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精确数据采集与科学决策至关重要。本研究聚焦于时空范围观测者-被观测对象二分网络（STROOBnet），该网络将观测节点（如监控摄像头）与特定地理区域内的事件相连接，从而实现高效监控。利用新奥尔良市的实时犯罪摄像头（RTCC）系统与警务服务呼叫（CFS）数据——在该地区警力缩减而犯罪率上升的背景下，RTCC系统正发挥关键作用——我们解决了网络初始存在的观测不均衡问题。为实现均匀的观测效能，我们提出了近端递归方法。该方法通过综合考量事件频率与空间分布，提供了比k-means、DBSCAN等传统聚类方法更优的性能，显著提升了观测覆盖范围。