ISPRS Int. J. Geo-Inf., Volume 8, Issue 12 (December 2019) – 68 articles
Cover Story (view full-size image): Human activity type inference is important for applications ranging from managing transportation demand to land use management. Unlabelled mobility data offer the opportunity to explore different approaches for this task; however, validation is out of reach due to lack of reference data. This research proposes a framework for urban activity type inference using a Dirichlet multinomial dynamic Bayesian network with an empirical Bayes prior that that can be applied to mobility data of low spatiotemporal resolution. Results were validated using foursquare data, providing evidence of activity type inference accuracy. 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.View this paper.
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