13 March 2013 Modeling for spatial multilevel structural data
Author Affiliations +
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.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Suqin Min, Suqin Min, Xiaoqun He, 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); doi: 10.1117/12.2013907; https://doi.org/10.1117/12.2013907
PROCEEDINGS
6 PAGES


SHARE
Back to Top