. 2024 Mar 11;10(6):e27677. doi: 10.1016/j.heliyon.2024.e27677

Table 1.

Measurement indicators and their geographical spatial meanings.

No.	Index（Variable）	Formula	Parameter Description	Spatial Geographical Meaning
1	Nearest neighbor index (R)	$R = \overline{r_{1}} / \overline{r_{E}}$ ; $\overline{r_{E}} = 1 / (2 \sqrt{D})$	R is the nearest neighbor index， $\overline{r_{1}}$ Is the actual closest distance; $\overline{r_{E}}$ Is the theoretical closest distance; D represents the point density; When R = 1, the distribution of point elements is random; R > 1, the point elements tend to be uniform; R < 1, point elements tend to be condensed	It's a geographical indicator indicating the proximity of point elements in geographical space [34]
2	kernel density estimation f(x)	$f (x) = \frac{1}{n h} \sum_{i = 1}^{n} k (\frac{x - x_{i}}{h})$	f(x) is the estimated kernel density， $k (\frac{x - x_{i}}{h})$ is the kernel function; h is the search bandwidth，nis the number of point elements; $(x - x_{i})$ represents the distance from estimated points x to $x_{i}$	Reflecting the degree of condensation by the spatial distribution of point density [35,45]
3	Moran index（I）	$I = Z_{i} \sum_{i = 1}^{n} W_{i j} Z_{j}$	I is the Moran index; $Z_{i}$ 、 $Z_{j}$ represent the standardized values of the observed values of space unit i and j; $W_{i j}$ is the space weight	It represents the spatial correlation between point elements and adjacent points [36]
4	Cold and hot analysis ( $G_{i}$ )	$G_{i} = \sum_{i} x^{i} W_{i j} / \sum_{j} x_{j}$ $Z = G_{i} - E (G_{i}) / \sqrt{V a r (G_{i})}$	$x_{i}$ 、 $y_{j}$ represent the number of villages in region i and region j respectively; $W_{i j}$ represents the space weight function; $E (G_{i})$ is the expected value; $Var (G_{i})$ is the coefficient of variation	It's used to measure the spatial dependence intensity between a point and other points within the range of distance [30]
5	Geodetector（Q）	$Q = 1 - \frac{1}{N σ^{2}} \sum_{h = 1}^{L} N_{h} σ_{h}^{2}$	h = 1，,L is the stratification of variable Y or factor X; $N_{h}$ and N are the number of units in layer h and the whole area respectively; $σ_{h}^{2}$ and $σ^{2}$ are the variances of layer h and region Y values respectively	It's used to detect whether a factor affects the reason for the spatial differentiation of a certain indicator value, and the degree of strength [46]