This paper presents results from an evaluation of algorithms for ranking results by probability of relevance for Geographic Information Retrieval (GIR) applications. We review the work done on GIR and especially on ranking algorithms for GIR. We evaluate these algorithms using a test collection of 2500 metadata records from a geographic digital library. We present an algorithm for GIR ranking based on logistic regression from samples of the test collection. We also examine the effects of different representations of the geographic regions being searched, including minimum bounding rectangles, and convex hulls.
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