PreprintCC BY 4.0arXiv
Migration Flows: Population Scaling and Heavy Tails
Abstract
We show how migration between counties in different cities can explain both the population scaling of mean intercity flows and heavy-tailed intercity net-flow distributions. Density-dependent departure and arrival activities determine how mean flows scale with the populations of the origin and destination cities. We assume Gaussian county-pair net flows with standard deviations proportional to their mean directional flows. At a fixed correlation between county-pair net flows, their sum is Gaussian. Averaging this conditional Gaussian density over the correlation distribution can produce an intermediate power law with a Gaussian cutoff. We also present a stochastic model that generates the variance distribution used in this mixture.
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BibTeX
@misc{kim2026migration,
title = {{Migration Flows: Population Scaling and Heavy Tails}},
author = {Cook Hyun Kim and Sangjoon Park and Seoyoung Jin and Jaiyong Lee},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.00192},
eprint = {2610.00192},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.00192},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Migration Flows: Population Scaling and Heavy Tails
AU - Cook Hyun Kim
AU - Sangjoon Park
AU - Seoyoung Jin
AU - Jaiyong Lee
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.00192
UR - https://arxiv.org/abs/2610.00192
AB - We show how migration between counties in different cities can explain both the population scaling of mean intercity flows and heavy-tailed intercity net-flow distributions. Density-dependent departure and arrival activities determine how mean flows scale with the populations of the origin and destination cities. We assume Gaussian county-pair net flows with standard deviations proportional to their mean directional flows. At a fixed correlation between county-pair net flows, their sum is Gaussian. Averaging this conditional Gaussian density over the correlation distribution can produce an intermediate power law with a Gaussian cutoff. We also present a stochastic model that generates the variance distribution used in this mixture.
ER -