Abstract
In this article, I develop and implement the multiscale geographically weighted negative binomial (MGWNB) model, extending the spatially weighted interaction models by integrating a multiscale framework. This model effectively tackles the multiscale nonstationarity and overdispersion issues found in spatial interaction models. By comparing it with multiscale geographically weighted Poisson regression using simulated data, I demonstrate its superior performance in several aspects, including its capability to estimate the scale of processes, its effectiveness in capturing the spatial heterogeneity, and its ability to produce a better goodness of fit. The application of MGWNB in interprovincial population migration in China, using 2020 Chinese census data, also demonstrates its effectiveness and efficiency, revealing strong multiscale spatial heterogeneity in the migration patterns.
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