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오대식(Oh, Dae-Sick),지승열(Ji, Seung-Yeul),전한종(Jun, Han-Jong) 대한건축학회 2014 대한건축학회논문집 Vol.30 No.12
There are many changes in environment of housing market along with changes in economic, social and cultural environment. Supply of housing provider focused on the provider has changed to providing house for consumer’s needs and preferences. However, there is not a systematic decision maleng way for stakeholders and subjective information provided such as scientific statistics and result through analysis. Among derived issues, the critical part is absences of decision support platform between consumers and providers. This research presents segmentation of householder’s desire upon increased level of living and quality of life according to historical background, and duty required for consumer based residential environment, upon promotion of residential satisfaction. This research is carried on using housing survey of Ministry of Land, empirical analysis of housing choice through development of prediction models and building decision support system.
빅데이터를 활용한 가구특성별 주거입지 및 주택유형 선택에 관한 데이터베이스 구축
백민호(Min-ho, Back),오대식(Dae-sick, Oh),전한종(Han-jong, Jun) (사)한국CDE학회 2015 한국 CAD/CAM 학회 학술발표회 논문집 Vol.2015 No.동계
Recently there has been a growing interest in the effective use of big data. In particular, until now extracts the rules and patterns from a large amount of various data that little-known, is growing social interest to use the information. Big data is simply to extract information that is not limited to extract not well known information by monitoring the social issues but develop customized service model through the simulation, formulates support services, and a policy can be the ultimate purpose is to cooperate to achieve a happy society. However, the reality is still that such as academic research and discussion for effective use of big data from residential, urban, architecture in the social sciences. In this study, as part of the effective use of big data and take advantage of government 3.0 public data that the current government is promoting, based on the database to select the type of furniture properties by residential location and housing type, it is an object of this by indexing to construct a data rule. In this study, it is Summarizing to the public data Obtain meaningful result value. It plans to leverage the data subsystem for establishing decision support systems needed for decision-making on household characteristics by residential location and housing type selected.