Extracting Hotspots without A-priori by Enabling Signal Processing over Geospatial Data

Détails

ID Serval
serval:BIB_B08A82A32CA6
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
Extracting Hotspots without A-priori by Enabling Signal Processing over Geospatial Data
Titre de la conférence
SIGSPATIAL'17 Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Auteur(s)
Kulkarni V., Moro A., Chapuis B., Garbinato B.
Editeur
ACM
Adresse
Redondo Beach, CA, USA
ISBN
978-1-4503-5490-5
Statut éditorial
Publié
Date de publication
2017
Langue
anglais
Notes
Article 79
Résumé
The proliferation of mobile devices equipped with internet connectivity and global positioning functionality (GPS) has resulted in the generation of large volumes of spatiotemporal data. This has led to the rapid evolution of location-based services. The anticipatory nature of these services, demand exploitation of a broader range of user information for service personalization. Determining the users' places of interest, i.e. hotspots is critical to understand their behaviors and preferences. Existing techniques to detect hotspots rely on a set of a-priori determined parameters that are either dataset dependent or derived without any empirical basis. This leads to biased results and inaccuracies in estimating the total number of hotspots belonging to a user, their shape and the average dwelling time. In this paper, we propose a parameter-less technique for extracting hotspots from spatiotemporal trajectories without any a-priori assumptions. We eliminate parameter dependence by treating trajectories as spatiotemporal signals and rely on signal processing algorithms to derive hotspots. We experimentally show that, our technique does not necessitate any spatiotemporal or behavior dependent bounds, which makes it suitable to extract hotspots from a larger variety of datasets and across users having disparate mobility behaviors. Our evaluation results on a real world dataset, show accuracy rates exceeding 80% and outperforms traditional clustering techniques used for hotspot detection.
Création de la notice
20/07/2018 14:27
Dernière modification de la notice
21/08/2019 5:12
Données d'usage