Human migration patterns in large scale spatial with the resume data

TL;DR

This study analyzes large-scale human migration patterns using resume data, revealing asymmetrical flow influenced by city attractiveness, with economic level being a key factor, and showing that long-term migration is less sensitive to distance than short-term travel.

Contribution

It introduces a novel approach to studying long-term migration using resume data and applies the gravity model to reveal key factors influencing migration patterns.

Findings

Migration flow asymmetry is caused by city attractiveness differences.

Long-term migration is less sensitive to distance than short-term travel.

Economic level significantly influences migration patterns.

Abstract

Researches on the human mobility have made great progress in many aspects, but the long-term and long-distance migration behavior is lack of in-depth and extensive research because of the difficult in accessing to household data. In this paper, we use the resume data to discover the human migration behavior on the large scale scope. It is found that the asymmetry in the flow structure which reflects the influence of population competition is caused by the difference of attractiveness among cities. This flow structure can be approximately described by the gravity model of spatial economics. Besides, the value of scaling exponent of distance function in the gravity model is less than the value of short-term travel behavior. It means that, compared with the short-term travel behavior, the long-term human migration behavior is less sensitive. Moreover, the scaling coefficients of each variable in the gravity model are investigated. The result shows that the economic level is a mainly factor on the migration.