香农熵与复杂网络社区检测研究市政尺度选举联盟行为
Shannon entropy and complex network community detection to study electoral coalition behaviour at municipal scale
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中文总结 AI 辅助
本研究提出基于统计物理和复杂网络理论的框架，利用巴里市选举数据，通过社区检测和香农熵分析，发现获胜联盟具有更高地域碎片化，表明选举成功源于聚合多样投票集团而非统一共识。
中文摘要 AI 辅助
传统选举分析往往依赖于总体社会政治指标或基于统计物理的宏观模型，这些模型在国家层面处理选举数据；然而，这些模型很少应用于地方层面的投票数据，而地方层面的分析复杂性显著更高。在本研究中，我们提出一个植根于统计物理和复杂网络理论的框架，以探究地方尺度投票行为的精细架构。利用意大利巴里2024年市政选举和2025年地区选举的细粒度数据集，我们将城市选举格局表示为复杂网络，其中投票站被建模为节点，节点之间通过编码基于投票表达的统计显著相关性的链接相连。对于参与选举的每个联盟，社区检测揭示了跨越行政边界的地理邻近集群。此外，为了量化联盟是表现出地域同质的投票行为，还是分裂为不同的投票集团，我们将潜在意识形态估计和信息论香农熵$H$适应到联盟内部尺度。我们的结果表明，获胜联盟表现出显著更高的地域碎片化，这些发现表明，在地方层面，选举成功并非由维持地理上统一的共识所驱动，而是由聚合多样且非同质的投票集团的能力所驱动。这一可扩展的、数据驱动的框架超越了简单的地理统计，为揭示复杂城市环境中政治思想聚类和联盟碎片化的结构动态提供了稳健工具。
英文摘要
Traditional electoral analyses often rely on aggregate sociopolitical indicators or on macroscopic models grounded in statistical physics that treat election data at national level; however, these models have rarely been applied to local-level voting data, where the analytical complexity is substantially higher. In this study, we propose a framework rooted in statistical physics and complex network theory to investigate the fine-grained architecture of voting behaviour at the local scale. Leveraging a granular dataset from the 2024 municipal and 2025 regional elections in Bari, Italy, we represent the urban electoral landscape as a complex network, in which polling sections are modelled as nodes connected by links that encode statistically significant correlations based on vote expression. For each coalition participating in the elections, community detection reveals geographically proximal clusters that transcend administrative boundaries. Furthermore, in order to quantify whether a coalition exhibits territorially homogeneous voting behaviour or, instead, is fragmented into distinct voting blocs, we adapt latent-ideology estimation and the information-theoretic Shannon entropy $H$ to the intra-coalition scale. Our results indicate that winning coalitions exhibit significantly higher territorial fragmentation, and these findings suggest that, at the local level, electoral success is not driven by the maintenance of geographically uniform consensus rather than by the capacity to aggregate diverse and non-homogeneous voting blocs. This scalable, data-driven framework moves beyond simple geographic accounting, providing a robust tool for uncovering the structural dynamics of political idea clustering and coalition fragmentation in complex urban environments.
