A large amount of data is an important operation in a wide range of domains. Felipe et al. has recently extended its study to spatial databases, where keyword search becomes a fundamental building block for an increasing number of real-world applications, and proposed the IR2-Tree. A main limitation of the IR2-Tree is that it only supports exact keyword search. In practice, keyword search for retrieving approximate string matches is required. Since exact match is a special case of approximate string match, it is clear that keyword search by approximate string matches has a much larger pool of applications. Approximate string search is necessary when users have a fuzzy search condition, or a spelling error when submitting the query, or the strings in the database contain some degree of uncertainty or error. On range queries and dub such queries as Spatial Approximate String (SAS) queries. An example in the Euclidean space is depicting a common scenario in location-based services: find all objects within a spatial range r (specified by a rectangular area) that have a description that is similar to “theatre”. We denote SAS queries in Euclidean space as (ESAS) queries. a query point q and a network distance r on a road network,to retrieve all objects within distance r to q and with the description similar to “theatre”, where the distance between two points is the length of their shortest path. A straightforward solution to any SAS query is to use any existing techniques for answering the spatial component of an SAS query and verify the approximate string match predicate either in post-processing or on the intermediate results of the spatial search. We refer to them as the spatial solution.