Acta Geographica Sinica ›› 2021, Vol. 76 ›› Issue (12): 2964-2977.doi: 10.11821/dlxb202112007

• Population and Urban Studies • Previous Articles     Next Articles

Analyzing the spatial mechanism of interprovincial migration in China under uncertainty

PU Yingxia1,2,3(), WU Zhenwei1, GE Ying4, KONG Fanhua1   

  1. 1. School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
    2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing 210023, China
    3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
    4. School of Earth Science and Engineering, Hohai University, Nanjing 211100, China
  • Received:2020-10-23 Revised:2021-05-20 Online:2021-12-25 Published:2022-02-25
  • Supported by:
    National Natural Science Foundation of China(41771417);National Natural Science Foundation of China(41771029);Priority Academic Program Development of Jiangsu Higher Education Institutions;Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application

Abstract:

Population migration process has an innate uncertainty with the increasing complexity of regional socioeconomic development. Bayesian model averaging (BMA) provides a feasible solution to the uncertainty of linear regression models. However, model uncertainties are seldom considered in spatial modeling. To reduce the uncertainties in migration modeling, this paper incorporates BMA approaches with spatial origin-destination (OD) models to quantify the spillover mechanism of interprovincial migration in China, 2010-2015. Specifically, we specified network dependence for migration flows and selected origin/destination's population size, gross domestic product (GDP), real wage, urbanization rate, the number of beds in health facilities per 1000 persons, the number of people over college level per 100000 persons in 2010 and migration stocks between 2005 and 2010 as well as railway travel time between provincial capitals in 2010 as explanatory variables. Among 2615 unique models based on 300000 samples using Markov chain Monte Carlo model combination (MC3), 58 models with posterior probability greater than 0.1% were chosen to estimate explanatory variables' origin effects, destination effects, and network effects. Some findings are as follows: (1) BMA model estimates are more robust and reliable. Compared with results from the single spatial origin-destination (OD) model, the widths of 90% credible interval of different explanatory variables are markedly reduced, indicating the degree of model uncertainty has been greatly decreased. (2) Regional population size, quality, and migration stocks have a significant influence on interprovincial migration processes. After model sampling, the inclusion probabilities of population size and GDP at origins, education level and migration stocks at destinations as well as distance variable are beyond 90%. (3) Network effects of most variables are statistically significant, accounting for more than 40% of their corresponding total effects. Moreover, the spillover effects of real wage, education level, and migration stocks are even greater than their corresponding origin and destination effects. (4) The impacts of most explanatory variables on interprovincial migration would be overestimated without considering uncertainties in modeling migration processes.

Key words: population migration, spatial OD models, Bayesian model averaging (BMA), posterior model probability, posterior probability of variable inclusion, network effects, China