Inequalities in experiencing urban functions. An exploration of human digital (geo-)footprints

Studies on mobility inequalities have so far mostly relied on Survey data or Censuses. While such studies have demonstrated that inequalities strongly influence everyday mobility choices, these data sources lack granular information on people’s movements on a daily basis. By capitalising on high spatio-temporal resolution data provided by Spectus.ai, this study aims at investigating how the deprivation level of the area where people live influences the kinds of urban environment they are more likely to use for their everyday activities. To do this, raw GPS trajectories collected in 2019 in Great Britain (GB) are transformed into semantic trajectories where short-time changes and the functional nature of urban contexts are acknowledged as two key dimensions to understand human spatial behaviours. Hourly sequences of stops are extracted from GPS trajectories and enriched with contextual information based on a new area-based classification detecting urban functions. The data exploration shows that some human patterns are widely common across all levels of deprivation, such as the tendency to be mostly exposed to the urban context near the home location. At the same time, we show that differences exist, especially between those who live in the most deprived areas and those who live in the least deprived areas of GB. It appears that people living in the most deprived areas tend to have a less regular working pattern and be more exposed to urban-based functions and well-served areas, while those living in the least deprived areas have a more regular working patterns and are mostly exposed to the countryside and low-density areas. Our approach and results provide new insights on the temporal and contextual dimensions of mobility inequalities, informing on who is exposed to issues characterising certain urban environments. </jats:p

An approach for traffic pattern recognition integration of ship AIS data and port geospatial features

Recognition of ship traffic patterns can provide insights into the rules of navigation, maneuvering, and collision avoidance for ships at sea. This is essential for ensuring safe navigation at sea and improving navigational efficiency. With the popularization of the Automatic Identification System (AIS), numerous studies utilized ship trajectories to identify maritime traffic patterns. However, the current research focuses on the spatiotemporal behavioral feature clustering of ship trajectory points or segments while lacking consideration for multiple factors that influence ship behavior, such as ship static and maritime geospatial features, resulting in insufficient precision in ship traffic pattern recognition. This study proposes a ship traffic pattern recognition method that considers multi-attribute trajectory similarity (STPMTS), which considers ship static feature, dynamic feature, port geospatial feature, as well as semantic relationships between these features. First, A ship trajectory reconstruction method based on grid compression was introduced to eliminate redundant data and enhance the efficiency of trajectory similarity measurements. Subsequently, to quantify the degree of similarity of ship trajectories, a trajectory similarity measurement method is proposed that combines ship static and dynamic information with port geospatial features. Furthermore, trajectory clustering with hierarchical methods was applied based on the trajectory similarity matrix for dividing trajectories into different clusters. The quality of the similarity measurement results was evaluated by quality criterion to recognize the optimal number of ship traffic patterns. Finally, the effectiveness of the proposed method was verified using actual port ship trajectory data from the Tianjin Port of China, ranging from September to November 2016. Compared with other methods, the proposed method exhibits significant advantages in identifying traffic patterns of ships entering and leaving the port in terms of geometric features, dynamic features, and adherence to navigation rules. This study could serve as an inspiration for a comprehensive exploration of maritime transportation knowledge from multiple perspectives

Método para el cálculo de la similitud entre dos trayectorias basado en los sitios visitados, las actividades ejecutadas y la cronología de los episodios

Hallar trayectorias semánticas similares es de gran utilidad en campos como el mercadeo o las redes sociales, ya que permite encontrar usuarios con gustos y preferencias similares. No es una tarea trivial puesto que gran cantidad de factores influyen en el cálculo de la similitud. En esta tesis se propone un método para calcular la similitud de acuerdo con los sitios visitados, basado en un árbol de categorías que permite relacionar los tipos de sitios de manera jerárquica, brindando flexibilidad y adaptabilidad a diferentes dominios de aplicación. Dicho método se puede utilizar para calcular la similitud basada en las actividades ejecutadas por los usuarios en lugar de los sitios visitados; y se propone una extensión de este para tener en cuenta la cronología de los hechos como criterio temporal. A través de datos de usuarios reales se evidencia el funcionamiento del método y se compara con otros métodos actuales. Se analizan también diversos factores temporales que pueden influir en la medida de similitud de dos trayectorias y como han sido abordados por diferentes autores. Se plantea una serie de retos y situaciones a considerar en el aspecto temporal, y se propone un método basado en la cronología (orden) para abordar el problema.Abstract: Finding similar semantic trajectories can be useful in fields such as marketing or social networks, since it allows you to find users with similar likes and preferences. However, it is not a trivial task since many factors influence the calculation of similarity. In this thesis a method is proposed to calculate the similarity according to the visited sites, based on a tree of categories that allows to relate the types of sites in a hierarchical way, providing flexibility and adaptability to different application domains. This method can be used to calculate the similarity based on the activities executed by the users instead of the visited sites; and an extension of this is proposed to consider the chronology of events as temporal criteria. Data collected from real users is used to see how the method operates and it is compared with other current methods. Different temporal factors are analyzed and is shown how they can influence the similarity measurement of two trajectories. How they have been addressed by different authors are also analyzed. Some challenges and situations to consider in the temporal aspect and a method based on chronology (order) are proposed to address the problem.Maestrí