With the recent surge in the volume of search queries that explicitly or implicitly express users' geographical interests, to accurately infer users' locality preference becomes an increasingly important yet challenging issue. We study two click-based models of the distribution of such geographical interests by mining the user click stream data in the search engine logs, addressing three important issues in spatial Web search. First, search queries and documents can be classified by the models according to their spatial specificity. Second, the geographic center(s) of interests for queries and documents can be inferred. Finally, the model can be applied to generate relevance features for search ranking. We evaluated our proposals on a large dataset with about 10,000 unique queries sampled from the Yahoo! Search query logs, and about 450 million user clicks on 1.4 million unique Web pages over a six-months period. We report about 90% accuracy and about 3% false positive rate in identifying search queries with or without specific geographical interests, as well as statistically significant improvement in relevance ranking over a strong baseline.