Modeling for spatial multilevel structural data

Suqin Min; Xiaoqun He

doi:10.1117/12.2013907

13 March 2013 Modeling for spatial multilevel structural data

Suqin Min, Xiaoqun He

Proceedings Volume 8783, Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing; 87830R (2013) https://doi.org/10.1117/12.2013907
Event: Fifth International Conference on Machine Vision (ICMV 12), 2012, Wuhan, China

Abstract

The traditional multilevel model assumed independence between groups. However, the datasets grouped by geographical units often has spatial dependence. The individual is influenced not only by its region but also by the adjacent regions, and level-2 residual distribution assumption of traditional multilevel model is violated. In order to deal with such spatial multilevel data, we introduce spatial statistics and spatial econometric models into multilevel model, and apply spatial parameters and adjacency matrix in traditional level-2 model to reflect the spatial autocorrelation. Spatial lag model express spatial effects. We build spatial multilevel model which consider both multilevel thinking and spatial correlation.

Citation Download Citation

Suqin Min and Xiaoqun He "Modeling for spatial multilevel structural data", Proc. SPIE 8783, Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing, 87830R (13 March 2013); https://doi.org/10.1117/12.2013907

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