Breadcrumbs: A Rich Mobility Dataset with Point-of-Interest Annotations (short paper)

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serval:BIB_4EF866325A1C
Type
Inproceedings: an article in a conference proceedings.
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Publications
Institution
Title
Breadcrumbs: A Rich Mobility Dataset with Point-of-Interest Annotations (short paper)
Title of the conference
Proceedings of the 27th ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL)
Author(s)
Moro Arielle, Kulkarni Vaibhav, Ghiringhelli Pierre-Adrien, Chapuis Bertil, Huguenin Kévin, Garbinato Benoit
Publisher
ACM
Address
Chicago, IL, United States
Publication state
Published
Issued date
11/2019
Peer-reviewed
Oui
Pages
508-511
Language
english
Abstract
Rich human mobility datasets are fundamental for evaluating algorithms pertaining to geographic information systems. Unfortunately , existing mobility datasets-that are available to the research community-are restricted to location data captured through a single sensor (typically GPS) and have a low spatiotemporal granularity. They also lack ground-truth data regarding points of interest and the associated semantic labels (e.g., "home", "work", etc.). In this paper, we present Breadcrumbs, a rich mobility dataset collected from multiple sensors (incl. GPS, GSM, WiFi, Bluetooth) on the smartphones of 81 individuals. In addition to sensor data, Breadcrumbs contains ground-truth data regarding people points of interest (incl. semantic labels) as well as demographic attributes, contact records, calendar events, lifestyle information, and social relationship labels between the participants of the study. We describe the data collection methodology and present a preliminary quantitative analysis of the dataset. A sanitized version of the dataset as well as the source code will be made available to the research community.
Create date
25/09/2019 18:35
Last modification date
15/11/2019 7:18
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