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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.8
- 수록면
- 15 - 24 (10page)
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초록· 키워드
오류제보하기
This study delves into the evolving welfare paradigm, transitioning from selective support services to comprehensive space-based universal services. To accomplish this, relevant research literature linking space and welfare was gathered and subjected to text mining analysis to discern prevalent trends. The research landscape has witnessed a shift. While earlier studies predominantly concentrated on socially disadvantaged groups, a growing number of investigations no longer target specific demographics. This reflects an evolving social consciousness wherein universal welfare is progressively being integrated into scholarly discussions. As welfare beneficiaries extend to encompass the wider local populace, there arises a need for an in depth exploration of demand-oriented spatial welfare. This entails identifying the genuine welfare requirements of local residents and providing fitting spatial solutions. Although the range of studied spaces has expanded to encompass diverse areas like residential and living infrastructure facilities, the majority of research still centers around conventional social welfare facilities. This is somewhat insufficient to mirror the swiftly changing societal expectations. In this context of the new normal era, it is imperative to consolidate research across previously segmented facilities, considering the anticipated surge in multifaceted welfare service demands. Lastly, it was observed that distinct topics within the Built Environment realm, such as CPTED, universal design, and sustainable design, have intertwined with welfare discussions. Employing text mining analysis, this study endeavors to uncover research trends more objectively and quantitatively. This study’s outcomes hold academic significance by enhancing comprehension of the concept of space-based welfare. It also identifies macro research trends, contributing to a more comprehensive understanding of the subject’s scope and core themes.
목차
Abstract
1. 서론
2. 문헌고찰
3. 데이터 수집 및 정제
4. 결과 분석
5. 결론 및 제언
REFERENCES
참고문헌 (37)
참고문헌 신청
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993-1022.
Choi, H. Y. (2000). A study of data mining application in information management field. Journal of Information Science Theory and Practice, 31(3), 1-20.
Choi, M. M., Park, H. K., & Lee, H. J. (2017). Exploring the possibility of space-based social work practice’ based on Lefebvre’s space theory - A case study on the production of differential space in permanent rental housing -. Korean Journal of Social W
Choi, Y. J., & Park, S. S. (2002). Interplay of text mining and data mining for classifying web Contents. Korean Journal of Cognitive Science, 13(3), 33-46.
Ha, H., Kim, S., & Ha, M. (2022). An analysis of domestic research trends in indoor healing environment using text mining and topic modeling. Conference Journal of the Architectural Institute of Korea, 42(2), 1-4.
Choi, H. Y. (2000). A study of data mining application in information management field. Journal of Information Science Theory and Practice, 31(3), 1-20.
Choi, M. M., Park, H. K., & Lee, H. J. (2017). Exploring the possibility of space-based social work practice’ based on Lefebvre’s space theory - A case study on the production of differential space in permanent rental housing -. Korean Journal of Social W
Choi, Y. J., & Park, S. S. (2002). Interplay of text mining and data mining for classifying web Contents. Korean Journal of Cognitive Science, 13(3), 33-46.
Ha, H., Kim, S., & Ha, M. (2022). An analysis of domestic research trends in indoor healing environment using text mining and topic modeling. Conference Journal of the Architectural Institute of Korea, 42(2), 1-4.
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