Abstract
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.
Abstract (translated)
空间时间网络的观测能力对于准确收集数据和跨多个领域的明智决策至关重要。本研究重点关注Spatiotemporal Ranged Observer-Observable Bipartite Network (STROOBnet)，该网络将观测节点（例如，监控摄像头）与定义地理区域内的事件相连，实现高效的监测。利用来自实时犯罪 camera（RTCC）系统和求救电话（CFS）的数据，其中RTCC 在新奥良面对减少警力上升犯罪的情况下，我们解决了网络的初始观测不平衡问题。为了实现统一的观测效果，我们提出了Proximal Recurrence方法。与传统的聚类方法（如k-means 和 DBSCAN）相比，该方法提供了全面的事件频率和空间考虑，提高了观测覆盖。
URL
https://arxiv.org/abs/2404.14388
