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Error sources in the analysis of crowdsourced spatial tracking data

Author
Organization
Project
Flamenco: FLAnders Mobile Enacted Citizen Observations
Abstract
Governments are increasingly interested in the use of crowdsourced spatial tracking data to gain information on the travel behaviour of their citizens. This approach requires that the accuracy and the reliability of the data and transformation processes are clearly characterized. To improve the reliability of reporting in such mobility studieswe systematically analyse the propagation of errors from low level operations to high level indicators, such as the modal split and travelled distances. Studies have shown that errors that occur in early stages of the data processing can have drastic consequences on the accuracy of later stages. 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, Angel Lopez Aguirre, and Sidharta Gautama. “Error Sources in the Analysis of Crowdsourced Spatial Tracking Data.” Proceedings of 19th FEA Research Symposium. Ghent, 2019. Print.
APA
Van Gheluwe, C., Lopez Aguirre, A., & Gautama, S. (2019). Error sources in the analysis of crowdsourced spatial tracking data. Proceedings of 19th FEA Research Symposium. Presented at the 19th Faculty of Engineering and Architecture Research Symposium, Ghent.
Chicago author-date
Van Gheluwe, Casper, Angel Lopez Aguirre, and Sidharta Gautama. 2019. “Error Sources in the Analysis of Crowdsourced Spatial Tracking Data.” In Proceedings of 19th FEA Research Symposium. Ghent.
Chicago author-date (all authors)
Van Gheluwe, Casper, Angel Lopez Aguirre, and Sidharta Gautama. 2019. “Error Sources in the Analysis of Crowdsourced Spatial Tracking Data.” In Proceedings of 19th FEA Research Symposium. Ghent.
Vancouver
1.
Van Gheluwe C, Lopez Aguirre A, Gautama S. Error sources in the analysis of crowdsourced spatial tracking data. Proceedings of 19th FEA Research Symposium. Ghent; 2019.
IEEE
[1]
C. Van Gheluwe, A. Lopez Aguirre, and S. Gautama, “Error sources in the analysis of crowdsourced spatial tracking data,” in Proceedings of 19th FEA Research Symposium, Ghent, 2019.
@inproceedings{8604622,
  abstract     = {Governments are increasingly interested in the use of crowdsourced spatial tracking data to gain information on the travel behaviour of their citizens. This approach requires that the accuracy and the reliability of the data and transformation processes are clearly characterized. To improve the reliability of reporting in such mobility studieswe systematically analyse the propagation of errors from low level operations to high level indicators, such as the modal split and travelled distances. Studies have shown that errors that occur in early stages of the data processing can have drastic consequences on the accuracy of later stages. 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 Aguirre, Angel and Gautama, Sidharta},
  booktitle    = {Proceedings of 19th FEA Research Symposium},
  keywords     = {data quality,geospatial data,crowdsensing,data processing,error propagation},
  language     = {eng},
  location     = {Ghent},
  title        = {Error sources in the analysis of crowdsourced spatial tracking data},
  year         = {2019},
}