A universal model for mobility and migration patterns

TL;DR

This paper introduces a parameter-free stochastic radiation model that accurately predicts diverse mobility and migration patterns using only population distribution data, improving upon traditional gravity models.

Contribution

The paper presents a novel, parameter-free radiation model for mobility prediction, addressing limitations of the gravity law and enabling applications without prior mobility measurements.

Findings

Accurately predicts migration and communication patterns across various phenomena.

Outperforms traditional gravity models in predictive accuracy.

Applicable in regions lacking mobility data.

Abstract

Introduced in its contemporary form by George Kingsley Zipf in 1946, but with roots that go back to the work of Gaspard Monge in the 18th century, the gravity law is the prevailing framework to predict population movement, cargo shipping volume, inter-city phone calls, as well as bilateral trade flows between nations. Despite its widespread use, it relies on adjustable parameters that vary from region to region and suffers from known analytic inconsistencies. Here we introduce a stochastic process capturing local mobility decisions that helps us analytically derive commuting and mobility fluxes that require as input only information on the population distribution. The resulting radiation model predicts mobility patterns in good agreement with mobility and transport patterns observed in a wide range of phenomena, from long-term migration patterns to communication volume between different regions. Given its parameter-free nature, the model can be applied in areas where we lack previous mobility measurements, significantly improving the predictive accuracy of most of phenomena affected by mobility and transport processes.