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c. Demographic and socioeconomic characteristics
Population and household characteristics for the census output area units were obtained from the Statistic geographic information service in 2010 and 2015.
Individual variables were selected from eight types to explain the different heat environments according to demographic and socioeconomic characteristics (Table 3.4). Persons over 65 or under 5 years of age were considered as part of the population vulnerable to heat. Basic living, single and rented households, and housing prices were considered vulnerable socioeconomic variables. Foreigners and persons aged over 15 and under 39 were considered workers.
The SGIS data were provided at five-year intervals. Rented household data were from 2010 because no information was available from 2015. Housing prices (2012-2017) were also provided in the SGIS data. Six-year housing price data were averaged according to census output area after the prices of detached houses and apartments were combined. Data on foreigners and basic living conditions were determined in Dong-units with larger areas than those in the census output data because census output areas did not provide them. To distribute population data according to Dong-units in the census output area, the total number of houses in the census output area were first divided by the total number of houses in Dong-units and then calculated as a ratio. The calculated value divided by the total population of the census output area was then reprocessed as a ratio.
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Figure 3.4. Conceptural diagram for study anaysis process
Thus, this study used structural equation modeling (SEM), which was conducted according to three mediation variables. The relationship between these variables and the proximity of the industrial areas revealed whether neighboring areas were thermally vulnerable. Thermal inequity was explained by comparing the proximity of the green buffer and long-delayed green facility.
a. Structural Equation Modeling (SEM)
Structural equation modeling (SEM) is an extension of general linear modeling. It allows researchers to test causal relationships between variables. These statistics cannot be used to theorize why one factor may cause another, but the effects of one factor can likely be determined (Lei and Wu, 2007). SEM was thus used to estimate complex causal relationships as well as the direct and indirect effects among the variables.
SEM was implemented using the lavaan function in the R-studio library. This was done to comprehensively explain the relationships between the individually analyzed built environments, social vulnerabilities, and LST differences. I examined the effects of the study variables on LST and found those causing thermal inequity. Model fitness was analysis through an exploratory factor analysis.
Latent variables were then selected through a confirmatory factor analysis. The final model was constructed by determining the path between the variables according to the study hypothesis.
SEM fitness was also calculated. This was determined using the Goodness of Fit Index (GFI), Comparative Fit Index (CFI), Normally Fixed Index (NFI), Root Mean Square Residual (RMR), and RMSEA. The GFI, CFI, and NFI criteria were presented as good models with scores over 0.9. RMR
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and RMSEA were generally reported as model fitness indexes of less 0.1 (Steiger, 1990; Hu and Bentler, 1999; Genfen et al, 2000).
b. Principal Component Analysis (PCA)
An exploratory factor analysis includes a principal component analysis (PCA). Six variables were considered in this study’s PCA, which was performed using varimax rotation (Table 3.5). If the Cronbach's alpha of the PCA is over 0.50, the model is considered fit; if it is over 0.70, it is considered reliable (Kim, K., et al., 2016). KMO test was conducted to confirm the fitness of the PCA (reliability was determined at 0.7874). Some variables were excluded from the PCA because they reduced the explanatory power of the model.
Table 3.5 PCA of vulnerable social and housing factors
Variable Component1 (0.4825) Component 2 (0.7874)
Pop. Over65 0.6294
Single household 0.5429
Rented household 0.5903
Detached house 0.3963
APT -0.4429
Housing over 30 years 0.7005
Factors with eigenvalues over 1 were extracted. The two components showed an explanatory power of 78.7%. The first factor was determined according to whether the structure was a single household, rented household, detached house, or apartment (vulnerable households were detached). The second factor was determined according to the ratio of the population over 65 years old and the housing over 30 years (vulnerable populations lived in old houses).
c. Confirmatory factor analysis
The separate characteristics of the components were mixed as a result of the PCA. A confirmatory factor analysis was performed after the mixed factors were reclassified. Component 1 was reclassified as the population over 65 years old as well as single and rented households. Component 2 was reclassified as detached houses and houses over 30 years (apartments were excluded because they were contrary to other variables). Finally, non-latent variables were excluded and instead considered as observational variables used to explain thermal inequity.
The model was considered valid if the average variance extracted (AVE) was over 0.5 and conceptual
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reliability (Cronbach’s alpha) was over 0.7 (Yoon, C. & Choi, G., 2015). Internal reliability was confirmed with an AVE of 0.57 and a Cronbach’s alpha of 0.83. Consequently, the latent variables consisted of vulnerable social classes (populations over 65 years old and those living in single or rented houses) and descript detached housing (detached houses and houses over 30 years).