用点云-体素-社区聚类法加速地理热点预测，快19%且准度损失仅6%
Geographical hotspot prediction based on point cloud-voxel-community partition clustering
- 将空间数据转为点云再分体素，通过社区划分找相似区域
- 在土耳其考古遗址数据上处理速度提升19.31%，准确率下降6%
- 适合高维地理数据分析与热点预测任务
现有地理信息领域的热点预测方法仍处于初步阶段。本文提出一种基于点云-体素-社区划分聚类的新方法，用于检测和预测地理热点。通过分析高维数据，将空间信息表示为点云，并进一步划分为多个体素以提升分析效率。利用社区划分识别具有相似特征的空间体素，从而揭示热点分布的潜在模式。实验结果表明，在土耳其考古遗址数据集上，该方法处理速度提升19.31%，准确率仅下降6%，优于传统聚类方法。该方法不仅为热点预测提供了新视角，也为高维数据处理提供了有效工具。
原文摘要 · Abstract (English)
Existing solutions to the hotspot prediction problem in the field of geographic information remain at a relatively preliminary stage. This study presents a novel approach for detecting and predicting geographical hotspots, utilizing point cloud-voxel-community partition clustering. By analyzing high-dimensional data, we represent spatial information through point clouds, which are then subdivided into multiple voxels to enhance analytical efficiency. Our method identifies spatial voxels with similar characteristics through community partitioning, thereby revealing underlying patterns in hotspot distributions. Experimental results indicate that when applied to a dataset of archaeological sites in Turkey, our approach achieves a 19.31% increase in processing speed, with an accuracy loss of merely 6%, outperforming traditional clustering methods. This method not only provides a fresh perspective for hotspot prediction but also serves as an effective tool for high-dimensional data analysis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。