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Estimating migration flows and forecasting future trends is important, both to understand the causes and effects of migration and to implement policies directed at supplying particular services. Over the years, less research has been done on modeling migration flows than the efforts allocated to...
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Format: | Theses and Dissertations |
Language: | other |
Published: |
เชียงใหม่ : บัณฑิตวิทยาลัย มหาวิทยาลัยเชียงใหม่
2020
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Online Access: | http://cmuir.cmu.ac.th/jspui/handle/6653943832/69485 |
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Institution: | Chiang Mai University |
Language: | other |
Summary: | Estimating migration flows and forecasting future trends is important, both to understand the
causes and effects of migration and to implement policies directed at supplying particular services.
Over the years, less research has been done on modeling migration flows than the efforts allocated
to modeling other flow types, for instance, commute. Limited data availability has been one of the
major impediments for empirical analyses and for theoretical advances in the modeling of migration
flows. As a migration trip takes place much less frequent compared to the commute, it requires a
longitudinal set of data for the analysis. This study makes use a massive mobile phone network data
to infer migration trips and their distribution. Insightful characteristics of the inferred migration trips
are revealed, such as intra/inter-district migration flows, migration distance distribution, and origindestination
(O-D) movements. For migration trip distribution modelling, log-linear model,
traditional gravity model, and recently introduced radiation model were examined with different
approaches taken in defining parameters for each model. As the result, the gravity and log-linear
models with a direct distance (displacement) used as its travel cost and district centroids used as the
reference points perform best among the other alternative models. A radiation model that considers
district population performs best among the radiation models, but worse than that of the gravity and
log-linear models. |
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