We have developed a method for spatiotemporally integrating databases of shop and company information, such as from a digital\r\ntelephone directory, spatiotemporally, in order to monitor dynamic urban transformations in a detailed manner. To realize this,\r\nan additional method is necessary to verify the identicalness of different instances of Japanese shop and company names that\r\nmight contain fluctuations of description. In this paper, we discuss a method that utilizes an n-gram model for comparing and\r\nidentifying Japanese words. The processing accuracy was improved through developing various kinds of libraries for frequently\r\nappearing words, and using these libraries to clean shop and company names. In addition, the accuracy was greatly and novelty\r\nimproved through the detection of those frequently appearing words that appear eccentrically across both space and time. By\r\nutilizing natural language processing (NLP), our method incorporates a novel technique for the advanced processing of spatial\r\nand temporal data.
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