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.

翻译：空间索引是地理信息系统（GIS）与多维数据管理的基础，然而当前开源生态对采用或基准测试空间访问方法的研究构成了显著障碍。我们观察到，现有开源库大多仅包含单一索引，部分受限于复杂依赖、缺失关键功能、接口不一致以及对空间数据类型支持的严格约束。针对这一问题，我们提出Indexicon：一个统一、高可移植、可扩展的开源空间索引库，专为主存空间访问方法的快速集成与便捷修改而设计。Indexicon提供了一套全面的基于树的流行空间访问方法，包括R树、四叉树变体以及KD树。每种结构均被精心实现为自包含、单文件、仅头文件的C++模板，除标准库外无任何外部依赖。关键在于，每个索引均具备统一接口，原生支持批量加载、动态插入/删除、范围查询、$k$近邻（$k$NN）搜索及结构统计追踪。我们还基于六个真实世界地理数据集，将Indexicon与这些结构的成熟且广泛使用的实现（包括Boost Geometry、PCL和Nanoflann）进行了全面性能评估。结果表明，Indexicon的轻量级设计在匹配或超越现有最先进实现性能的同时，提供了无与伦比的架构灵活性。为促进可复现的空间研究，我们以开源形式发布了完整库及其数据集和查询负载。