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Location-based information contained in publicly available GIS databases is invaluable for many applications such as disaster response, national infrastructure protection, crime analysis, and numerous others. The information entities of such databases have both spatial and textual descriptions. Likewise, queries issued to the databases also contain spatial and textual components, for example, "Find shelters with emergency medical facilities in Orange County," or "Find earthquake-prone zones in Southern California." We refer to such queries as spatial-keyword queries or SK queries for short. In recent times, a lot of interest has been generated in efficient processing of SK queries for a variety of applications from Web-search to GIS decision support systems. We refer to systems built for enabling such applications as Geographic Information Retrieval (GIR) Systems. An example GIR system that we address in this paper is a search engine built on top of hundreds of thousands of publicly available GIS databases. Building a search engine over such large repositories is a challenge. One of the key aspects of such a search engine is the performance. In this paper, we propose a framework for GIR systems and focus on indexing strategies that can process SK queries efficiently. We show through experiments that our indexing strategies lead to significant improvement in efficiency of answering SK queries over existing techniques.