Distributed Integration of Spatial Data with Different Positional Accuracies

Spatial Data Infrastructures (SDIs) have been developed in many countries, determining the need for new techniques able to efficiently integrate spatial data in a distributed context. In order to preserve coherence and consistency of the integrated data, such techniques cannot ignore the positional accuracy of both the source datasets and the new computed data. Considering accuracy information during the integration process inevitably increases the complexity of such operation in terms of time and space required to compute and store the updated data. This paper presents a novel integration technique based on a multi-accuracy spatial data model, which includes a distributed update phase performed by each SDI member, and a centralized recombination phase performed by an SDI manager. Moreover, some optimizations are proposed for efficiently storing and transferring accuracy information. These two aspects make the technique applicable in a distributed context, even in presence of huge among of data.