Abstract
Human mobility is known to be distributed across several orders of magnitude of physical distances, which makes it generally difficult to endogenously find or define typical and meaningful scales. Relevant analyses, from movements to geographical partitions, seem to be relative to some ad-hoc scale, or no scale at all. Relying on geotagged data collected from photo-sharing social media, we apply community detection to movement networks constrained by increasing percentiles of the distance distribution. Using a simple parameter-free discontinuity detection algorithm, we discover clear phase transitions in the community partition space. The detection of these phases constitutes the first objective method of characterising endogenous, natural scales of human movement. Our study covers nine regions, ranging from cities to countries of various sizes and a transnational area. For all regions, the number of natural scales is remarkably low (2 or 3). Further, our results hint at scale-related behaviours rather than scale-related users. The partitions of the natural scales allow us to draw discrete multi-scale geographical boundaries, potentially capable of providing key insights in fields such as epidemiology or cultural contagion where the introduction of spatial boundaries is pivotal.
Highlights
Geographical scaling has been at the core of a wealth of studies of human mobility
We demonstrate that it is possible to endogenously uncover a small number of meaningful description scale ranges from apparently scale-free raw data
The breakpoints automatically found by our algorithm mostly match the visual intuition: in Fig. 1, we see that these phase transitions are quite obvious by visual inspection
Summary
Geographical scaling has been at the core of a wealth of studies of human mobility. On one hand, physical distances between connected individuals or between related places have repeatedly been shown to hardly obey any distinctive scale, let alone exhibit distinct phases. Behavioural traces spanning several orders of magnitudes are typically aggregated independently of the physical scale they correspond to; geographical areas or patterns are uncovered by community detection algorithms; a final level of description is chosen according to some criterion These methods generally produce dendrograms defining an embedded series of geographical partitions, where lower-level partitions include higher-level ones in a continuum of increasingly coarse description scales. Rather than using all the data and discussing the optimality of a high-level observation scale a posteriori, we work the other way around by a priori relying on link scale to blindly define a series of scale-dependent networks These networks are based on an increasing link distance threshold and configure an increasing movement radius. We liken this implicit finding to the explicit, man-made hierarchies which can be found in more traditional top-down approaches relying on discrete ontologies featuring a small number of embedded spatial scales, such as administrative divisions (e.g. NUTS)[16]
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