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A Study on the Development of the Utilization Indicators for Closed School Facilities Using Big Data - Focused on Text Mining-
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빅데이터를 활용한 폐교시설 활용지표 개발에 관한 연구 - 텍스트마이닝을 중심으로 -

논문 기본 정보

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Proceeding
Author
Journal
ARCHITECTURAL INSTITUTE OF KOREA 대한건축학회 학술발표대회 논문집 대한건축학회 2019년도 춘계학술발표대회논문집 제39권 제1호(통권 제71집)
Published
2019.4
Pages
172 - 175 (4page)

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A Study on the Development of the Utilization Indicators for Closed School Facilities Using Big Data - Focused on Text Mining-
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Research history (4)

2019

A Study on the Development of the Utilization Indicators for Closed School Facilities Using Big Data – Focused on Text Mining -
김재영 한국실내디자인학회 학술대회논문집 2019.05 Proceeding
A Study on the Development of the Utilization Indicators for Closed School Facilities Using Big Data - Focused on Text Mining-
Kim ,  Jae-Young ,  Lee 대한건축학회 학술발표대회 논문집 2019.04 Proceeding
김재영 건축학과 2019.01 Thesis
A Study on the Development of the Use Index of Closed School Facilities Using Big Data -Focused on Text-Mining Techniques-
Kim Jaeyoung ,  jongkuk lee 교육녹색환경연구 2019.01 Academic journal
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Abstract· Keywords

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The purpose of this study is the preliminary index was derived through the literature review and comparison of public design indices of Korea and other nations and then, its indicators were verified through the text mining analysis using big data to suggest the final index for utilization of abolished school facilities. The results of this study are summarized as follows.
First, with the review on relevant previous studies, public design indices of Korean and other nations, and possibility of applicability for each abolished school facility type, 5 upper categories (economical, public spatial, physical, regional, and environmental and design aspects) and 15 indicators and detailed indicators were deduced. Second, for the verification of the preliminary index for utilization of abolished school facilities, the TF analysis and the TF-IDF analysis, of text mining techniques, were performed. Third, with the analysis of association between keywords, additional indicators were deduced. With the algorithm, calculated using this equation, association rules with a support value of 0.01 or above and a confidence value of 0.3 or above were identified among the screened articles, and among them, 15 association rules having the highest lift values are as follows. Fourth, the final index classification system could classify indicators into the economic, public space, physical, business planning, facility, and local environment. In addition, by stereotyping the indicators dentified by the linkage analysis in Chapter 6.2, population distribution characteristics, and facilities and land characteristics were added as indicatore categories.
#빅데이터 #폐교시설 #활용지표 #텍스트마이닝 #Bigdata #Closed School #Utilization Indicator #Text Mining

Contents

Abstract
1. 서론
2. 폐교시설의 활용 예비지표 도출
3. 빅데이터를 활용한 폐교시설 활용지표 개발
4. 결론
참고문헌

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