Reflecting on “Scaling Behavior of Human Mobility Distributions”

Analyzing big data is an obstacle across GIS, and movement is no exception. Cutting out potentially unnecessary components of the data in order to reduce the dataset  is one way of addressing this challenge. In Paul et al.’s piece they look at how much cutting down on datasets’ time windows may affect the end distribution.

Specifically, they examine the effects of changing the spatio-temporal scale of five different movement datasets, revealing which metrics are best to compare human relationships to movement across datasets. The findings of the study, which examines GPS data from undergraduate students, graduate students, schoolchildren, and working people, reveal that changing temporal sampling periods does affect the distributions across datasets, but the extent of this change is reliant on the dataset.

After reading this piece, I would like to understand more about how researchers studying movement address privacy. I’m sure having enormous datasets of anonymized data addresses part of this issue; however, I’m sure different government agencies, organizations, corporations, etc. collecting this data have different standards regarding the importance of privacy. How strictly enforced are data privacy laws (looking at movement data specifically)? 

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