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Purpose — In this chapter, we will review several alternative methods of collecting data from mobile phones for human mobility analysis. We propose considering cellular network location data as a useful complementary source for human mobility research and provide case studies to illustrate the advantages and disadvantages of each method.

Methodology/approach — We briefly describe cellular phone network architecture and the location data it can provide, and discuss two types of data collection: active and passive localization. Active localization is something like a personal travel diary. It provides a tool for recording positioning data on a survey sample over a long period of time. Passive localization, on the other hand, is based on phone network data that are automatically recorded for technical or billing purposes. It offers the advantage of access to very large user populations for mobility flow analysis of a broad area.

Findings — We review several alternative methods of collecting data from mobile phone for human mobility analysis to show that cellular network data, although limited in terms of location precision and recording frequency, offer two major advantages for studying human mobility. First, very large user samples – covering broad geographical areas – can be followed over a long period of time. Second, this type of data allows researchers to choose a specific data collection methodology (active or passive), depending on the objectives of their study. The big mobile phone localization datasets have provided a new impulse for the interdisciplinary research in human mobility.

Originality/value of chapter — We propose considering cellular network location data as a useful complementary source for transportation research and provide case studies to illustrate the advantages and disadvantages of each proposed method. Mobile phones have become a kind of “personal sensor” offering an ever-increasing amount of location data on mobile phone users over long time periods. These data can thus provide a framework for a comprehensive and longitudinal study of temporal dynamics, and can be used to capture ephemeral events and fluctuations in day-to-day mobility behavior offering powerful tools to transportation research, urban planning, or even real-time city monitoring.

The purpose of this paper is to demonstrate how technological innovation serves as an enabling factor to innovation in tourism management. The motivation of this paper is related to the question of how to innovate destination marketing as a tool to manage long‐term customer relationships.

The authors use mobile positioning‐based research methods to measure visitors' behaviour. This provides new data for the detection and measurement of destination loyalty that could be used as valuable input to improve destination marketing strategy and develop new services.

The use of mobile positioning helps to improve the quality of data about tourism flows in Estonia. The authors were able to observe and measure the duration, timing, density, seasonality and dynamics of visitations. Further, it allowed also to distinguish repeat visitors. The rich dataset provided by passive mobile positioning (PMP) allowed the implementation of the proposed, more detailed, classification of segments of repeat visitors and the identification of not loyal, somewhat loyal, loyal, very loyal, functionally loyal and forced to be loyal visitors. This analysis made it possible to reveal transit, long‐term, one‐day and other specific visitors among repeat visitors.

The theoretical novelty of the paper consists in the creation of the innovation model of the destination marketing of the country and providing the new approach of segmentation of repeat visitors. Empirical novelty is the use of PMP in studying repeat visitations for destination marketing. The paper offers new ways for governments to shape service policies and allows tourism industry firms to offer new services.

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.

The purpose of this paper is to examine how consumers differ by gender in terms of the values sought from mobile data services.

The technology acceptance model (TAM) and utilitarian and hedonic value were employed to examine the differences in mobile data services usage. A total of 200 respondents participated in an online survey. Of the sample, 116 participants were female and 84 participants were male. Multiple group structural equation modeling analysis was used to examine gender differences in using mobile data services.

The findings indicated that the effect of hedonic value was stronger in the female group than the male group. Utilitarian value was a significant driving value in using mobile data services in the male group and showed a stronger effect for the male group than the female group. The results supported that the effects of hedonic value and utilitarian value on using mobile data services generated different mobile data services usages across the gender groups.

The results suggest that mobile data services can be gendered in terms of utilitarian value and hedonic value. The task‐oriented utilitarian value of mobile data services can be classified as preferred by males and the communicative and hedonic aspect of mobile data services can be classified as preferred by females.

The paper contributes to determining driving value in using mobile data services sought by each gender and the results provide insights for mobile marketers to use in successfully positioning mobile data services into targeted markets.

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.

This study aims to examine the differences in adoption of mobile data services between two age cohorts (gen Xers and baby boomers). Three elements in the extended Technology Acceptance Model – perceived usefulness, perceived ease of use, and perceived fun – were used to identify the differences in adoption of mobile data services for the two age cohorts.

A sample of 200 mobile services users drawn from a purchased consumer panel participated in an online survey. Of this sample, 67 gen Xers and 86 baby boomers were used for the analysis (n=153). Multigroup structural equation modeling analysis was used to examine the differences in adoption of mobile data services between the two age cohorts.

This study found that baby boomers perceived mobile data services as more difficult to use than gen Xers. However, the perception of usefulness of mobile data services was stronger for the baby boomers than gen Xers. Usefulness of mobile data services may be a critical motivator for baby boomers to adopt mobile data services. Mobile data service marketers should focus on mobile data service usefulness when they are targeting the baby boomer cohort.

The results of this study provide insights to assist marketers in developing and positioning appropriate mobile data services to targeted age cohorts during the adoption stage.

This chapter conceptualises a link between Industrial Revolution 4.0 (IR 4.0), big data, data science and sustainable tourism.

The author adopts a grounded theory and conceptual approach to endeavour in this exploratory research.

The outcome shows a significant rise of big data in the tourism sector under three major dimensions, i.e. business, governance and research. And, some exemplary evidence of institutions promoting the use of big data and data science for sustainable tourism has been discussed.

The conceptualised interlinkage of concepts like IR 4.0, big data, data science and sustainable development provides a valuable knowledge resource to policy-makers, researchers, businesses and students.

Tourism reconfigures the metropolitan dynamics and the patterns of use of the urban systems. The seasonal nature of tourism produces an impact on the urban hierarchies, since it affects the labor, residential, and recreational markets. As a result, people move to and in the destination and it challenges the supply of sustainable modes of transport such as public transport. This research is set within the context of three demanding challenges that tourist destinations need to face-up: to increase environmental sustainability, to enhance destination competitiveness, and finally to assure quality and comfort of public transport services for the local resident population. Camp de Tarragona region, where Costa Daurada (one of the most important Spanish tourist brands) is located, is analyzed to illustrate how different data sources can aid to confront the aforementioned challenges. Given that seasonality is a dynamic phenomenon, suitable data should be flexible in terms of its time framework. To this end data from smart travel cards provided by the consortium that manages the public transport system in the region has been analyzed. Data unveiled the impact of seasonality on the evolution of demand throughout the year, the type of transport tickets used, or changes occurred in the geographical distribution of the mobility Alternative data sources such as surveys and passive mobile positioning data have also been examined, and their pros and cons have been addressed.

The purpose of this paper is to propose and demonstrate how Tourism2vec, an adaptation of a natural language processing technique Word2vec, can serve as a tool to investigate tourism spatio-temporal behavior and quantifying tourism dynamics.

Tourism2vec, the proposed destination-tourist embedding model that learns from tourist spatio-temporal behavior is introduced, assessed and applied. Mobile positioning data from international tourists visiting Tuscany are used to construct travel itineraries, which are subsequently analyzed by applying the proposed algorithm. Locations and tourist types are then clustered according to travel patterns.

Municipalities that are similar in terms of their scores of their neural embeddings tend to have a greater number of attractions than those geographically close. Moreover, clusters of municipalities obtained from the K-means algorithm do not entirely align with the provincial administrative segmentation.

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。