# STROOBnet Optimization via GPU-Accelerated Proximal Recurrence Strategies

arXiv: https://arxiv.org/abs/2404.14388 · HTML: https://mlsanity.com/paper/2404.14388/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 26 references that are in the map.
- First posted on arXiv 22 Apr 2024.

## Builds on (most cited first, 1 of 1)

- [Graph Metrics for Temporal Networks](https://mlsanity.com/paper/1306.0493/) (2013)

## Built on by (most cited first, 1 of 1)

- [A Knowledge-Graph Translation Layer for Mission-Aware Multi-Agent Path Planning in Spatiotemporal Dynamics](https://mlsanity.com/paper/2510.21695/) (2025)

## Abstract (arXiv)

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.