Towards the Semantic Enrichment of Trajectories Using Spatial Data Infrastructures

Abstract

The term Semantic Trajectories of Moving Objects (STMO) corresponds to a sequence of spatial-temporal points with associated semantic information (for example, annotations about locations visited by the user or types of transportation used). However, the growth of Big Data generated by users, such as data produced by social networks or collected by an electronic equipment with embedded sensors, causes the STMO to require services and standards for enabling data documentation and ensuring the quality of STMOs. Spatial Data Infrastructures (SDI), on the other hand, provide a shared interoperable and integrated environment for data documentation. The main challenge is how to lead traditional SDIs to evolve to an STMO document due to the lack of specific metadata standards and services for semantic annotation. This paper presents a new concept of SDI for STMO, named SDI4Trajectory, which supports the documentation of different types of STMO—holistic trajectories, for example. The SDI4Trajectory allows us to propose semi-automatic and manual semantic enrichment processes, which are efficient in supporting semantic annotations and STMO documentation as well. These processes are hardly found in traditional SDIs and have been developed through Web and semantic micro-services. To validate the SDI4Trajectory, we used a dataset collected by voluntary users through the MyTracks application for the following purposes: (i) comparing the semi-automatic and manual semantic enrichment processes in the SDI4Trajectory; (ii) investigating the viability of the documentation processes carried out by the SDI4Trajectory, which was able to document all the collected trajectories.

STO2Vec: A Multiscale Spatio-Temporal Object Representation Method for Association Analysis

“…There are currently numerous studies analyzing the association relationships based on spatio-temporal co-occurrence from different perspectives. These studies can be classified into semantic trajectory-based approaches [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] and location embedding-based approaches [23][24][25][26][27][28][29][30][31][32][33] according to the analysis methods.…”

“…Alvares et al proposed the stop-move model, which converts trajectories into sequences with labels through semantic annotation, thereby mining and analyzing the interaction and association of moving objects in geographic space [11]. Based on the stop-move model, some of the research work has focused on how to better geographically associate this semantic annotation of trajectories [9,[12][13][14]; meanwhile, many studies have constructed semantic trajectory models and designed corresponding association analysis algorithms for different application domains. For instance, Ying et al used the frequent pattern of geography-time-semantics in semantic trajectories for location prediction of moving objects [15].…”

“…Associations are universal [3], making association analysis applicable to various fields with different relationship types. Sharma et al [2] grouped spatio-temporal associations into three types based on whether a temporal sequence was considered: sequential (e.g., analyzing event-oriented spatio-temporal association in video surveillance [4]), cascading (e.g., studying relationships between events, locations, and criminal activities in criminal geography [5]), and co-occurrences (e.g., similar associations between trajectories [6], co-location patterns between geographic entities [7], semantic annotation of trajectories [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22], and location embedding [23][24][25][26][27][28][29][30][31][32][33], etc.). By comparing their frequency of co-occurrence, spatio-temporal co-occurrence-based association analysis can reveal implicit associations between entities.…”