2.4.1. PCA Analysis

PCA was performed before establishing a regression model to involve all built environment factors and meteorological factors in the models as much as possible. The method can also remove the collinearity of independent variables to a certain extent. The principle of PCA is to convert a large number of factors into new principal factors through certain calculation methods and retain most of the information contained in the initial factors [41]. The principal factors are independent of each other and have no correlation, thereby eliminating the collinearity between factors when performing regression analysis. The relationship between the principal factors and the initial factors is as follows:

$$P\_i = \sum\_{i=1}^{n} l\_{ni} \mathbf{x}\_i \tag{1}$$

$$l\_{\rm ni} = A\_{\rm ni} / \sqrt{\lambda\_i} \tag{2}$$

where *Pi* is the *i*-th principal factor, *n* is the number of principal factors, equal to 22, *lni* refers to a principal component loading of *xi*, and *λ<sup>i</sup>* denotes an eigenvalue of the *i*-th principal factor.

Before PCA, it is necessary to carry out standardization due to the various dimensions of each factor in green space, gray space, and meteorology. The Kaiser–Meyer–Olkin (KMO) test and Bartlett sphere test were performed for 22 factors. PCA can be carried out only when the KMO value is greater than 0.5 and the Bartlett sphere test is significant (*p*-value < 0.01). PCA was then carried out using standardized factors. According to the number of independent variables, a total of 22 principal factors can be obtained. The variance and eigenvalue of the principal factor reflect their contribution to the initial factor. The greater the value, the greater the contribution and the information containing the initial factor.
