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179I01215
Abstract
Governments are increasingly interested in the use of crowdsourced spatial tracking data to gain information on the travel behaviour of their citizens. To improve the reliability of reporting in such mobility studies, this paper systematically analyses the propagation of errors from low level operations to high level indicators, such as the modal split and travelled distances. We find that most existing metrics in literature are insufficient to fully quantify this evolution of data quality. The propagation channels are presented schematically and a new approach to quantify the spatial data quality at the end of each processing stage is proposed. This procedure, within the context of Smart Cities, ensures that the data analytics and resulting changes in policy are sufficiently substantiated by credible and reliable information.
Keywords
data quality, geospatial data, crowdsensing, data processing, error propagation

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Citation

Please use this url to cite or link to this publication:

MLA
Van Gheluwe, Casper, et al. “Error Sources in the Analysis of Crowdsourced Spatial Tracking Data.” 202019 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS), IEEE, 2019, pp. 183–88.
APA
Van Gheluwe, C., Lopez, A. J., & Gautama, S. (2019). Error sources in the analysis of crowdsourced spatial tracking data. In 202019 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS) (pp. 183–188). Kyoto, Japan: IEEE.
Chicago author-date
Van Gheluwe, Casper, Angel J. Lopez, and Sidharta Gautama. 2019. “Error Sources in the Analysis of Crowdsourced Spatial Tracking Data.” In 202019 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS), 183–88. IEEE.
Chicago author-date (all authors)
Van Gheluwe, Casper, Angel J. Lopez, and Sidharta Gautama. 2019. “Error Sources in the Analysis of Crowdsourced Spatial Tracking Data.” In 202019 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS), 183–188. IEEE.
Vancouver
1.
Van Gheluwe C, Lopez AJ, Gautama S. Error sources in the analysis of crowdsourced spatial tracking data. In: 202019 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS). IEEE; 2019. p. 183–8.
IEEE
[1]
C. Van Gheluwe, A. J. Lopez, and S. Gautama, “Error sources in the analysis of crowdsourced spatial tracking data,” in 202019 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS), Kyoto, Japan, 2019, pp. 183–188.
@inproceedings{8588250,
  abstract     = {Governments are increasingly interested in the use of crowdsourced spatial tracking data to gain information on the travel behaviour of their citizens. To improve the reliability of reporting in such mobility studies, this paper systematically analyses the propagation of errors from low level operations to high level indicators, such as the modal split and travelled distances. We find that most existing metrics in literature are insufficient to fully quantify this evolution of data quality. The propagation channels are presented schematically and a new approach to quantify the spatial data quality at the end of each processing stage is proposed. This procedure, within the context of Smart Cities, ensures that the data analytics and resulting changes in policy are sufficiently substantiated by credible and reliable information.},
  author       = {Van Gheluwe, Casper and Lopez, Angel J. and Gautama, Sidharta},
  booktitle    = {202019 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS)},
  isbn         = {9781538691519},
  issn         = {2474-2503},
  keywords     = {data quality,geospatial data,crowdsensing,data processing,error propagation},
  language     = {eng},
  location     = {Kyoto, Japan},
  pages        = {183--188},
  publisher    = {IEEE},
  title        = {Error sources in the analysis of crowdsourced spatial tracking data},
  year         = {2019},
}

Web of Science
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