Spatial structure effects in spatial interaction model: a Geographically Weighted Regression (GWR) approach
Abstract
En
The development of local forms of spatial analysis has been the subject of intense research over last decade. In this paper we propose a local calibration procedure for handling varying parameter estimates of an origin-constrained spatial interaction model. In this context, the estimates of local parameters depends both on origins and destinations and a four dimensional space is involved. A suitable estimation of local parameters can be obtained by the maximisation of a weighted maximum likelihood function, exploiting the same principle of geographical weighted regression (GWR) approach.
The development of local forms of spatial analysis has been the subject of intense research over last decade. In this paper we propose a local calibration procedure for handling varying parameter estimates of an origin-constrained spatial interaction model. In this context, the estimates of local parameters depends both on origins and destinations and a four dimensional space is involved. A suitable estimation of local parameters can be obtained by the maximisation of a weighted maximum likelihood function, exploiting the same principle of geographical weighted regression (GWR) approach.
DOI Code:
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Keywords:
spatial interaction models; geographically weighted regression approach; spatial structure effects; migration flows; local estimates
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