A proportional odds model of human mobility and migration patterns. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Ma, Ting; Zhu, Rong; Wang, Jianghao; Zhao, Na; Pei, Tao; Du, Yunyan; Zhou, Chenghu; Chen, Jie
• Is Part Of: International journal of geographical information science. Volume 33:Issue 1(2019)
• Page Start: 81
• Page End: 98
• Publication Date: 2019-01-02
• Subjects: Human mobility and migration -- the proportion odds -- migration probability -- probability-based modelling; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2018.1514608
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: The modelling of human mobility and migration patterns has received much attention due to its substantial importance. Despite long-term efforts, we still lack a modelling framework that captures mobility patterns and further obtains a prospective view of movement trends with regards to diverse impacting factors. Here, we propose a proportional odds model of human mobility and migration (POM-HM) that takes a probabilistic approach to model human movements. Our model is based on the migration probability with a log-logistic distribution under the proportional odds assumption. Explanatory variables are introduced into the model by re-parameterizing the probability distribution function. The two resultant functions, namely, the migration strength and cumulative hazard, are used to estimate regional differences among travel fluxes and their tendencies. The performance of the POM-HM in terms of its validity and accuracy is examined and compared with the gravity model and the radiation model. The probability-based modelling framework enables us to investigate regional variations in migrant fluxes consequently further predict potential future patterns. In short, our modelling approach captures the probabilistic nature of human mobility and migration and furthers our understanding of both the spatiotemporal patterns of population movements and the impacts of various driving forces.