  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.
