Spatial linear combination interpretation model based on spatial drift and its empirical study

The accuracy of spatial interpretation is close relation to the selection of spatial model. Each model has its own advantage or disadvantage. A more accurate spatial interpretation model can be obtained by a linear combination of some models. In this study, first-order spatial autoregressive interpretation (SARI(1)) model, kriging algorithm interpretation (KAI) model and back-propagation neural network interpretation (BPNNI) model are established by using cross-section data or time series data. A spatial linear combination interpretation (SLCI) model is constructed by the results of these models. The weights of SLCI model obtained by spatial drift. An empirical research is carried out with interpretation some areaspsila GDP per capita in Fujian, 2003. It is found that the best one is the SLCI model.