plan.

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 paper is to explore external, mediator and moderator factors that influence tourist intention to use GPS-based navigation apps from the perspective of spatiotemporal tourist behavior (STTB).

A total of 636 valid questionnaires were selected from tourists visiting Lijiang Ancient Town. The partial least squares-structural equation modeling with the SmartPLS approach was adopted to estimate and validate the model.

The results of this paper showed that wayfinding efficiency, sensation-seeking behavior and spatial ability (SA) influence tourists’ intention to use (IU) navigation apps. A mediator of perceived location accuracy between SA and IU has been found. A multigroup generation moderator is verified in the model.

This paper provides a better understanding of the relationship between tourist spatiotemporal behavior and navigation apps, presenting practical suggestions for app developers, destination managers and vacation planners.

While the effects of information technology on tourist behavior have become a topic of interest among tourism industry stakeholders, this paper examines the effects of STTB on the acceptance of navigation apps in reverse, which enriches the theoretical framework.

本文从时空旅游行为 (STTB) 的角度探讨了影响游客使用基于 GPS 的导航应用程序的外部、中介和调节因素。

本研究共从丽江古城旅游者中抽取了636份有效问卷。采用 SmartPLS 方法的偏最小二乘结构方程建模 (PLS-SEM) 来估计和验证模型。

研究结果表明, 寻路效率 (WE)、感觉寻求行为 (SSB) 和空间能力 (SA) 会影响游客的使用意向 (IU) 导航应用程序。研究还发现感知位置准确度 (PLA)是空间能力 (SA) 和使用意图 (IU) 之间的中介变量。世代在多组比较模型中作为调节变量。

本文提供了对旅游时空行为与导航应用程序之间关系的更好理解, 为应用程序开发人员、目的地管理者和度假规划者提供了实用建议。

虽然信息技术对游客行为的影响已成为旅游业利益相关者感兴趣的话题, 但本文研究了 STTB 对导航应用程序接受度的影响, 从而丰富了理论框架。

The behavior of tourists strongly depends on the availability and quality of information. Too little information as well as too much can be disorienting and forces many tourists to join the majority visiting major sights. This causes a few crowded places in contrast to many which are under‐utilized. A Destination Management Organization has the goal to spread tourists more evenly, whereas the tourists would like to enjoy the destination to its full potential according to their personal interests. The paper aims to focus on the issues surrounding the creation of a mobile tourist guide.

A field trial was conducted in the summer of 2005 to study the following questions as a precondition for the development: Is it possible to seed generic interest profiles in the mobile context that allow the accurate prediction of actual rankings? Are the interest profiles sufficiently diverse to base personalized tours on individual interest profiles instead of interest prototypes? How do personalized tours affect the spatial behavior of tourist? Three methods to elicit the generic preferences of tourist in the mobile context are compared with actual rankings using Spearman's rank order coefficient.

The diversity of the interest profiles is analyzed in various ways leading to the conclusion that personalized interest profiles are necessary. For the gathered profiles tours are computed and simulated in order to gain a first insight into the effect on the spatial behavior of tourists.

The dynamic tour guide is supporting both goals by means of pervasive computing based on the actual context which is defined by personal interests, location and schedule of a tourist. It enables a personalized, spontaneous and guided tour.

This paper gathers tourism digital footprint from online travel platforms, choosing social network analysis method to learn the structure of destination networks and to probe into the features of tourist flow network structure and flow characteristics in Guilin of China.

The digital footprint of tourists can be applied to study the behaviors and laws of digital footprint. This research contributes to improving the understanding of demand-driven network relationships among tourist attractions in a destination.

(1) Yulong River, Yangshuo West Street, Longji Terraced Fields, Silver Rock and Four Lakes are the divergent and agglomerative centers of tourist flow, which are the top tourist attractions for transiting tourists. (2) The core-periphery structure of the network is clearly stratified. More specifically, the core nodes in the network are prominent and the core area of the network has weak interaction with the peripheral area. (3) There are eight cohesive subgroups in the network structure, which contains certain differences in the radiation effects.

This research aims at exploring the spatial network structure characteristics of tourism flows in Guilin by analyzing the online footprints of tourists. It takes a good try to analyze the application of network footprint with the research of tourism flow characteristics, and also provides a theoretical reference for the design of tourist routes and the cooperative marketing among various attractions.

The research analyses the spatial-temporal behaviour of international visitors using mobile positioning data (MPD) in three second-tier cities in Hungary: Szeged, Debrecen and Pécs. The purpose of this study is to identify the origins of visitors, length of stay, seasonal concentrations and mobility between cities.

A large volume of data generated by foreign mobile phone users was processed and analysed for six consecutive months in 2018. The movements of around 3.5 million foreigners visiting Hungary were captured.

The data showed significant differences in tourist turnover, mobility patterns and flows indicating different levels of tourism activity and potential in the three cities. This included day trips, cross-border tourism, seasonal fluctuations and activity-driven demand (e.g. special events and cultural tourism).

