Featured Application: This research describes a prototype of a system that extracts data from a social network for the purposes of urban environment analysis and that can transform subjective citizen feelings into quantifiable data via machine learning. Urban planning is usually dependent on urban analysis and tends to use data from sensor networks collected over a long period time. However, in recent years, due to increased urbanization and the rapid growth of transport, a gap has developed between urban environments and citizen feelings. Rebuilding urban infrastructure or making urban planning changes require a lot of time and resource costs. The hardware in a city cannot be easily changed, but citizen activities change all the time. Distributing city space according to a software-based recommendation, such as arranging different locations for citizen activities or traffic, is a method that can be implemented to improve city environments and to avoid resource waste. In this paper, citizens were used as sensors to extract environmental information collected using a social network service (SNS), and the information was analyzed to turn subjective feelings into objective environmental phenomena. The research focused on how to collect citizens’ feelings regarding urban environments and to develop a citizen-sensing system to bridge the gap between citizen feelings and sensor networks. The results prove that citizens who sense the city environment create small-sized data that are suitable for small-scaled, high-density cities.
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