research-article

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

Authors:

Victor de Graaff,

Maurice van KeulenAuthors Info & Claims

SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing

Pages 552 - 559

https://doi.org/10.1145/2851613.2851709

Published: 04 April 2016 Publication History

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.

References

[1]

V. de Graaff, M. van Keulen, and R. A. de By, "Towards geosocial recommender systems," in 4th Intern. Workshop on Web Intelligence & Communities (WI&C 2012), Lyon, France, ACM, 2012.

Digital Library

[2]

V. de Graaff, D. Pfoser, M. van Keulen, and R. A. de By, "Spatiotemporal behavior profiling: A treasure hunt case study," in Proc. of Web & Wireless GIS, Springer Verlag, 2015.

[3]

Z. Yan, J. Macedo, C. Parent, and S. Spaccapietra, "Trajectory ontologies and queries," Transactions in GIS, vol. 12, no. s1, pp. 75--91, 2008.

[4]

L. O. Alvares, A. Palma, G. Oliveira, and V. Bogorny, "Weka-STPM: from trajectory samples to semantic trajectories," in Proceedings of the XI Workshop de Software Livre, WSL, vol. 10, pp. 164--169, 2010.

[5]

Z. Yan, D. Chakraborty, C. Parent, S. Spaccapietra, and K. Aberer, "SeMiTri: a framework for semantic annotation of heterogeneous trajectories," in Proc. of the 14th Int. Conf. on EDBT, pp. 259--270, ACM, 2011.

Digital Library

[6]

Z. Yan, D. Chakraborty, C. Parent, S. Spaccapietra, and K. Aberer, "Semantic trajectories: Mobility data computation and annotation," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 4, no. 3, p. 49, 2013.

Digital Library

[7]

D. Ashbrook and T. Starner, "Using GPS to learn significant locations and predict movement across multiple users," Personal and Ubiquitous Computing, vol. 7, no. 5, pp. 275--286, 2003.

Digital Library

[8]

Y. Zheng and X. Xie, "Learning location correlation from GPS trajectories," in 11th Intern. Conf. on Mobile Data Management, pp. 27--32, IEEE, 2010.

Digital Library

[9]

L. O. Alvares, V. Bogorny, B. Kuijpers, J. A. F. de Macedo, B. Moelans, and A. Vaisman, "A model for enriching trajectories with semantic geographical information," in Proceedings of the 15th Int. Conf. on Adv. in GIS, p. 22, ACM, 2007.

Digital Library

[10]

A. T. Palma, V. Bogorny, B. Kuijpers, and L. O. Alvares, "A clustering-based approach for discovering interesting places in trajectories," in Proceedings of the 2008 ACM symposium on Applied computing, pp. 863--868, ACM, 2008.

Digital Library

[11]

J. A. M. Rocha, G. Oliveira, L. O. Alvares, V. Bogorny, and V. C. Times, "DB-SMoT: A direction-based spatio-temporal clustering method," in Intelligent systems (IS), 2010 5th IEEE international conference, pp. 114--119, IEEE, 2010.

[12]

M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise.," in KDD, vol. 96, pp. 226--231, 1996.

Digital Library

[13]

E. Stefanakis, "SELF: Semantically enriched line simplification," Int. Journal of GIS, pp. 1--19, 2015.

Digital Library

[14]

Y. Chen, K. Jiang, Y. Zheng, C. Li, and N. Yu, "Trajectory simplification method for location-based social networking services," in Proc. of the 2009 Int. WS on LBSN, pp. 33--40, Citeseer, 2009.

Digital Library

[15]

B. Guc, M. May, Y. Saygin, and C. Körner, "Semantic annotation of GPS trajectories," in 11th AGILE international conference on GIS, 2008.

[16]

Y. Tao, D. Papadias, and Q. Shen, "Continuous nearest neighbor search," in Proceedings of the 28th Int. Conf. on VLDB, pp. 287--298, 2002.

Digital Library

[17]

J. H. Kang, W. Welbourne, B. Stewart, and G. Borriello, "Extracting places from traces of locations," in Proc. of the 2nd ACM Int. WS on Wireless mobile appl. and services on WLAN hotspots, pp. 110--118, ACM, 2004.

Digital Library

[18]

P. A. Zandbergen, "Accuracy of iPhone locations: A comparison of assisted GPS, WiFi and cellular positioning," Trans. in GIS, vol. 13, pp. 5--25, 2009.

[19]

Q. Li, Y. Zheng, X. Xie, Y. Chen, W. Liu, and W.-Y. Ma, "Mining user similarity based on location history," in Proc. of the 16th Int. Conf. on Advances in GIS, SIGSPATIAL '08, pp. 34:1--34:10, ACM, 2008.

Digital Library

[20]

V. de Graaff, R. A. de By, M. van Keulen, and J. Flokstra, "Point of interest to region of interest conversion," in Proc. of the 21st Int. Conf. on Advances in GIS, SIGSPATIAL'13, pp. 388--391, ACM, 2013.

Digital Library

[21]

S. Allen, V. Graupera, and L. Lundrigan, "PhoneGap," in Pro Smartphone Cross-Platform Development, pp. 131--152, Springer, 2010.

