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Modeling interregional commodity flows with incorporating network autocorrelation in spatial interaction models: An application of the US interstate commodity flows
Highlights ► Extending spatial interaction models by incorporating network autocorrelation. ► Examining the effects of spatial structure as well as network autocorrelation. ► Empirically examining network autocorrelation in US interstate commodity flows.
Abstract Spatial interaction models are frequently used to predict and explain interregional commodity flows. Studies suggest that the effects of spatial structure significantly influence spatial interaction models, often resulting in model misspecification. Competing destinations and intervening opportunities have been used to mitigate this issue. Some recent studies also show that the effects of spatial structure can be successfully modeled by incorporating network autocorrelation among flow data. The purpose of this paper is to investigate the existence of network autocorrelation among commodity origin–destination flow data and its effect on model estimation in spatial interaction models. This approach is demonstrated using commodity origin–destination flow data for 111 regions of the United States from the 2002 Commodity Flow Survey. The results empirically show how network autocorrelation affects modeling interregional flows and can be successfully captured in spatial autoregressive model specifications.
Modeling interregional commodity flows with incorporating network autocorrelation in spatial interaction models: An application of the US interstate commodity flows
Highlights ► Extending spatial interaction models by incorporating network autocorrelation. ► Examining the effects of spatial structure as well as network autocorrelation. ► Empirically examining network autocorrelation in US interstate commodity flows.
Abstract Spatial interaction models are frequently used to predict and explain interregional commodity flows. Studies suggest that the effects of spatial structure significantly influence spatial interaction models, often resulting in model misspecification. Competing destinations and intervening opportunities have been used to mitigate this issue. Some recent studies also show that the effects of spatial structure can be successfully modeled by incorporating network autocorrelation among flow data. The purpose of this paper is to investigate the existence of network autocorrelation among commodity origin–destination flow data and its effect on model estimation in spatial interaction models. This approach is demonstrated using commodity origin–destination flow data for 111 regions of the United States from the 2002 Commodity Flow Survey. The results empirically show how network autocorrelation affects modeling interregional flows and can be successfully captured in spatial autoregressive model specifications.
Modeling interregional commodity flows with incorporating network autocorrelation in spatial interaction models: An application of the US interstate commodity flows
Chun, Yongwan (author) / Kim, Hyun (author) / Kim, Changjoo (author)
Computers, Environments and Urban Systems ; 36 ; 583-591
2012-04-22
9 pages
Article (Journal)
Electronic Resource
English
Forecasting interregional commodity flows using artificial neural networks: an evaluation
| Taylor & Francis Verlag | 2004
Forecasting interregional commodity flows using artificial neural networks: an evaluation
| Online Contents | 2004