Performance prediction for adaptive quad tree graphical data structures

Performance prediction of multiple-storage quad trees is treated. Using random graphical data models, expected values of essential parameters such as the number of internal nodes, the number of object references, and the number of lists are estimated. From these results, more practical characteristics such as memory usage and the speed of windowing operations are obtained. The accuracy of the approximations is evaluated on experimental data.<>