Abstract
Fundamental laws governing human mobility have many important applications such as forecasting and controlling epidemics or optimizing transportation systems. These mobility patterns, studied in the context of out of home activity during travel or social interactions with observations recorded from cell phone use or diffusion of money, suggest that in extra-personal space humans follow a high degree of temporal and spatial regularity – most often in the form of time-independent universal scaling laws. Here we show that mobility patterns of older individuals in their home also show a high degree of predictability and regularity, although in a different way than has been reported for out-of-home mobility. Studying a data set of almost 15 million observations from 19 adults spanning up to 5 years of unobtrusive longitudinal home activity monitoring, we find that in-home mobility is not well represented by a universal scaling law, but that significant structure (predictability and regularity) is uncovered when explicitly accounting for contextual data in a model of in-home mobility. These results suggest that human mobility in personal space is highly stereotyped, and that monitoring discontinuities in routine room-level mobility patterns may provide an opportunity to predict individual human health and functional status or detect adverse events and trends.
Highlights
Many factors influence human mobility, spanning the continuum from regular and predictable commitments to unforeseen circumstances while encapsulating individuals’ preferences, wants, needs, and contextual effects
We find that while a power law is not a plausible representation for the observed in-home mobility data, by explicitly including context in a model of human mobility we obtain a high level of predictability and uncover structural regularity not previously reported
These results suggest that inhome mobility is highly stereotyped, albeit in a different way, which may have applications to predicting individual human health and functional status[25,26] by detecting adverse events or trends[7] and in conducting more meaningful clinical trials[27,28]
Summary
Many factors influence human mobility, spanning the continuum from regular and predictable commitments (e.g., commuting for work or taking a child to school) to unforeseen circumstances (e.g., travelling to help a sick relative or pausing to fix a flat tire) while encapsulating individuals’ preferences, wants, needs, and contextual effects (e.g., weather conditions or current health status). Despite the seemingly diverse array of reasons for which individuals move around[1,2,3,4,5,6], a large body of work has found significant regularity and predictability in human mobility patterns, primarily in the form of scaling properties and power laws[2,3,5,7,8,9,10,11] using location data collected predominantly from cell phones[7,12,13,14].
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