A proportional odds model of human mobility and migration patterns

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.

[1]  Wen-Xu Wang,et al.  Universal predictability of mobility patterns in cities , 2013, Journal of The Royal Society Interface.

[2]  E. Ravenstein.  The Laws of Migration , 1885, Encyclopedia of Gerontology and Population Aging.

[3]  S. J. Richards,et al.  A handbook of parametric survival models for actuarial use , 2012 .

[4]  David L. Smith,et al.  Quantifying the Impact of Human Mobility on Malaria , 2012, Science.

[5]  Caroline O. Buckee,et al.  The Use of Census Migration Data to Approximate Human Movement Patterns across Temporal Scales , 2013, PloS one.

[6]  G. Zipf.  The P 1 P 2 D Hypothesis: On the Intercity Movement of Persons , 1946 .

[7]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[8]  T. Geisel,et al.  The scaling laws of human travel , 2006, Nature.

[9]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[10]  K. Chan,et al.  Hukou and non-hukou migrations in China: comparisons and contrasts. , 1999, International journal of population geography : IJPG.

[11]  Marta C. González,et al.  A universal model for mobility and migration patterns , 2011, Nature.

[12]  F. Zhong,et al.  Economic Growth, Demographic Change and Rural-Urban Migration in China , 2013 .

[13]  A. Tatem,et al.  Dynamic population mapping using mobile phone data , 2014, Proceedings of the National Academy of Sciences.

[14]  Robert E. Page,et al.  Complex social behaviour derived from maternal reproductive traits , 2006, Nature.

[15]  Marc Barthelemy,et al.  Spatial Networks , 2010, Encyclopedia of Social Network Analysis and Mining.

[16]  G. Madey,et al.  Uncovering individual and collective human dynamics from mobile phone records , 2007, 0710.2939.

[17]  Yuan Tian,et al.  Understanding intra-urban trip patterns from taxi trajectory data , 2012, Journal of Geographical Systems.

[18]  Yong Gao,et al.  Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data , 2013, PloS one.

[19]  Marta C. González,et al.  Coupling human mobility and social ties , 2015, Journal of The Royal Society Interface.

[20]  S. Stouffer.  Intervening opportunities: a theory relating mobility and distance , 1940 .

[21]  Qian Li,et al.  A collective human mobility analysis method based on data usage detail records , 2017, Int. J. Geogr. Inf. Sci..

[22]  J. LeSage,et al.  Spatial Econometric Modeling of Origin-Destination Flows , 2008 .

[23]  Margaret Martonosi,et al.  ON CELLULAR , 2022 .

[24]  M. Batty,et al.  Gravity versus radiation models: on the importance of scale and heterogeneity in commuting flows. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  Petter Holme,et al.  Predictability of population displacement after the 2010 Haiti earthquake , 2012, Proceedings of the National Academy of Sciences.

[26]  Yu Liu,et al.  A Generalized Radiation Model for Human Mobility: Spatial Scale, Searching Direction and Trip Constraint , 2015, PloS one.

[27]  Alessandro Vespignani,et al.  Multiscale mobility networks and the spatial spreading of infectious diseases , 2009, Proceedings of the National Academy of Sciences.

[28]  S. Ellner,et al.  Human mobility patterns predict divergent epidemic dynamics among cities , 2013, Proceedings of the Royal Society B: Biological Sciences.

[29]  Cecilia Mascolo,et al.  A Tale of Many Cities: Universal Patterns in Human Urban Mobility , 2011, PloS one.

[30]  Alan M. MacEachren,et al.  Geo-Located Tweets. Enhancing Mobility Maps and Capturing Cross-Border Movement , 2015, PloS one.

[31]  T. Pei,et al.  Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China's cities , 2012 .

[32]  Chenghu Zhou,et al.  A new insight into land use classification based on aggregated mobile phone data , 2013, Int. J. Geogr. Inf. Sci..

[33]  Sarah Cook,et al.  Internal migration and health in China , 2008, The Lancet.