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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?

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      ISPRS International Journal of Geo-Information
      MDPI AG

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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.

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          Most cited references33

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          Trajectory Data Mining

          Yu Zheng (2015)
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            Accessibility, mobility and transport-related social exclusion

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              The promises of big data and small data for travel behavior (aka human mobility) analysis

              The last decade has witnessed very active development in two broad, but separate fields, both involving understanding and modeling of how individuals move in time and space (hereafter called “travel behavior analysis” or “human mobility analysis”). One field comprises transportation researchers who have been working in the field for decades and the other involves new comers from a wide range of disciplines, but primarily computer scientists and physicists. Researchers in these two fields work with different datasets, apply different methodologies, and answer different but overlapping questions. It is our view that there is much, hidden synergy between the two fields that needs to be brought out. It is thus the purpose of this paper to introduce datasets, concepts, knowledge and methods used in these two fields, and most importantly raise cross-discipline ideas for conversations and collaborations between the two. It is our hope that this paper will stimulate many future cross-cutting studies that involve researchers from both fields.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                ISPRS International Journal of Geo-Information
                IJGI
                MDPI AG
                2220-9964
                December 2019
                December 05 2019
                : 8
                : 12
                : 560
                Article
                10.3390/ijgi8120560
                9b32d58d-cab8-4553-af92-bc542bd047a8
                © 2019

                https://creativecommons.org/licenses/by/4.0/

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