## 4 Results
### Polygon Overlay Analysis Intersection Operator  
To compare the performances of the different data partitioning methods for different amounts of data, the running time, acceleration ratio, and load balancing index were calculated. Figure 6 shows the results for the intersection operator for the polygon overlay analysis of the land use patch data for China and the land use data for several counties in Ningxia in 2009. When the number of threads is 1, the multicore parallel computing methods based on both types of data division are equivalent to the serial algorithm. According to the test results for the multicore parallel intersection processing of the land use patch data for China, the serial running time of the intersection operator on the land use patch data for China is approximately 65,178 s, and the load balancing index is 0. When the number of threads is 4, the polygon number-based partitioning method is  
Figure 6: Multicore parallel intersection operator calculation results achieved with different methods for different amounts of data. superior to the shape complexity-based data partitioning method designed in this paper. The running time difference is approximately 7730 s, and the load balancing index is only 0.2, which indicates that the traditional polygon number-based partitioning method is more suitable when using four threads to perform intersection operations on a large amount of data. When the number of threads is greater than four, however, the method designed in this paper shows significant improvements compared with the traditional method. When 8 threads, 12 threads, and 16 threads are used, the effect is increased by 24%, 25%, and 33%, respectively. When the maximum number of threads in the parallel environment (16 threads) is reached, the running time is only 36 min, corresponding to an acceleration ratio of 29, which is 33% better than that of the polygon number-based partitioning method. According to Figure 6, when the number of threads is greater than four, the load balancing index under the proposed data partitioning method is significantly lower than that under the traditional partitioning method, which indicates that the data partitioning method based on shape complexity balances threads better than the data partitioning method based on the number of polygons. The running times of the two different data partitioning methods also gradually decrease with an increase in the number of CPU threads, and the acceleration ratio shows an upward trend. Figure 6 also shows the test results for the multicore parallel processing of the intersection operator on land use data from several counties in Ningxia in 2009. Due to the small amount of data, the serial algorithm execution time is only approximately 451 s. When four or eight threads are used, the traditional polygon number division method is faster. When 12 or 16 threads are used, the data division method based on shape complexity designed in this paper is faster. Both data division methods can significantly shorten the running time compared to that of the serial algorithm, and their acceleration effects are similar. With an increasing number of CPU threads, the two data partitioning methods do not achieve greater computational efficiency. In terms of the load balancing index, however, the partitioning method based on shape complexity proposed in this paper has obvious advantages. Regardless of how many threads are selected for calculation, the load balancing index remains below 0.5, and the highest value of this index is only 0.33. With the data partitioning method based on the number of polygons, the load balancing index can only reach as low as 2 and can even exceed 7. This shows that for a small amount of data, the proposed data partitioning method based on shape complexity can achieve better load balancing when performing multicore parallel intersection calculations.