Traditional random models for spatial two-dimensional data proposed in literature show their limits in generating in a satisfactory way instances of regions having a desired aggregation level. This is because none of them is really oriented to this aim. Rather, they are thought to model the behaviour of the constituting elements of the spatial data (so loosing sight of the context), or, alternatively, to model particular data structure for their representation, underestimating the fact that there is in general no semantic link between a region data and its representation. This means from one hand, the impossibility to produce meaningful the oretical results on time and space average performances of different data structures used to represent spatial regions, and, on the other hand, in an applicative context, the difficulty to generate instances of spatial regions having a statistical behaviour close to that of real data. To overcome this trouble, we introduce in our paper a new random model that provides the possibility to generate spatial regions having a desired aggregation.
[1]
Hanan Samet,et al.
A fast quadtree normalization algorithm
,
1994,
Pattern Recognit. Lett..
[2]
Hanan Samet,et al.
The Design and Analysis of Spatial Data Structures
,
1989
.
[3]
Yannis Manolopoulos,et al.
On the creation of quadtrees by using a branching process
,
1996,
Image Vis. Comput..
[4]
Christos Faloutsos,et al.
Analytical results on the quadtree decomposition of arbitrary rectangles
,
1992,
Pattern Recognit. Lett..
[5]
Claude Puech,et al.
Quadtrees, octrees, hyperoctrees: a unified analytical approach to tree data structures used in graphics, geometric modeling and image processing
,
1985,
SCG '85.
[6]
Yannis Manolopoulos,et al.
Analytical Results on the Quadtree Storage-Requirements
,
1993,
CAIP.