This paper examines the population heterogeneity of travel behaviours from a combined perspective of individual actors and collective behaviours. We use a social media dataset of 652,945 geotagged tweets generated by 2,933 Swedish Twitter users covering an average time span of 3.6 years. No explicit geographical boundaries, such as national borders or administrative boundaries, are applied to the data. We use spatial features, such as geographical characteristics and network properties, and apply a clustering technique to reveal the heterogeneity of geotagged activity patterns. We find four distinct groups of travellers: local explorers (78.0%), local returners (14.4%), global explorers (7.3%), and global returners (0.3%). These groups exhibit distinct mobility characteristics, such as trip distance, diffusion process, percentage of domestic trips, visiting frequency of the most-visited locations, and total number of geotagged locations. Geotagged social media data are gradually being incorporated into travel behaviour studies as user-contributed data sources. While such data have many advantages, including easy access and the flexibility to capture movements across multiple scales (individual, city, country, and globe), more attention is still needed on data validation and identifying potential biases associated with these data. We validate against the data from a household travel survey and find that despite good agreement of trip distances (one-day and long-distance trips), we also find some differences in home location and the frequency of international trips, possibly due to population bias and behaviour distortion in Twitter data. Future work includes identifying and removing additional biases so that results from geotagged activity patterns may be generalised to human mobility patterns. This study explores the heterogeneity of behavioural groups and their spatial mobility including travel and day-to-day displacement. The findings of this paper could be relevant for disease prediction, transport modelling, and the broader social sciences.

中文翻译：

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从个人行为到集体行为：基于社交媒体数据探索人口流动的异质性

本文从个体行为者和集体行为的综合角度考察了旅行行为的人口异质性。我们使用由2,933名瑞典Twitter用户生成的652,945个带有地理标记的推文的社交媒体数据集，平均时间跨度为3.6年。不会将明确的地理边界（例如国界或行政边界）应用于数据。我们使用空间特征，例如地理特征和网络属性，并应用聚类技术来揭示地理标记活动模式的异质性。我们发现了四个不同的旅行者类别：本地探险者（78.0％），本地返回者（14.4％），全球探险者（7.3％）和全球返回者（0.3％）。这些群体展现出独特的流动性特征，例如出行距离，扩散过程，国内出行百分比，访问量最高的位置的访问频率以及地理位置标记的位置总数。带有地理标签的社交媒体数据正逐渐作为用户提供的数据源纳入到旅行行为研究中。尽管此类数据具有许多优势，包括易于访问和捕获跨多个尺度（个人，城市，国家和全球）的移动的灵活性，但仍需要在数据验证和识别与这些数据相关的潜在偏见方面给予更多关注。我们根据家庭旅行调查中的数据进行了验证，发现尽管出行距离（一日和长途出行）的一致性很好，但我们也发现了居家位置和国际出行频率的一些差异，这可能是由于人口偏见和Twitter数据中的行为失真。未来的工作包括识别和消除其他偏差，以便可以将地理标记活动模式的结果推广到人类活动模式。这项研究探讨了行为群体的异质性及其空间流动性，包括旅行和日常迁移。本文的发现可能与疾病预测，运输模型和更广泛的社会科学有关。