Automated semantic trajectory annotation with indoor point-of-interest visits in urban areas

Abstract

User trajectories contain a wealth of implicit information. The places that people visit, provide us with information about their preferences and needs. Furthermore, it provides us with information about the popularity of places, for example at which time of the year or day these places are frequently visited. The potential for behavioral analysis of trajectories is widely discussed in literature, but all of these methods need a pre-processing step: the geometric trajectory data needs to be transformed into a semantic collection or sequence of visited points-of-interest that is more suitable for data mining. Especially indoor activities in urban areas are challenging to detect from raw trajectory data. In this paper, we propose a new algorithm for the automated detection of visited points-of-interest. This algorithm extracts the actual visited points-of-interest well, both in terms of precision and recall, even for the challenging urban indoor activity detection. We demonstrate the strength of the algorithm by comparing it to three existing and widely used algorithms, using annotated trajectory data, collected through an experiment with students in the city of Hengelo, The Netherlands. Our algorithm, which combines multiple trajectory pre-processing techniques from existing work with several novel ones, shows significant improvements.

Developing a Data Model of Indoor Points of Interest to Support Location-Based Services

“…Also, in an indoor setting, [25] proposed a location-aware POI recommender system based on user preferences mined from social networking data. Indoor POIs have also been used to build an indoor facility information and visualization system [26], annotators to denote user visits in urban areas [27], generating large scale maps [28] and in labeling objects and spaces in AR platforms [29] and navigation systems [30][31][32]. These applications, however, focused on utilizing Indoor POI as a marker for objects in indoor space, rather than differentiating its identity from POIs in the outdoors.…”

Location Privacy in the Wake of the GDPR

“…One needs to agree that we are running an urgent agenda in addressing location privacy concerns, because in this information age, and in this infoconomy, data sources are rapidly expanding and third-party inference capabilities show substantial growth. First of all, public domain geospatial base layers have drastically increased in volume, quality and geographic coverage, and thus, information once known as the yellow pages, the road infrastructure, or the land administration registry are now often online [45][46][47]. Such sources provide the background against which location intelligence gathering and interpretation is made fruitful.…”

Semantic Modelling of Ship Behavior in Harbor Based on Ontology and Dynamic Bayesian Network

“…Yuan et al [23] provided an overall picture of semantic trajectory research, believing that behavior detection is one of the nine important tasks and cutting edge studies. deGraaff et al [24] proposed a method named PIE to extract the points-of-interest and annotated them to the trajectory automatically. A framework that contains three methods for automatic annotation of semantic trajectories is proposed in the thesis of Nogueira [25].…”