Research by: Christian Alis, Erika Fille Legara & Christopher Monterola
The movement of individuals towards cities and other areas is critical in understanding the dynamics of people, institutions, and goods in cities and other urban areas. For a long time, the gravity model has been the primary model for modeling human mobility and migration. More recently, the radiation model for human migration has been introduced, which considers the search for better jobs as the primary motivation for movement. The model used population as the proxy for job availability and the attractiveness of a city to migrants. We find this assumption as limiting—people also move for reasons that do not necessarily include searching for better jobs.
Here, we introduce a model that generalizes the radiation model to include a set of urban attributes that will serve as attractors for migrants. In particular, we include amenities (offices, schools, leisure places, etc.) as features aside from population, thereby directly modeling how each amenity contributes to the migrant’s decision of moving and their choice of destination if ever they do. Using optimization and various machine learning procedures, we capture the weights of these urban-related parameters and their causal relation with migration behavior
We find that our generalized radiation model outperforms the state-of-the-art radiation model; in fact, the best-performing models do not even include population information. This suggests that the presence and diversity of amenities already contain the information that we get from the population.
The manuscript is a breakthrough contribution to Urban Science and Social Physics, especially in understanding the complex urban dynamics of developing nations where the motivation for the movement of people significantly goes beyond economic opportunities.
Keywords: radiation model, machine learning, urban mobility, human migration
To cite this article: Alis, C., Legara, E.F., & Monterola, C. (2021). Generalized radiation model for human migration. Nature Scientific Reports, 11, 22707. https://doi.org/10.1038/s41598-021-02109-1
To access this article: https://doi.org/10.1038/s41598-021-02109-1
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Nature Scientific Reports (NSR) is the 6th most-cited journal in the world, with more than 540,000 citations in 2020, and receives widespread attention in policy documents and the media. NSR is an open-access journal publishing original research from across all areas of the natural sciences, psychology, medicine, and engineering.
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Academic Journal Guide 2021
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|Scimago Journal & Country Rank||SJR213|
|Journal Citation Reports (Clarivate)||JCI2020: 0.80
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