Integrating Multi-Accuracy Spatial Data

In recent years the integration of spatial data coming from different sources has become a crucial issue for many geographical applications, in particular in the process of building and maintaining a Spatial Data Infrastructure (SDI). In such context new methodologies are necessary in order to acquire and update spatial datasets by collecting new measurements from different sources. The traditional approach implemented in GIS systems for updating spatial data does not usually consider the accuracy of these data, but just replaces the old geometries with the new ones. The application of such approach in the case of an SDI, where continuous and incremental updates occur, will lead very soon to an inconsistent spatial dataset with respect to spatial relations and relative distances among objects. In this report we address this problem and we propose a framework for representing multiaccuracy spatial databases, based on a statistical representation of the objects geometry, together with a method for the incremental and consistent update of the database objects, that applies a customized version of the Kalman filter. Moreover, in the framework we consider also the spatial relations among objects, since they represent a particular kind of observation, that could be derived from geometries or be observed independently in the real world. Therefore, also spatial relations among objects coming from different sources need to be compared and we show that they are necessary in order to obtain a correct result in objects geometry integration.