Abstract
The idea of a hierarchical spatial organization of society lies at the core of seminal theories in human geography that have strongly influenced our understanding of social organization. Along the same line, the recent availability of large-scale human mobility and communication data has offered novel quantitative insights hinting at a strong geographical confinement of human interactions within neighboring regions, extending to local levels within countries. However, models of human interaction largely ignore this effect. Here, we analyze several country-wide networks of telephone calls - both, mobile and landline - and in either case uncover a systematic decrease of communication induced by borders which we identify as the missing variable in state-of-the-art models. Using this empirical evidence, we propose an alternative modeling framework that naturally stylizes the damping effect of borders. We show that this new notion substantially improves the predictive power of widely used interaction models. This increases our ability to understand, model and predict social activities and to plan the development of infrastructures across multiple scales.
Highlights
Globalization has led us to believe that our world is becoming borderless and deterritorialized
In order to quantify the hypothesized effect of hierarchical organization on human interactions, we first define consistent nested regional partitions by recursively applying the recently developed community detection algorithm “Combo”[13] to country-wide phone call networks from the United Kingdom, Portugal, France, Ivory Coast and an anonymous country, Country X (Methods)
As previously noted in refs 5, 6, these results may come as a surprise, as the modularity approach of the Combo algorithm has no spatial constraint nor does it impose any restriction on the number of communities
Summary
Globalization has led us to believe that our world is becoming borderless and deterritorialized. Cores relating each to a densely populated area of Great Britain) These results, offer only a partial view, because human behavior, like communication and mobility, is nurtured by high-scale interactions and is increasingly becoming multi-scalar. This co-existence of short-range and long-range interactions[10] raises the question whether high-level community detection offers a sufficient view for the development of models of human interaction. We analyze the performance of state-of-the-art models that predict human interactions, revealing systematic biases in the way these models fit reality These biases include the inability to capture the impact of borders and to reproduce important properties of the hierarchical structure of the human society. Our model clearly oversimplifies reality, it outperforms previous, more complex, models quantitatively, emphasizing the crucial nature of the impact of borders on human interactions
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