用图细胞自动机评估城市空间网络相似性，发现规划道路更不均匀。
A Framework Based on Graph Cellular Automata for Similarity Evaluation in Urban Spatial Networks
- 基于图细胞自动机设计多阶段信息演化模型，通过分布差异衡量相似性。
- 在50个城级和50个区级路网上，轮廓系数超0.9，优于现有方法。
- 揭示规划型道路网络内部异质性更高，拓扑与土地价值随迭代趋同。
衡量城市空间网络的相似性是理解城市作为复杂系统的关键。然而，现有方法大多不适用于空间网络，难以有效区分其特征。我们提出GCA-Sim，一种基于图细胞自动机的相似性评估框架。每个子模型通过信息演化过程中多阶段值分布的差异来度量相似性。我们发现某些传播规则能放大网络信号间的差异，称之为“网络共振”。通过改进的可微逻辑门网络，学习多个引发网络共振的子模型。在50个城级和50个区级道路网络上评估相似性，该框架的子模型显著优于现有方法，轮廓系数超过0.9。使用最优子模型进一步发现：规划主导的道路网络内部同质性低于自然生长型；不同领域的形态类别贡献相当；度作为基本拓扑信号，随迭代过程与地价及相关变量逐渐对齐。
原文摘要 · Abstract (English)
Measuring similarity in urban spatial networks is key to understanding cities as complex systems. Yet most existing methods are not tailored for spatial networks and struggle to differentiate them effectively. We propose GCA-Sim, a similarity-evaluation framework based on graph cellular automata. Each submodel measures similarity by the divergence between value distributions recorded at multiple stages of an information evolution process. We find that some propagation rules magnify differences among network signals; we call this "network resonance." With an improved differentiable logic-gate network, we learn several submodels that induce network resonance. We evaluate similarity through clustering performance on fifty city-level and fifty district-level road networks. The submodels in this framework outperform existing methods, with Silhouette scores above 0.9. Using the best submodel, we further observe that planning-led street networks are less internally homogeneous than organically grown ones; morphological categories from different domains contribute with comparable importance; and degree, as a basic topological signal, becomes increasingly aligned with land value and related variables over iterations.
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