STROOBnet Optimization via GPU-Accelerated Proximal Recurrence Strategies

TL;DR

This paper introduces GPU-accelerated optimization strategies for STROOBnet, a spatiotemporal network model, improving observational coverage and balancing data collection across regions using a novel Proximal Recurrence method.

Contribution

The study presents a new Proximal Recurrence approach for optimizing STROOBnet, outperforming traditional clustering methods in enhancing spatial and event frequency coverage.

Findings

Proximal Recurrence outperforms k-means and DBSCAN in coverage.

Enhanced uniformity in observational efficacy across regions.

Effective handling of initial observational imbalances.

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.