MPD cannot fully explain or predict drivers of visitor behaviour, such as purpose of visit, the precise activities that they undertake there and the experiences that they gain. It can, however, provide information on spatial and temporal flows, patterns and concentrations of visitors.

The investigated cities should follow distinct tourism development policies to attract more tourists and strengthen the links with their neighbouring destinations at the same time as differentiating themselves through marketing as competitive alternative destinations.

Augmenting tourism has socio-economic implications for residents and communities. The development of cultural tourism and local event-based tourism must incorporate community needs.

The paper analyses the extent to which MPD can provide insights into visitors’ spatio-temporal mobility and flows in relatively under-visited second-tier cities and it highlights opportunities and gaps for big data research in an urban context.

The COVID-19 pandemic has significantly impacted global tourism, with international travel bearing the burden of restrictions. Domestic tourism has also faced substantial challenges. This paper aims to analyse the impact of the COVID-19 pandemic on domestic tourism in Spain, focusing on travel from Madrid (the country’s capital) to other tourist destinations.

Mobile phone data has been used to study the evolution of tourist trips over the summers of 2019, 2020 and 2021. Regression models are used to explain the number of visitors at destinations.

The pandemic not only caused a drastic drop in tourist flows but also disrupted the overall pattern of the domestic flow system. Winning destinations were typically areas in proximity to Madrid and less densely populated destinations, while urban destinations were major losers. The preferences of domestic tourists varied notably by income group, but the decrease in trip volumes showed only marginal differences.

The paper demonstrates the potential of mobile phone data analysis to study the uneven impact of external shocks, such as the COVID-19 pandemic, on tourist destinations. This approach considers spatial resilience heterogeneity within regions or provinces. By incorporating income information, the analysis introduces a social dimension to highly detailed spatial data, surpassing traditional studies conducted at the regional or national levels.

COVID-19大流行对全球旅游业产生了重大影响，国际旅行受到了限制的影响最为严重。国内旅游也面临着重大挑战。本文分析了COVID-19大流行对西班牙国内旅游的影响，重点关注从马德里（该国首都）到其他旅游目的地的旅行。

本研究使用移动电话数据研究了2019年、2020年和2021年夏季旅游出行的演变。采用回归模型解释了各目的地游客数量。

大流行不仅导致了旅游流量急剧下降，还扰乱了国内流动系统的总体模式。获胜的目的地通常是马德里附近的地区和人口较稀少的目的地，而城市目的地是主要的输家。国内游客的偏好在收入群体之间有明显差异，但旅行量的减少只显示出边际差异。

本文展示了使用移动电话数据分析研究外部冲击（如COVID-19大流行）对旅游目的地的不均匀影响的潜力。该方法考虑了区域或省份内的空间弹性异质性。通过整合收入信息，该分析为高度详细的空间数据引入了社会维度，超越了传统在区域或国家水平进行的研究。

With its capabilities for business mapping, geospatial analysis and its contribution to decision making, geographic information system (GIS) seems to be a valuable tool especially applicable in the discipline tourism geography. The capabilities of geospatial analysis for tourist consumer research at destinations will be exemplified by the case of German low-cost carrier passengers (LCCP) on Majorca island, Spain, one of the worlds' leading coastal mass holiday destinations with an annual visitor demand of around 10 million arrivals. The survey puts together primary and secondary research to profile LCCP groups located in different tourism spaces around the island. The approach as well as results shall motivate stakeholders in the tourism industry, especially destinations, to enlarge their marketing and management issues towards geospatial analysis.

Leisure‐based tourism constitutes an important part in China’s domestic tourist market. This article reports the findings of a case study of leisure‐based tourist behavior conducted in Changchun, the capital city of Northeast China’s Jilin Province. The composition and demographic profile of the leisure‐based tourist market are presented. Leisure‐based tourists’ preference with regard to destination choice, leisure facilities used, and accommodation is analyzed. It is concluded that in‐depth understanding of leisure‐based tourist behavior will contribute significantly to the enhancement of tourist city images, modification of urban tourist attraction systems, effective exploitation of urban tourism resources and destination management and marketing.

Understanding the essence of tourism flows is one of the fundamental undertakings of tourism geography research and a key issue behind effective destination management and development. However, thus far, few studies have analysed tourist movement on a national scale. This is due to the deficiency of spatially accurate data that can be used for recording tourists’ intra-national movements. This paper aims to illustrate the impact of major gateways on national tourism flows by using tracking data; and demonstrate and compare the use and applicability of tracking data on a national scale.

In this study, the authors analyse foreign tourists’ movements using two spatially and temporally precise tracking data sets – call detail records from passive mobile positioning data and GPS data from smartphones – in two countries, Estonia and Israel. The movements of international tourists entering the countries via main gateways are studied, with a focus on the impact of gateways on intra-national tourism flows.

The results clearly show the impact of gateways on the concentration of tourists. In the two respective countries, the critical mass of time was spent in close proximity to the gateway and, due to distance decay, a dramatic decrease was seen in visitation to areas that were distanced from both countries’ core areas.

To the best of authors’ knowledge, this is the first time when tourism flows attained from tracking data are compared on a national scale for two countries.