Collective Background Extraction for Station Market Area by Using Location Based Social Network

Half centuries of follow-up survey has enabled the architects and urban planners to design rationally by the aid of planning. Nonetheless, limitation has occurred at planning because city has been changing its utility in accordance with its users’ demand. In this paper, the authors proposed a method to analyze trait of users in market areas near stations by analyzing location based social network. After the datum collection from geotagged tweets, these GPS (global positioning system) datum were plotted to map attained from yahoo open location platform. Then the morphological analysis and terminology extraction system extracted the keywords and their scores. After calculating the distance from stations and users’ GPS coordination, the authors extracted the array of keywords and corresponding scores in some station market area. Lastly, ratios of all users’ scores and city’s scores were calculated to examine the locality. Full combination of data collection, natural language processing and visualization enabled the authors to envisage distribution of collective background in city.