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Error sources in the analysis of crowdsourced spatial tracking data

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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. 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:

Chicago
Van Gheluwe, Casper, Angel Lopez Aguirre, and Sidharta Gautama. 2019. “Error Sources in the Analysis of Crowdsourced Spatial Tracking Data.” In 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE.
APA
Van Gheluwe, C., Lopez Aguirre, A., & Gautama, S. (2019). Error sources in the analysis of crowdsourced spatial tracking data. 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom). Presented at the 4th IEEE International Workshop on Pervasive Context-Aware Smart Cities and Intelligent Transportation Systems (PerAwareCity), IEEE.
Vancouver
1.
Van Gheluwe C, Lopez Aguirre A, Gautama S. Error sources in the analysis of crowdsourced spatial tracking data. 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE; 2019.
MLA
Van Gheluwe, Casper, Angel Lopez Aguirre, and Sidharta Gautama. “Error Sources in the Analysis of Crowdsourced Spatial Tracking Data.” 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, 2019. Print.
@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 Aguirre, Angel and Gautama, Sidharta},
  booktitle    = {2019 IEEE International Conference on Pervasive Computing and Communications (PerCom)},
  keyword      = {data quality,geospatial data,crowdsensing,data processing,error propagation},
  language     = {eng},
  location     = {Kyoto},
  publisher    = {IEEE},
  title        = {Error sources in the analysis of crowdsourced spatial tracking data},
  year         = {2019},
}