From GPS to semantic data: how and why—a framework for enriching smartphone trajectories

Abstract

Deriving human behaviour from smartphone location data is a multitask enrichment process that can be of value in behavioural studies. Optimising the algorithmic details of the enrichment tasks has shaped the current advances in the literature. However, the lack of a processing framework built around those advances complicates the planning for implementing the enrichment. This work fulfils the need for a holistic and integrative view that comprehends smartphone-specific requirements and challenges to help researchers plan the implementation. We propose a structural framework from a systematic literature review conducted to pinpoint the main challenges and requirements of research on enriching location data. We classify findings based on the enrichment task and integrate them accordingly into workflows that facilitate the task’s implementation. These workflows help researchers better streamline their implementations of the enrichment process and analyse errors within and across tasks. Moreover, researchers can integrate the presented findings with the proposed opportunities to better predict the impact of their research.

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.…”

A general methodology for building multiple aspect trajectories

“…The social media posts were generated synthetically and represent posts on Twitter. To produce a dataset of MATs, we instantiate the semantic enrichment process described in [2] and execute its modules via the MAT-Builder UI. The modules are executed in this order: trajectory pre-processing, trajectory segmentation, and enrichment.…”

MAT-Builder: a System to Build Semantically Enriched Trajectories

“…The backend is the core component of our system as it represents the MAT-BUILDER's query processing engine. Following the process described in [10], we designed the backend to include three main modules: trajectory pre-processing, trajectory segmentation, and segment enrichment. Each module provides a subset of the system functionalities.…”