Spatiotemporal networks' observational capabilities are crucial for accurate data gathering and informed decisions across multiple sectors. This study focuses on the Spatiotemporal Ranged Observer-Observable Bipartite Network (STROOBnet), linking observational nodes (e.g., surveillance cameras) to events within defined geographical regions, enabling efficient monitoring. Using data from Real-Time Crime Camera (RTCC) systems and Calls for Service (CFS) in New Orleans, where RTCC combats rising crime amidst reduced police presence, we address the network's initial observational imbalances. Aiming for uniform observational efficacy, we propose the Proximal Recurrence approach. It outperformed traditional clustering methods like k-means and DBSCAN by offering holistic event frequency and spatial consideration, enhancing observational coverage.

翻译：时空网络的观测能力对于跨多个领域的准确数据收集和知情决策至关重要。本研究聚焦于时空范围观测者-可观测二分网络（STROOBnet），该网络将观测节点（如监控摄像头）与限定地理区域内的事件相连接，从而实现高效监测。利用新奥尔良市实时犯罪摄像头系统与警情服务数据（其中实时犯罪摄像头系统在警力减少的情况下应对日益增长的犯罪），我们解决了网络初始观测不平衡问题。为实现均匀观测效能，我们提出近端递归方法。该方法通过综合考虑事件频率与空间分布，在观测覆盖率方面优于 k-means 和 DBSCAN 等传统聚类方法。