*2.7. Regression Analysis, Modeling, and Validation*

The statistical datasets constituting land-use composition (%U, %V, and %W) and μLST with varying window sizes (from 120 × 120 m to 570 × 570 m) of 2017 were used to analyze the relationship between land-use composition and LST by performing a regression analysis. Based on these relationships, a multivariate regression model was derived for the prediction of LST from the land-use composition data. According to the European Green Capital report [39], the number of houses located at a distance of more than 300 m from a 0.5 ha adjacent green (or larger) is considered as a basis for the evaluation of a green city. If this number is large, the green score of the city will be reduced. It means that the smallest urban area used for evaluation is around 28 ha (=3.14 × 300 m2). In addition, the Ministry of Construction of Vietnam issued a Circular No. 10/2008/TT-BXD to guide the assessment and recognition of model new urban centers on 22 April 2008. In the Circular, the first requirement is that the urban area must be 50 ha or more. We assumed that a window size of 510 × 510 m, 17 × 17 pixels of Landsat 8 image, close to 25 ha (a half of the minimum requirement of the Circular No. 10) can be considered representative enough as a suitable unit for urban land management and planning. The performance of the newly derived regression model with a 510 × 510 m window size was assessed for the prediction of LST with different window sizes. The model was validated with the statistical datasets of the hottest day of 2016. Implications of the derived regression model for urban planning and design were discussed. The outline of the research flowchart is shown in Figure 3.

**Figure 3.** Outline of the research flowchart.

#### **3. Results and Discussion**