Cited By

Cheng JZhang XLuo PHuang JHuang J(2022)An unsupervised approach for semantic place annotation of trajectories based on the prior probabilityInformation Sciences: an International Journal10.1016/j.ins.2022.06.034607:C(1311-1327)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.06.034
Angelis SKotis KSpiliotopoulos D(2021)Semantic Trajectory Analytics and Recommender Systems in Cultural SpacesBig Data and Cognitive Computing10.3390/bdcc50400805:4(80)Online publication date: 16-Dec-2021
https://doi.org/10.3390/bdcc5040080
Massollar JGarcia A(2021)FenceBot: An Elderly Tracking App for Mitigating Health Risk Contacts2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD49262.2021.9437612(1009-1014)Online publication date: 5-May-2021
https://doi.org/10.1109/CSCWD49262.2021.9437612
Show More Cited By

Index Terms

Automated semantic trajectory annotation with indoor point-of-interest visits in urban areas
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing theory, concepts and paradigms
      1. Mobile computing
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems

Recommendations

Time-aware point-of-interest recommendation
SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

The availability of user check-in data in large volume from the rapid growing location based social networks (LBSNs) enables many important location-aware services to users. Point-of-interest (POI) recommendation is one of such services, which is to ...
NGPR: A comprehensive personalized point-of-interest recommendation method based on heterogeneous graphs
Abstract
Nowadays, many people like to share the places they visited in the Location-based Social Networks (LBSNs). A Point of Interest (POI) recommendation, as one of the location-based services, helps users find new locations they prefer to visit. ...
Detecting Event Visits in Urban Areas via Smartphone GPS Data
ECIR 2014: Proceedings of the 36th European Conference on IR Research on Advances in Information Retrieval - Volume 8416

In geographic search tasks, where the location of the user is an important part of the task context, knowing whether or not a user has visited a location associated with a returned result could be a useful indicator of system performance. In this paper ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing

April 2016

2360 pages

ISBN:9781450337397

DOI:10.1145/2851613

Conference Chair:
Sascha Ossowski
University Rey Juan Carlos, Spain

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 April 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SAC 2016

Sponsor:

SIGAPP

SAC 2016: Symposium on Applied Computing

April 4 - 8, 2016

Pisa, Italy

Acceptance Rates

SAC '16 Paper Acceptance Rate 252 of 1,047 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25

Sponsor:
sigapp

The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
241
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)1

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Cheng JZhang XLuo PHuang JHuang J(2022)An unsupervised approach for semantic place annotation of trajectories based on the prior probabilityInformation Sciences: an International Journal10.1016/j.ins.2022.06.034607:C(1311-1327)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.06.034
Angelis SKotis KSpiliotopoulos D(2021)Semantic Trajectory Analytics and Recommender Systems in Cultural SpacesBig Data and Cognitive Computing10.3390/bdcc50400805:4(80)Online publication date: 16-Dec-2021
https://doi.org/10.3390/bdcc5040080
Massollar JGarcia A(2021)FenceBot: An Elderly Tracking App for Mitigating Health Risk Contacts2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD49262.2021.9437612(1009-1014)Online publication date: 5-May-2021
https://doi.org/10.1109/CSCWD49262.2021.9437612
Ibrahim AZhang HClinch SHarper S(2021)From GPS to semantic data: how and why—a framework for enriching smartphone trajectoriesComputing10.1007/s00607-021-00993-z103:12(2763-2787)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/s00607-021-00993-z
He JChen H(2020)Geographical Stacking Point Map of Staying Points2020 5th IEEE International Conference on Big Data Analytics (ICBDA)10.1109/ICBDA49040.2020.9101275(73-79)Online publication date: May-2020
https://doi.org/10.1109/ICBDA49040.2020.9101275
Mieth CSon YHaas P(2019)Semantic enrichment of spatio-temporal production data to determine lead times for manufacturing simulationProceedings of the Winter Simulation Conference10.5555/3400397.3400563(2061-2072)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3400397.3400563
Georgiadou Yde By RKounadi O(2019)Location Privacy in the Wake of the GDPRISPRS International Journal of Geo-Information10.3390/ijgi80301578:3(157)Online publication date: 22-Mar-2019
https://doi.org/10.3390/ijgi8030157
Wen YZhang YHuang LZhou CXiao CZhang FPeng XZhan WSui Z(2019)Semantic Modelling of Ship Behavior in Harbor Based on Ontology and Dynamic Bayesian NetworkISPRS International Journal of Geo-Information10.3390/ijgi80301078:3(107)Online publication date: 27-Feb-2019
https://doi.org/10.3390/ijgi8030107
Mieth C(2019)Semantic Enrichment of Spatio-Temporal Production Data to Determine Lead Times for Manufacturing Simulation2019 Winter Simulation Conference (WSC)10.1109/WSC40007.2019.9004753(2061-2072)Online publication date: Dec-2019
https://doi.org/10.1109/WSC40007.2019.9004753
Arslan MCruz CGinhac D(2019)Semantic enrichment of spatio-temporal trajectories for worker safety on construction sitesPersonal and Ubiquitous Computing10.1007/s00779-018-01199-523:5-6(749-764)Online publication date: 30-Jan-2019
https://dl.acm.org/doi/10.1007/s00779-018-01199-5
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten