Abstract
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.
Highlights
Understanding human movement patterns at local, national and international scales is critical in a range of fields, including transportation, logistics and epidemiology
How well do distinct data sources capture population-scale mobility? The choice of dataset determines the success of the statistical model derived from it, yet all too often the choice is only driven by data availability
We address the need for movement models capturing mobility flows across large geographic areas but representative across spatial scales
Summary
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. The models that have so far been proposed for human movement are best suited to specific spatial scales and types of movement. 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. The radiation model has been shown to outperform the gravity model on occasion, including predicting movement patterns within a state and across country scale[15] Both model types have been shown to inadequately describe population movement in some settings, i.e. low income countries[12]
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