Natural Scales in Geographical Patterns

TLDR

Human mobility spans many orders of magnitude, making it difficult to define typical scales and forcing analyses to rely on ad‑hoc or no scale. The study aims to identify natural scales of human movement by applying community detection to geotagged movement networks. Using photo‑sharing data, the authors construct movement networks at increasing distance percentiles and apply community detection. The analysis revealed distinct phase transitions yielding only two or three natural movement scales per region, providing an objective, parameter‑free method to delineate discrete multi‑scale geographical boundaries with implications for epidemiology and cultural contagion.

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.