Touristic travel behaviour in a spatio-temporal context: Statistical analyses for the identification and development of tourists’ behavioural patterns

研究项目“时空背景下的旅游旅游行为：识别和发展游客行为模式的统计分析”调查了游客的行为和旅游决策如何随时间和不同空间之间的变化而变化。时间部分侧重于年龄、队列和周期效应，而空间部分包括目的地选择变化以及游客的客源市场特征。该项目的另一个核心要素是发现旅行行为的影响因素和这种旅行行为变化的诱因，以便更好地估计和预测旅游需求的未来发展。与过去的研究不同，德国游客的旅游行为模式将使用一个全面的、具有代表性的年度数据集进行分析，该数据集将扩展到关于潜在影响因素的次要数据。到目前为止，这个数据集主要用于横断面单变量分析。然而，旅游地理学和统计学之间的综合研究合作使现代统计技术的应用能够进一步检查这一数据集，以发现旅游决策和旅游行为的动态模式。这将使人们能够在时空背景下识别和系统化旅游需求的变化，并从旅游研究的角度对这些变化进行解释。将来自辅助数据源的附加信息整合到原始数据集中，使得有可能专门调查外部影响因素(例如，社会、政治和经济环境)，以及已经包括在数据集中的旅行行为的内部影响因素(例如，社会人口学、地理来源)。因此，旅游需求的决定因素可以从微观和宏观两个层面得出结论。先进的统计方法目前在旅游研究中很少使用。特别是在统计和旅游研究方面，应用和发展更复杂的技术来分析长期研究的横断面数据尤其具有创新性。该项目的目的是加强对旅行行为和旅行决策的了解，超越一次性调查研究的知识，并开发改进的应用统计方法以及以互动门户网站的形式公布和传播科学研究成果的新方法。

The research project „Touristic travel behaviour in a spatio-temporal context: Statistical analyses for the identification and development of tourists’ behavioural patterns“ investigates how tourists’ behaviour and touristic travel decision-making changes over time and between different spaces. The temporal component focuses in particular on age, cohort and period effects while the spatial component includes destination choice changes as well as tourists’ source market characteristics. Another core element of this project is to detect influencing factors of travel behaviour and triggers of such changes in travel behaviour in order to better estimate and predict future developments of tourism demand. In contrast to past research, travel behaviour patterns of German tourists will be analysed using a comprehensive, representative, yearly dataset that will be extended with secondary data on potential influencing factors. So far, this dataset has mainly been used for cross-sectional univariate analyses. The integrative research cooperation between tourism geographies and statistics, however, enables the application of modern statistical techniques to further inspect this dataset to detect dynamic patterns of travel decision-making and travel behaviour. This will allow the identification and systematisation of changes of tourism demand in a spatio-temporal context as well as the interpretation of these changes from a tourism research perspective. The integration of additional information from secondary data sources into the original dataset makes it possible to specifically investigate external influencing factors (e g. social, political and economic environment), besides internal influencing factors of travel behaviour (e. g. sociodemography, geographical origin) already included in the dataset. Hence, conclusions on determinants of tourism demand can both be drawn from a micro and macro level perspective. Advanced statistical methods are currently only rarely used in tourism research. Especially the application and development of more sophisticated techniques to analyse cross-sectional data for long-term studies is particularly innovative, both for statistics and tourism research. This project aims to enhance the understanding of travel behaviour and travel decision-making going beyond the knowledge from one-time survey studies and to develop refined methods of applied statistics as well as novel ways to publish and impart scientific research results in form of an interactive web portal.