# A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)
## 6 Experiments and Results
### Regional and Time-Slot ADS-B Data Extraction Performance Tests  
The area of interest is still randomly selected, and the ADS-B point data source is searched for within the corresponding stratification scale with 113.304006\({}^{\circ}\) E, 23.385286\({}^{\circ}\) N functioning as the lower left corner point, and the planes corresponding to 15*10\({}^{\prime}\), 5*5\({}^{\prime}\), 1*1\({}^{\prime}\), and 30*30\({}^{\prime\prime}\) rectangular envelope space enclosing boxes, and the heights are set to 1200, 600, 300, and 150, according to the stratification rules.  
Figure 11: Comparison of time spent on the spatiotemporal range indexing for a fixed time: (**a**) actual time consumption for the indexing operation; (**b**) time consumption for the Index Scan process.  
Under the above regional conditions, the indexing query with time interval settings was divided into four time intervals for the experimental query, and the indexing operations were performed for four task durations with five random time starting points and time intervals of 1 h, 30 min, and 15 min. The average time consumption of different methods and their time comparisons are shown in the figure below. This condition, i.e., the indexing tasks with different time intervals are set to judge the effectiveness of the time-coded indexes included in the null field coding.  
From the efficiency test results, it was found that the spatiotemporal query response time of each index decreases significantly when the query range increases (i.e., when the spatial range of the index decreases), which matches the empirical amount of the actual experiment. Elsewhere, another aspect of the cross-sectional comparison was that the spatiotemporal query response time tended to decrease when the time interval of the spatiotemporal index was compressed, which is consistent with the overall trend when considering the query volume of the whole data set. The response time is shown in Figure 12.  
Figure 12: Regional and time segment data extraction performance test: (**a**) set the time frame to 1 h; (**b**) set the time frame to 30 min; (**c**) set the time frame to 15 min.  
When comparing longitudinally, it was discovered that the initial approach of constructing a composite spatiotemporal index using LLHT and spatial indices had some disadvantages. Specifically, the query response time was abnormally slow and constituted the longest response time among all types of tasks. The second way of constructing composite spatiotemporal index with 2 dsphere spatial index plus highly hierarchical index and time slice was better than the first way of constructing composite spatiotemporal indices. The second type of 2 dsHT index, which is a composite spatiotemporal index constructed by using 2 dsphere spatial index plus highly hierarchical index and temporal slicing, is tested to be more efficient under different data volumes and is more efficient than the first type of construction. The third spatiotemporal grid-based coding in the index constructed in this way is better than the first two spatiotemporal query performances; moreover, the fluctuation under the condition of index hierarchy change is much smaller than the first two methods, approximating to O(n).