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Vector-to-raster conversion is a process accompanied with errors. The errors are classified into predicted errors before rasterization and actual errors after that. Accurate prediction of the errors is beneficial to developing reasonable rasterization technical schemes and to making products of high quality. Analyzing and establishing a quantitative relationship between the error and its affecting factors is the key to error prediction. In this study, land cover data of China at a scale of 1:250 000 were taken as an example for analyzing the relationship between rasterization errors and the density of arc length (DA), the density of polygon (DP) and the size of grid cells (SG). Significant correlations were found between the errors and DA, DP and SG. The correlation coefficient (R ²) of a model established based on samples collected in a small region (Beijing) reaches 0.95, and the value of R ² is equal to 0.91 while the model was validated with samples from the whole nation. On the other hand, the R ² of a model established based on nationwide samples reaches 0.96, and R ² is equal to 0.91 while it was validated with the samples in Beijing. These models depict well the relationships between rasterization errors and their affecting factors (DA, DP and SG). The analyzing method established in this study can be applied to effectively predicting rasterization errors in other cases as well.
