Spatial correlation analysis of the indicators of anthropocene using urban data : focusing on ground-level ozone and fine particulate matters = 도시 데이터를 활용한 인류세 지표의 공간 상관성 분석 : 지표면오존과 초미세먼지를 중심으로focusing on ground-level ozone and fine particulate matters

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dc.contributor.advisorLee, Ji-Hyun-
dc.contributor.advisor이지현-
dc.contributor.authorLee, Gyu-eun-
dc.date.accessioned2021-05-12T19:36:53Z-
dc.date.available2021-05-12T19:36:53Z-
dc.date.issued2020-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=910811&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/284018-
dc.description학위논문(석사) - 한국과학기술원 : 문화기술대학원, 2020.2,[iii, 43 p. :]-
dc.description.abstractAs the impact of human development on earth system change has been rapidly increased, a lot of debates going on to define a new geological epoch called "Anthropocene" in academia. The noticeable global system changes detected in urban areas are a great deal of ground-level ozone and fine particulate matters (PM2.5). Those are having negative effects on humans again. In this research, open-source data which can correspond to ‘great acceleration’ indicators were collected to perform correlation analysis between socioeconomic indicators and earth system indicators in the local urban area spatially. A total of 29 variables which are representing urban infrastructure data were collected as explanatory variables, and ground-level ozone ($O_3$) and fine particulate matters (PM2.5) data representing earth system change in an urban environment were collected as dependent variables in the 424 administrative districts of Seoul. The regression model using ground-level ozone ($O_3$) as a dependent variable showed that air pollution emission facilities, the number of parking lots, the number of residential facilities, and the size of reconstruction were highly correlated. The geographically weighted regression model was more effective than the ordinary least squares model to explain the spatial distribution of ground-level ozone. Also, the four significant explanatory variables have regional differences in their influence on the ground-level ozone ($O_3$). The regression model using fine particulate matters (PM2.5) as a dependent variable had not much correlation with the collected explanatory data. The results of this research could be used as a basis and reference for urban planning and development projects or for policies to reduce air pollution in autonomous administrative areas.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectAnthropocene▼aGreat Acceleration▼aUrban Data▼aGround-Level Ozone($O_3$)▼aFine Particulate Matters(PM2.5)▼aGeographically Weighted Regression(GWR)-
dc.subject인류세▼a거대가속▼a도시데이터▼a지표면오존($O_3$)▼a초미세먼지(PM2.5)▼a공간가중회귀분석-
dc.titleSpatial correlation analysis of the indicators of anthropocene using urban data : focusing on ground-level ozone and fine particulate matters = 도시 데이터를 활용한 인류세 지표의 공간 상관성 분석 : 지표면오존과 초미세먼지를 중심으로-
dc.title.alternativefocusing on ground-level ozone and fine particulate matters-
dc.typeThesis(Master)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :문화기술대학원,-
dc.contributor.alternativeauthor이규은-
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