[Table 3]
2.6 Spatial autocorrelation
analysis
Spatial autocorrelation was conducted to explore the spatial
associations between UI and EHI in China. Global bivariate Moran’s I was
adopted to investigate spatial correlations of EHI and UI across the
entire study areas, while bivariate local indicators of spatial
association (LISA) mainly focused on evaluating the local spatial
correlations within different spatial units, such as cities in this
study (Cui, 2019). Moreover, the statistical significance of bivariate
Moran’s I was assessed by permutation tests, and 999 permutations were
used in this study. The significance value for spatial correlation
between EHI and UI was set at <0.05 for the obtained pseudo
value.
The generated clustering graph could be divided into four agglomeration
types, namely H-H (High-High), L-L (Low-Low), H-L (High-Low) and L-H
(Low-High). H-H clustering indicates high EHI values surrounded by high
UI values; H-L clustering indicates high EHI values surrounded by low UI
values; L-H clustering indicates low EHI values surrounded by high UI
values; and L-L clustering indicates low EHI values surrounded by low UI
values. The spatial autocorrelation between EHI and UI was analyzed with
the help of GeoDa 1.20 in this paper. The formulas for calculating
Moran’s I are shown below.
\(I_{\text{eu}}=\ \frac{n\sum_{i}^{n}{\sum_{j\neq i}^{n}{W_{\text{ij}}x_{i}^{e}x_{j}^{u}}}}{(n-1)\sum_{i}^{n}{\sum_{j\neq i}^{n}W_{\text{ij}}}}\)(10)
\({I^{\prime}}_{\text{eu}}=\ \ x^{e}\sum_{j=1}^{n}{W_{\text{ij}}x_{\text{ij}}^{u}}\)(11)
where Ieu and I’eu are the
global and local bivariate Moran’s I for EHI and UI, respectively.n denotes the total number of spatial units.Wij is an N-by-N spatial weight matrix for
measuring spatial correlation between the ith andjth spatial unit, which is generated based on
queen contiguity weight with a first order of neighbor in a 3 × 3
matrix. xie andxju denote the standardized
value of EHI and UI for the ith andjth spatial units, respectively, which are
calculated by Eq. (1).
2.7 Statistical
analysis
Partial least squares structural
equation modeling (PLS-SEM) combines principal component analysis and
multiple regression. It has the advantages of small sample size,
prediction of key target structures, and construction of complex
structural models, which is widely used in the study of multi-factor
causal relationships (Heir, 2019). In this paper, PLS-SEM was utilized
to evaluate the essential factors affecting UI and EHI distributions and
changes. To explore the correlation among these variables in PLS-SEM
preliminarily, the correlation analysis was used with the help of the
”corr” package of R software. EHI, UI, social factors, economic factors,
climate, and topography were used as latent variables to construct the
PLS-SEM collectively. Then, we established the indicator loading for
latent variables, and the hypothesized relationships between exogenous
variables (social factors, economic factors, climate, and topography)
and endogenous variables (EHI and UI) were calculated as path
coefficients. Moreover, bootstrapping analysis in the PLS-SEM setting
can be used to test the stability of the estimated model parameters.
Each variable was log-transformed to reduce the difference by an order
of magnitude.
The measurement model is a prerequisite for SEM, which relates
measurement items to their respective latent variables (Munim, 2020).
The indicator loadings were recommended above 0.708, which indicated
that the construct explained more than 50% of the indicator variance,
providing acceptable item reliability (Hair, 2019). Furthermore, we used
the average variance extracted (AVE) for all items on each construct to
evaluate the convergent validity of each construct measure. An
acceptable AVE of 0.50 or higher indicated that the construct explains
at least 50% of the variance of its items (Hair, 2019). The
construction and estimation of PLS-SEM model were processed using the
”plspm” package of R software (version: 3.6.3).