Spatial Generalization: an Adaptive Lattice Model Based on Spatial Resolution

The paper presents an Adaptive Lattice Model (ALM) based on spatial constraints or resolution such as minimum object size and minimum spatial interval. An ALM represents GIS data by latticing adaptively the spatial space at a certain abstraction level or spatial resolution and omitting all details smaller or finer than the resolution. One implementation of ALM is called CELL, which considers each lattice as a cell whose characteristics are statistically measured as a whole. And another one is called GRID, which moves all vertexes of the objects to the nearest point or one of the corners of lattices in ALM. This paper concentrates on the CELL based generalization including adaptive clustering, adaptive aggregation, and adaptive simplification. Adaptive clustering uses the target resolution to decide which features of the same type can be clustered or not. Adaptive aggregation, following the adaptive clustering, aggregates features in a cluster to form a new feature with the same type. And, adaptive simplification merges adjoining vertexes and deletes small loops and kickbacks by rounding up all vertexes of linear features in the input dataset. Experimental results are also presented to verify the effectiveness of the proposed model.