Abstract
We have developed a method for spatiotemporally integrating databases of shop and company information, such as from a digital telephone directory, spatiotemporally, in order to monitor dynamic urban transformations in a detailed manner. To realize this, an additional method is necessary to verify the identicalness of different instances of Japanese shop and company names that might contain fluctuations of description. In this paper, we discuss a method that utilizes an n-gram model for comparing and identifying Japanese words. The processing accuracy was improved through developing various kinds of libraries for frequently appearing words, and using these libraries to clean shop and company names. In addition, the accuracy was greatly and novelty improved through the detection of those frequently appearing words that appear eccentrically across both space and time. By utilizing natural language processing (NLP), our method incorporates a novel technique for the advanced processing of spatial and temporal data.
Highlights
Spatiotemporal changes of shop and company locations have a major effect on the vitality and attraction of urban space
This paper focuses on a particular method of name identification, pertinent to shop and company names—that is, an identification method for Japanese words
We have developed a method for removing various kinds of noise words from shop and company names, and one for verifying that differing names may refer to the same tenant, by calculating the name similarity
Summary
Spatiotemporal changes of shop and company locations have a major effect on the vitality and attraction of urban space. On the other hand, detailed information on shop and company locations and names can be collected using telephone directories and web information This is possible in Japan, because of the availability of digital telephone directories and detailed digital maps which can monitor almost all residents and tenants in a given building. The yearly continuations and changes in tenants or residents can be monitored for a certain location, and we can integrate these data across multiple years. The same can be done for shop and company locations over multiple years, by measuring changes in shop and company names. This measure is not easy because of name fluctuations between different two years or different kinds of data. This paper focuses on a particular method of name identification, pertinent to shop and company names—that is, an identification method for Japanese words
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