## 3 Data Sources and Research Methods
### Research Methods  
(1) Nearest Neighbor Distance Method  
The nearest neighbor analysis is a quantitative method utilized to assess the spatial distribution characteristics of points within a given area [65; 66; 67]. This approach involves comparing the actual minimum distance between each point to the expected distance that would occur under a theoretical nearest neighbor model. The essence of this method is encapsulated in the construction of a standardized nearest neighbor index. \[E=\frac{\widetilde{\gamma_{a}}}{\widetilde{\gamma_{e}}}=\frac{(\sum_{i}r_{i})/n }{\frac{\sqrt{n/A}}{2}}=\frac{2\sqrt{\rho}}{n}{\sum_{i}\gamma_{i}} \tag{1}\]  
To elaborate, this index \(E\) is calculated by first determining the average nearest neighbor distance among points, represented as \(\overline{d}\). This average is obtained by averaging the nearest neighbor distances \(d_{i}\) for each point \(i\). Additionally, \(A\) represents the area under consideration, \(n\) denotes the total number of points within this area, and \(\rho\) is the density of the points, calculated as \(n/A\). The expected average nearest neighbor distance under random distribution conditions is denoted as \(\overline{D}\) and is inversely proportional to the square root of \(\rho\). The standard nearest neighbor index \(E\) is then computed by dividing the observed average nearest neighbor distance \(\overline{d}\) by the expected distance \(\overline{D}\). The value of \(E\) is indicative of the type of distribution of the points: a value of \(E=1\) suggests a random distribution, reflecting no significant spatial clustering or dispersion; \(E=0\) indicates a completely clustered distribution, where points are significantly closer than expected; and \(E=2.1491\) characterizes an even distribution, implying that the points are dispersed in a uniform manner throughout the area. This methodological framework not only facilitates the understanding of the spatial patterns observed in the data but also provides insights into the underlying spatial processes affecting these patterns. By applying the nearest neighbor analysis, researchers and analysts can evaluate the degree of clustering or dispersion in geographic phenomena, making it a vital tool in the fields of geography, ecology, and urban planning. (2) Kernel Density Estimation Method  
Kernel density estimation (KDE) is a sophisticated, non-parametric statistical technique that aligns closely with [PERSON]'s First Law of Geography. This fundamental geographic principle asserts that while all entities are interconnected, those in closer proximity exhibit a greater degree of correlation than those further apart [68; 69; 70]. KDE excels in detecting and illustrating the spatial clustering of elements that are situated near one another by quantitatively assessing the intensity of events across a defined space through detailed density analysis. This method effectively models a density function where the influence of a kernel element, representing a point or feature in space, gradually diminishes from its peak value to zero as the distance from the point extends beyond a specific threshold. This decay is controlled by a function that ensures the diminishing influence is smooth and gradual, reflecting the natural decrease in interaction intensity with increasing distance. The strength and utility of KDE lie in its ability to provide a clear, visual representation of spatial data, illustrating areas of high density and potential clusters. By employing this method, researchers can gain valuable insights into the geographic distribution of phenomena, making KDE an indispensable tool in fields such as ecology, epidemiology, crime analysis, and urban planning. Through the application of KDE, spatial patterns that might not be evident through other analytical approaches become visible, offering a deeper understanding of spatial relationships and dynamics.