Thoughts on ” Scaling Behavior of Human Mobility Distributions”

This paper presented an empirical study of how temporal and spatial scale impacts the distribution of mobility data. The main finding is not surprising – a different spatial and temporal scale of analysis leads to a different distribution of data. Once again we saw the importance of scale in the analysis of the spatial datasets.

What interests me are finding 3 and 5. Finding 3 states that ordering between metrics over datasets is generally preserved under resampling, which implicates that the comparison across the datasets can be made regardless of the spatial and temporal resolution. This reminds me of the reading of spatial data quality. Though it is critical about the effects of scale, it is also important to bear in mind about the “use”. In the case of comparing human mobility across different datasets, the scale does not seem to matter anymore.

Find 5 concludes that the sensitivity to resampling can itself be a metric. I think this is a good point but I was having some difficulties to grasp what the authors want to express in the subsequent argument of “difference in sensitivity indicates that information about population mobility is encoded in the scaling behavior”. I think they could have explained this better. To my understanding, the difference in sensitivity to resampling is nothing more than the difference in the heterogeneity of the datasets.

Another point I want to make is that although the analysis is performed on mobility datasets, it seems to me that the most conclusions they made can be generalized to all kinds of datasets. I’m not sure what is special about the mobility data here in their analysis.

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