## Appendix A  
The partitioning algorithm refers to the research of [PERSON] and [PERSON] [28], and the specific steps are as follows.  
Initial partitioning of Ncut  
The network is constructed as an undirected graph \(G(V,E)\), the set of nodes is \(V\), and each node \(i\) corresponds to a grid on the network, \(i\in V\). In each grid, the mean of link densities is used as the density of the grid, \(d_{i}\). The spatial connectivity between the grids is defined by the adjacency matrix \(a\) (the value is 0 or 1) is obtained. If grid \(i\) and grid \(j\) are adjacent, \(a(i,j)=1\); otherwise, \(a(i,j)=0\). The spatial distance \(r(i,j)\) between two grids is defined as the shortest path between nodes \(i\) and \(j\), which is the minimum number of grids from \(i\) to \(j\) on the graph. The similarity function \(w(i,j)\) between grid \(i\) and \(j\) is defined as follows:  
\[w(i,j)=\left\{\begin{array}{cc}\exp\Bigl{\{}-(d_{i}-d_{j})^{2}\Bigr{\}}&r(i,j)=1\\ 0&r(i,j)>1\end{array}\right. \tag{1}\]  
Suppose the network \(U\) is partitioned into subregion \(A\) and \(B\), the total similarity between \(A\) and \(B\) can be expressed as:  
\[cut(A,B)=\sum_{i\in A,j\in B}w(i,j) \tag{2}\]  
where \(u\) is the grid of the network \(U\). Then define the total disassociation \(Ncut\) between two subregions and the association \(Nassoc\) within in each subregion are given as follows:  
\[Ncut(A,B)=\frac{cut(A,B)}{cut(A,U)}+\frac{cut(A,B)}{cut(B,U)} \tag{3}\]  
\[Nassoc(A,B)=\frac{cut(A,A)}{cut(A,U)}+\frac{cut(B,B)}{cut(B,U)} \tag{4}\]  
Ncut algorithm aims at minimizing \(Ncut\) between each subregion and maximizing \(Nassoc\) within each subregion. That is to say that Equation (3) reaches the minimum and Equation (4) is the maximum. \(Ncut\) in Equation (3) and \(Nassoc\) in Equation (4) have the relationship:  
\[Ncut(A,B)=2-Nassoc(A,B) \tag{5}\]Therefore, the Ncut algorithm is used to minimize \(Ncut(A,B)\), which can simultaneously maximize the total disassociation between different subregions and the association within a subregion. Solving the problem is an NP-complete problem. [PERSON] and [PERSON] [29] proposed the equivalent eigenvalue system to partition the eigenvector corresponding to the second smallest eigenvalue.  
Merging and evaluation indicators for partitioning  
After the initial partitioning, the network is partitioned into several subregions with similar densities. A region with a higher degree of similarity is possibly divided since each subregion always is divided into two parts at each iteration step. Therefore, merging is necessary to improve the rationality of partitioning further. In the merging process, the average density differences in all two adjacent subregions are calculated. The two subregions with the smallest difference are merged until the evaluation indicators introduced in the next step are optimized.  
Suppose that the whole network is partitioned into \(k\) subregions. The indicator \(NS_{k}\) is defined to evaluate the difference between the two subregions:  
\[NS_{k}(A,B)=\frac{\sum_{i\in A}\sum_{j\in B}\left(d_{i}-d_{j}\right)^{2}}{N_{A }N_{B}} \tag{10}\]  
where \(N_{A}\) and \(N_{B}\) are the number of grids in subregion \(A\) and \(B\). To make sure the highest internal similarity and low similarity with other subregions, the following self-similarity can be used:  
\[NSS_{k}(A)=\frac{NS_{k}(A,A)}{NSN_{k}(A,B)} \tag{11}\]  
where \(NSN_{k}(A,B)=\min\{NS_{k}(A,X)|X\in Neighbor(A)\}\),\(Neighbor(A)\) is the set of the subregions adjacent to subregion \(A\) in space. If \(NSS_{k}(A)<1\), subregion \(A\) is properly partitioned. The overall partitioning can be evaluated by the value of the average similarity \(ANS_{k}\):  
\[ANS_{k}=\frac{\sum_{A\in K}NSS_{k}(A)}{k} \tag{12}\]  
where \(K\) is the set of subregions and \(k\) is the number of subregions partitioned. The optimal partitioning result is obtained when \(ANS_{k}\) reaches the minimum.