Error analysis and correction of spatialization of crop yield in China - Different variables scales, partitioning schemes and error correction methods

Spatialization of crop yield is beneficial to comprehensive analysis between interdisciplinary data. Multivariable linear regression models are often applied to spatialization of attribute data. The variables scales and the partitioning of China should be considered when the model is constructed. Different variables scales and partitioning schemes will inevitably results in different spatialization errors. Spatialization errors can be reduced by error correction methods. Different methods have different influence on the accuracy of crop yield spatialization. In this study, three variables scales were selected including prefectural scale, county scale and grid cell (1 km × 1 km). Five partitioning schemes (no partition of China, 7 regions of China, 9 regions of China, 10 regions of China, partitions of China by province) were considered. A total of 28 kinds of multivariable linear regression models were constructed with area of different types of farmland as independent variables, crop yields as dependent variables. Then, seven kinds of error correction methods were used to correct crop yield spatialization results. Three error evaluation indicators were selected to investigate the influence of different variables scales, partitioning schemes and error correction methods on the precision of spatialization results. The conclusions can be drawn as follows: (a) Nine models with intercept based on variables at regional scale could not be used to spatialize crop yield, while the others can be used for spatialization of crop yield. (b) The precision of the spatialization result based on the model without intercept is higher than that based on the model with intercept. (c) For models without intercept, precision of spatialization results increased first and then decreased with the refinement of partitioning scheme. (d) For models without intercept, the precision of spatialization results improved with scaling down of the variables scale from prefectural scale to county scale and grid scale. (e) Among the seven kinds of error correction methods, average correction method, weight coefficient correction methodⅡ and weight coefficient correction method III can't be used to correct initial spatialization results. (f) Proportional coefficient correction method, weight coefficient correction methodⅠ, weight coefficient correction method Ⅳ and weight coefficient correction method Ⅴ can be used to correct initial results of spatialization. (g) The precisions of corrected spatialization products based on error correction methods, which can improve the precision of initial spatialization products, are very closely. This research made up for the deficiency of spatial error analysis of crop yield, explored the relationship between different sample scales and partitioning schemes and spatial error, compared the pros and cons of different error correction methods. Meanwhile, it also provided valuable information for other types of social and economic statistical data.