  We evaluated our proposed methods by comparing the mean absolute errors and running times of interpolated grid cells to those of 7 common baselines . Our analysis involved detailed experiments on two synthetic and two real-world datasets over 44 total experimental conditions. Experimental results show the competitive advantage of MRP interpolation on real-world data, as the average performance of SD-MRP on real-world data under all experimental conditionswas ranked significantly higher than that of all other methods, followed by WP-MRP.
<meaning-changed>   We evaluated our proposed methods by comparing the mean absolute errors and running times of interpolated grid cells to those of 7 common baselines . Our analysis involved detailed experiments on two synthetic and two real-world datasets over 44 total experimental conditions. Experimental results show the competitive advantage of MRP interpolation on real-world data, as the average performance of SD-MRP on real-world data under all experimental conditionswas ranked significantly higher than that of all other methods, followed by WP-MRP.
 We evaluated our proposed methods by comparing the mean absolute errors and running times of interpolated grid cells to those of 7 common baselines , selected from models based on spatial autocorrelation, (spatial) regression, and deep learning. We performed detailed evaluations on two publicly available datasets (local GDP values, and COVID-19 patient trajectory data). The results from these experiments clearly show the competitive advantage of MRP interpolation on real-world data, as the average performance of SD-MRP on real-world data under all experimental conditionswas ranked significantly higher than that of all other methods, followed by WP-MRP.