Natural Scales in Geographical Patterns

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.

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• Clustering action potential spikes: Insights on the use of overfitted finite mixture models and Dirichlet process mixture models
• On Perfect Classification and Clustering for Gaussian Processes
• Improved Hierarchical Clustering on Massive Datasets with Broad Guarantees
• On Pairwise Clustering with Side Information
• Data-Driven Tree Transforms and Metrics
• Statistical Latent Space Approach for Mixed Data Modelling and Applications
• A Bayesian Mixture Model for Clustering on the Stiefel Manifold
• Stateless actor-critic for instance segmentation with high-level priors
• Weighted paths between partitions
• On the use of Wasserstein metric in topological clustering of distributional data
• A concave pairwise fusion approach to subgroup analysis
• Variable Selection for Clustering and Classification
• Socially Constrained Structural Learning for Groups Detection in Crowd
• Data privacy protection in microscopic image analysis for material data mining
• Statistical Inference using the Morse-Smale Complex
• Subgroup Analysis for Longitudinal data via Semiparametric Additive Mixed Effect Model
• Generative Adversarial Nets for Multiple Text Corpora
• Image patch analysis of sunspots and active regions. II. Clustering via matrix factorization
• Bayesian Semiparametric Longitudinal Inverse-Probit Mixed Models for Category Learning
• Novel Feature-Based Clustering of Micro-Panel Data (CluMP)