Mining Human Mobility Data to Discover Locations and Habits

Abstract

Many aspects of life are associated with places of human mobility patterns and nowadays we are facing an increase in the pervasiveness of mobile devices these individuals carry. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way. Therefore, detecting significant places and the frequency of movements between them is fundamental to understand human behavior. In this paper, we propose a method for discovering user habits without any a priori or external knowledge by introducing a densitybased clustering for spatio-temporal data to identify meaningful places and by applying a Gaussian Mixture Model (GMM) over the set of meaningful places to identify the representations of individual habits. To evaluate the proposed method we use two real-world datasets. One dataset contains high-density GPS data and the other one contains GSM mobile phone data in a coarse representation. The results show that the proposed method is suitable for this task as many unique habits were identified. This can be used for understanding users' behavior and to draw their characterizing profiles having a panorama of the mobility patterns from the data.

From mobility data to habits and common pathways

“…Although user movements are constrained by road or sidewalk networks, the trajectories can present dynamic and arbitrary shapes with respect to the individuals' needs. Following this idea, we cluster the SPs by using the method proposed by Andrade, Cancela, and Gama (2020). This approach proved to be more suited for identifying locations and find the most significant locations based on the frequency of visits/stops.…”

Uma Ferramenta de Visualização de Dados Georreferenciados e o Turismo Inteligente como Estudo de Caso

“…Um conceito que vem surgindo nos últimos anos é o de "turismo inteligente", como um componente de uma Smart City [Tripathy et al 2018], contemplado com a aplicação de ferramentas e abordagens inovadoras de tecnologias de informação para sua evolução contínua. Para tal, conforme [Andrade, Gama and Cancela 2019], existe uma riqueza de infraestruturas tecnológicas fornecendo grandes quantidades de dados espaço-temporais alimentando continuamente, em tempo real, enormes repositórios de dados de mobilidade humana que estão associados a muitos aspectos da vida contemporânea. Ainda, segundo [Xu et al 2021], dados de mobilidade turística podem ser vinculados a abordagens de ciência de dados para melhor compreender os destinos turísticos e suas interações.…”

Individual differences in adolescent and young adult daily mobility patterns and their relationships to big five personality traits: A behavioral genetic analysis

“…Smartphone data offer standardized, objective measures of participants' locations and movement patterns which may be useful in corroborating the findings of existing research on adolescents' daily lives. Previous research has demonstrated that human mobility patterns can be reliably measured using GPS data (Andrade et al, 2019) and that such patterns are meaningfully related to personality and daily activities in adolescence and young adulthood. Several studies have reported relationships between daily mobility patterns and personality traits in adolescence and young adulthood (Ai et al, 2019;Alessandretti et al, 2018;Stachl et al, 2020).…”