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Ensemble model for estimating continental-scale patterns of human movement: a case study of Australia

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journal contribution
posted on 2021-05-12, 00:24 authored by Karen McCullochKaren McCulloch, N Golding, J McVernon, S Goodwin, M Tomko
Understanding human movement patterns at local, national and international scales is critical in a range of fields, including transportation, logistics and epidemiology. Data on human movement is increasingly available, and when combined with statistical models, enables predictions of movement patterns across broad regions. Movement characteristics, however, strongly depend on the scale and type of movement captured for a given study. The models that have so far been proposed for human movement are best suited to specific spatial scales and types of movement. Selecting both the scale of data collection, and the appropriate model for the data remains a key challenge in predicting human movements. We used two different data sources on human movement in Australia, at different spatial scales, to train a range of statistical movement models and evaluate their ability to predict movement patterns for each data type and scale. Whilst the five commonly-used movement models we evaluated varied markedly between datasets in their predictive ability, we show that an ensemble modelling approach that combines the predictions of these models consistently outperformed all individual models against hold-out data.


This work has been supported by FrontierSI (formerly CRC for Spatial Information), a not-for-profit company that exists to deliver major benefits to governments, industry and the community in Australia and New Zealand through the application of spatial information. The authors would also like to thank Sygic for allowing access to anonymized data for the purposes of this research.


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Scientific Reports





Article Number

ARTN 4806







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