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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?

Bantis, T; Haworth, J; (2019) Non-Employment Activity Type Imputation from Points of Interest and Mobility Data at an Individual Level: How Accurate Can We Get? International Journal of Geo-Information , 8 (12) , Article 560. 10.3390/ijgi8120560. Green open access

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Abstract

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.

Type: Article
Title: Non-Employment Activity Type Imputation from Points of Interest and Mobility Data at an Individual Level: How Accurate Can We Get?
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/ijgi8120560
Publisher version: https://doi.org/10.3390/ijgi8120560
Language: English
Additional information: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
Keywords: activity type inference; dynamic Bayesian networks; Dirichlet/multinomial
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10087755
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