Research And Application Of Multi-way Spatial Distance Join Query

With the development of information technology and the extension of application, GIS (Geographic Information System) technology has developed rapidly. GIS is applied more and more widely, the data included is more and more enormous, while the efficiency requirement is getting higher. So it becomes increasingly important that efficient query in the enormous spatial data, creating appropriate spatial index is key to the efficient query. Due to the rapid development of the current real estate industry, simple query can't satisfy the users'query requirement for the spatial data such as distance-based multi-way spatial join query. It becomes particularly important that applying spatial data query to the real estate industry to realize multi-level information management.The system adopts open source MapGuide platform, typical three-tier architecture and.Net programming environment to realize implementation. This paper studies distance-based multi-way spatial join query. Firstly, the system uses K-means algorithms to clustering analyze spatial data and create the minimum boundary rectangle which improve the performance of query spatial data because of immensity and complexity of the spatial data. And then using R-tree insertion algorithm creates spatial index structure. Using plane filter based on distance algorithm filters spatial data object at last.The results indicate data object that doesn't satisfy query conditions is removed from the candidate set through data filter, decreases the calculation count of spat data distance, disk access count and CPU response time, greatly improves the efficiency of users'spatial query.