Scaling Behavior of Human Mobility Distributions (Paul et al., 2016)

This paper characterizes human mobility patterns at different spatiotemporal resolutions using high-resolution data and finds that some aggregate distributions have scaling behaviors. This paper reaffirms that scale is a central tenet of GIScience.

First, the authors mentioned that varying resolution impacts datasets through the underlying behaviors of the individuals and the data collection context. Indeed, movement is often driven by the characteristics of the surrounding environment and the nature of space that the object is moving through. However, besides of spatial scale and granularity of movement, I would argue that this research should also take into account the temporal scale, such as the sequential structure of trajectories. Also, it is important to note that trajectory data is uncertain, and this can negatively impact the accuracy of algorithms used to obtain the movement patterns of objects. One of the sources of trajectory uncertainty is the error inherent to GPS measurements. Those datasets were collected using different GPS devices, which may make it difficult to assess and compare the internal quality of different datasets.

Because this paper focuses on the influence of scale, I am looking forward to knowing more methodologies applied in movement research, such as modeling and visualizing movement. Further, the integration of mobility data may lead to ethical challenges because environmental and other contextual data can reveal personal information beyond only location and time. Note that attaching the devices may affect animals’ behavior and perhaps the survival of the animal.

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