An Intelligent Method of Detecting Multi-factors Neighborhood Relation Based On Constrained Delaunay Triangulation

Spatial neighborhood relation detecting is the basis of organization, query, analysis and reasoning of spatial data. For the spatial neighborhood relations of the geographic entities are contained in the Delaunay triangulation, in this paper, the spatial neighborhood relations between multi-factors (including points, lines and polygons) are intelligently detected based on the constrained Delaunay triangulation (CDT). This approach consists of steps listed below: A matched candidate points index is set up in order to reorganize the related data of multi-factors. Then the CDT, which is constrained by edges of lines and polygons, is established with the source of coordinates in point index. After coding the CDT according to a rule this paper proposing, neighborhood relation of geographic entities can be searched automatically. And a global neighborhood relation (including separation and neighbor relation) among multi-factors is automatically established by spatial reasoning. This intelligent method of spatial neighborhood relation detecting, which is no need for manual intervention and not limited between two kinds of geographic entities, has high precision and great feasibility in practice.