空间索引是地理信息系统（GIS）及多维数据管理的基础，但当前开源生态对采用或评测空间访问方法的研究构成了显著障碍。我们发现，现有大多数开源库仅支持单一索引；部分库受限于复杂的依赖关系、缺失关键功能、API 不一致，以及对空间数据类型支持的刚性约束。为解决该问题，我们提出 Indexicon：一个统一、高度可移植、可扩展的开源空间索引库，专为内存中空间访问方法的快速集成与便捷修改而设计。Indexicon 提供了一整套主流基于树的空间访问方法，包括 R 树、四叉树变体及 KD 树。每种结构均以自包含、单文件、仅头文件（header-only）的 C++ 模板形式精心实现，除标准库外不依赖任何外部组件。尤为关键的是，所有索引均采用统一接口，原生支持批量加载、动态插入/删除、范围查询、k 近邻（kNN）搜索以及结构统计信息追踪。我们还在六个真实世界地理数据集上，将 Indexicon 与这些结构的成熟且广泛应用的实现（包括 Boost Geometry、PCL 和 Nanoflann）进行了全面性能评测。结果表明，Indexicon 的轻量级设计在性能上达到或超越现有最先进实现，同时提供无与伦比的架构灵活性。为促进可复现的空间研究，我们已将完整库、数据集及查询负载全部开源。

Spatial indexing is foundational to Geographic Information Systems (GIS) and multi-dimensional data management, yet the current open-source landscape poses a significant barrier to research that employs or benchmarks spatial access methods. We observe that most of the existing open-source libraries include a single index. Some are hindered by complex dependencies, missing critical functionalities, inconsistent APIs, and rigid constraints regarding the support of spatial data types. To address this issue, we introduce Indexicon: a unified, highly portable, extendable, open-source spatial indexing library, designed specifically for rapid integration and ease of modification of main-memory spatial access methods. Indexicon provides a comprehensive suite of popular tree-based spatial access methods, including the R-tree, Quad-tree variants, and the KD-tree. Each structure is meticulously implemented as a self-contained, single-file, header-only C++ template with zero external dependencies beyond the standard library. Crucially, every index features a uniform interface, natively supporting bulk-loading, dynamic insertions/deletions, range queries, $k$-nearest neighbor ($k$NN) search, and structural statistics tracking. We also present an extensive performance evaluation of Indexicon against well-established and widely used implementations of these structures (including Boost Geometry, PCL, and Nanoflann) across six real-world geographic datasets. Our results demonstrate that Indexicon's lightweight design matches or outperforms existing state-of-the-art implementations while offering unmatched architectural flexibility. To foster reproducible spatial research, we open-source the complete library alongside our datasets and query workloads.