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Non-Employment Activity Type Imputation from Points of Interest and Mobility Data at an Individual Level: How Accurate Can We Get?

SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineer, University College London (UCL) Gower Street, London WC1E 6BT, UK
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ISPRS Int. J. Geo-Inf. 2019, 8(12), 560; https://doi.org/10.3390/ijgi8120560
Received: 4 August 2019 / Revised: 7 November 2019 / Accepted: 28 November 2019 / Published: 5 December 2019
Human activity type inference has long been the focus for applications ranging from managing transportation demand to monitoring changes in land use patterns. Today’s ever increasing volume of mobility data allow researchers to explore a wide range of methodological approaches for this task. Such data, however, lack reference observations that would allow the validation of methodological approaches. This research proposes a methodological framework for urban activity type inference using a Dirichlet multinomial dynamic Bayesian network with an empirical Bayes prior that can be applied to mobility data of low spatiotemporal resolution. The method was validated using open source Foursquare data under different isochrone configurations. The results provide evidence of the limits of activity detection accuracy using such data as determined by the Area Under Receiving Operating Curve (AUROC), log-loss, and accuracy metrics. At the same time, results demonstrate that a hierarchical modeling framework can provide some flexibility against the challenges related to the nature of unsupervised activity classification using trajectory variables and POIs as input. View Full-Text
Keywords: activity type inference; dynamic Bayesian networks; Dirichlet/multinomial activity type inference; dynamic Bayesian networks; Dirichlet/multinomial
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Bantis, T.; Haworth, J. Non-Employment Activity Type Imputation from Points of Interest and Mobility Data at an Individual Level: How Accurate Can We Get? ISPRS Int. J. Geo-Inf. 2019, 8, 560.

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