공간통계 기법을 이용한 서울시 지표면온도 모델링: 온도와 인구변수 간의 공간적 분포를 고려한 회귀모형분석 = Modeling Land Surface Temperature Using Spatial Statistical Methods: A Regression Modelling Approach to Analyzing Spatial Patterns Between Temperature and Demographic Data in Seoul, South Korea

다국어 초록 (Multilingual Abstract)

This study analyzes the relationship between demographic data and land surface temperature (LST) in urban areas using statistical approach. Remote sensing techniques, LST values were used to derive from Landsat 7 ETM+ imagery, while demographic variables—such as employment density and population density—were incorporated from census output area data. Initial modeling with ordinary least squares (OLS) regression was found to be unreliable due to assumption violations, prompting the adoption of a spatial regression approach. The spatial error model ultimately proved most effective in capturing the relationship between LST and demographic factors. Findings revealed a positive correlation between surface temperature and population variables: a 10% increase in employment density corresponded to a 0.095% rise in surface temperature, while a 10% increase in population density led to a 0.085% increase. Dummy variables representing rivers and mountainous areas were incorporated to control for potential overestimation by natural environmental factors, showing a negative correlation with surface temperature. Additionally, residuals exceeding 2.5 standard deviations identified high-temperature zones associated with specialized land use (e.g., military installations, airfields, parking lots, and railway facilities), whereas residuals below -2.5 standard deviations indicated cooler, natural areas.

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