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LBSLab: A User Data Collection System in Mobile Environments

Published: 08 October 2018 Publication History

Abstract

Thanks to the emergence of mobile computing technologies, location-based services (LBS) have been widely used. Massive data of LBS user activities would be useful for studying human mobility and urban computing. In this paper, we design and implement LBSLab, a system which facilitates large-scale data collection of mobile user activities, and provides data visualization in an informative way. LBSLab interacts with users via a mini-program built in the WeChat app, assembling several representative location-related functions such as conducting check-ins. A mobile user can join a data collection experiment by simply scanning a QR code. LBSLab can serve as an efficient system to study the user behavior of LBS-related applications.

References

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Hazim Almuhimedi, Florian Schaub, and et al. 2015. Your Location has been Shared 5, 398 Times!: A Field Study on Mobile App Privacy Nudging. In Proc. of ACM CHI. 787--796.
[2]
Guojun Qin, Constantinos Patsakis, and Mélanie Bouroche. 2014. Playing Hide and Seek with Mobile Dating Applications. In Proc. of IFIP SEC. 185--196.
[3]
Sandra Servia Rodríguez, Kiran K. Rachuri, and et al. 2017. Mobile Sensing at the Service of Mental Well-being: a Large-scale Longitudinal Study. In Proc. of WWW. 103--112.
[4]
Niels van Berkel, Jorge Gonçalves, Simo Hosio, and Vassilis Kostakos. 2017. Gamification of Mobile Experience Sampling Improves Data Quality and Quantity. IMWUT 1, 3 (2017), 107:1--107:21.

Cited By

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  • (2021)Recommendations for Conducting Longitudinal Experience Sampling StudiesAdvances in Longitudinal HCI Research10.1007/978-3-030-67322-2_4(59-78)Online publication date: 12-Aug-2021
  • (2018)MHC '18Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers10.1145/3267305.3274134(590-593)Online publication date: 8-Oct-2018

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cover image ACM Conferences
UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers
October 2018
1881 pages
ISBN:9781450359665
DOI:10.1145/3267305
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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 October 2018

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Author Tags

  1. Data Collection
  2. Location-Based Services
  3. Mini-Program
  4. Urban Computing
  5. User Behavior

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

View all
  • (2021)Recommendations for Conducting Longitudinal Experience Sampling StudiesAdvances in Longitudinal HCI Research10.1007/978-3-030-67322-2_4(59-78)Online publication date: 12-Aug-2021
  • (2018)MHC '18Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers10.1145/3267305.3274134(590-593)Online publication date: 8-Oct-2018

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