Classification of Guiding Policies in Location Optimization Plans Using Natural Language Processing

Abstract

In order to clarify the relationship of the guiding policies with aimed urban structure and guiding areas in Location Optimization Plans, this paper classified the guiding policies by clustering using natural language processing, and analyzed the characteristics of each type. The guiding policies were classified into three types: station-centered type, non-railway network type, and special type. The station-centered type tended to set up a wide residential guiding areas and to concentrate urban functions in the largest hub. The non-railway network type tended to create an overall vision of connecting urban areas, and tended to narrow the range of residential guiding areas and to create a multicore urban structure.