As the volume of documents on the Web increases, technologies to extract useful information from them become increasingly essential. For instance, information extracted from social network services such as Twitter and Facebook is useful because it contains a lot of location-specific information. To extract such information, it is necessary to identify the location of each location-relevant expression within a document. Previous studies on location disambiguation have tackled this problem on the basis of word sense disambiguation, and did not make use of location-specific clues. In this paper, we propose a method for location disambiguation that takes advantage of the following two clues: spatial proximity and temporal consistency. We confirm the effectiveness of these clues through experiments on Twitter tweets with GPS information.