Predicting migration system dynamics with conditional and posterior probabilities

Clio Maria Andris, Samuel Halverson, Frank Hardisty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Traditional models of migration assume that migrants move to places of greatest economic incentive, and are more likely to move when current economic conditions push migrants from their origin. Although prospective income at a destination has been a major determining factor for migration in preexisting migration models, and distance between origin and destination is also a major consideration, we take a new approach with a model that reflects migration chaining, where migrants to a city B send information back to their origin city A, and interest other members of A to migrate to B. We isolate the social factors of place-pair synergies through components from Bayes' Law: conditional probability and posterior probability of unique origin/destination migrant volume, and a system-wide probability of unique O/D transfer. These allow us to model social space as well as physical space, rather than physical space alone. We test these variables' power for predicting future migration against four other predictive models: the traditional gravity model, transit data, airline and trip data, and linear trends. We use a case study of U.S. Migration flows in a system of major cities, given annual data from 1996-2004 to predict city-to-city flows annually for 2005-2008, and find that conditional and posterior probabilities outperform system-wide probabilities, gravity, transit and linear forecast models. These probabilities also exhibit a surprising level of steady-state stationarity, and therefore are a promising avenue for more accurately modelling future migration flows.

Original languageEnglish (US)
Title of host publicationICSDM 2011 - Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services
Pages192-197
Number of pages6
DOIs
StatePublished - Sep 1 2011
Event2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, ICSDM 2011 - In Conjunction with 8th Beijing International Workshop on Geographical Information Science, BJ-IWGIS 2011 - Fuzhou, China
Duration: Jun 29 2011Jul 1 2011

Publication series

NameICSDM 2011 - Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services

Other

Other2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, ICSDM 2011 - In Conjunction with 8th Beijing International Workshop on Geographical Information Science, BJ-IWGIS 2011
CountryChina
CityFuzhou
Period6/29/117/1/11

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software

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    Andris, C. M., Halverson, S., & Hardisty, F. (2011). Predicting migration system dynamics with conditional and posterior probabilities. In ICSDM 2011 - Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (pp. 192-197). [5969030] (ICSDM 2011 - Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services). https://doi.org/10.1109/ICSDM.2011.5969030