Spatial query is a special type of database query supported by geo databases and spatial databases. The queries differ from non-spatial SQL queries. The traditional spatial queries are usually meaningless in dynamic environments since their results may be invalidated as soon as the query or data objects move. We propose spatial inverted index that extends the conventional inverted index to deal with multi-dimensional data. In this paper, we suggest a new solution to sequential nearest neighbor search which is based on pre calculating and indexing the solution space instead of indexing the data. The solution space may be characterized by a complete and overlap-free partitioning of the data space into cells, each containing exactly one data point. Each cell consists of all potential query points which have the corresponding data point as a nearest neighbor. The cells therefore correspond to the d-dimensional Voronoi cells [PS 85]. Deter-mining the nearest neighbor of a query point now becomes equivalent to determining the Voronoi cell in which the query point is located. Since the Voronoi cells may be rather complex high-dimensional polyhedral which require too much disk space when stored explicitly, the system approximate the cells by minimum bounding (hyper-) rectangles and store them in a multidimensional index structure such as the X-tree . The nearest neighbor query now becomes a simple point query which can be processed efficiently using the multidimensional index.