Spatial interpolation includes point interpolation and areal interpolation. The point interpolation is mainly used to deal with natural geography data and the areal interpolation is usually used to solve the interpolation problem of social economic statistic data. However, social economic statistic data are often collected by areas, which cannot represent their real spatial distribution. A spatial distribution model of social economic statistic data is to be established to conform to their real spatial distribution. Using the areal weighting interpolation and inverse distance weighted averaging, a spatial distribution model of GDP is established in this paper.