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Spatial Combination Interpolation Model Based on Panel Data and Its Empirical Study

Jiansheng Gan

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Abstract

To discuss the spatial interpolation based on panel data with spatial autocorrelation, the first-order spatial autoregressive interpolation model and the Kriging algorithm interpolation model are established from the perspective of the cross-sectional data. Genetic algorithm back-propagation neural network interpolation model is established from the perspective of the time-series data. A spatial combination interpolation model is established by the results of these models. The weights of the combination model is calculated by a new method of spatial drift. An empirical study is carried out with interpolation some areaspsila GDP per capita in Fujian 2007, China. The result shows that the most effective one is the spatial combination interpolation model.

About this research paper

What this paper is about

To discuss the spatial interpolation based on panel data with spatial autocorrelation, the first-order spatial autoregressive interpolation model and the Kriging algorithm interpolation model are established from the perspective of the cross-sectional data. Genetic algorithm back-propagation neural network interpolation model is established from the perspective of the time-series data. A spatial combination interpolation model is established by the results of these models. The weights of the combination model is calculated by a new method of spatial drift. An empirical study is carried out with interpolation some areaspsila GDP per capita in Fujian 2007, China. The result shows that the most effective one is the spatial combination interpolation model.

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Available abstract

To discuss the spatial interpolation based on panel data with spatial autocorrelation, the first-order spatial autoregressive interpolation model and the Kriging algorithm interpolation model are established from the perspective of the cross-sectional data. Genetic algorithm back-propagation neural network interpolation model is established from the perspective of the time-series data. A spatial combination interpolation model is established by the results of these models. The weights of the combination model is calculated by a new method of spatial drift. An empirical study is carried out with interpolation some areaspsila GDP per capita in Fujian 2007, China. The result shows that the most effective one is the spatial combination interpolation model.

Key concepts: Multivariate interpolation, Interpolation (computer graphics), Bilinear interpolation, Nearest-neighbor interpolation, Autoregressive model, Kriging, Spatial analysis, Data modeling

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