Anti-Poaching Patrol Route Optimization Algorithm

Mongolia's Tost Tosonbumba Nature Reserve is considered the world's only protected area dedicated solely to snow leopard conservation. My visit two years ago inspired me to leverage my skills in computer science for wildlife protection. Witnessing firsthand the challenges faced by rangers patrolling vast, rugged terrain against mounting threats from mining encroachment and poaching, I developed a specialized algorithm to optimize their patrol routes.

My algorithm addresses the complex logistics of conservation patrols by transforming the reserve into a weighted graph where nodes represent key locations (water sources, observation points, trails, and potential poaching hotspots). Each node receives a dynamic significance score based on multiple factors: inherent importance, proximity to other high-value locations, time since last patrol, and accessibility. Using principles from tourist trip design and orienteering problems, the algorithm calculates optimal patrol routes that maximize coverage of critical areas while respecting time and resource constraints.

The project required extensive data collection and synthesis from diverse sources, including USGS Global Mountain Explorer, NOAA NGS maps, and field reconnaissance. I created a comprehensive dataset of 48 significant locations throughout the reserve, each with specific attributes relevant to patrol planning. One of the greatest challenges was accurately modeling the complex relationships between locations and developing a weighting system that would prevent predictable or localized patrol patterns—a critical security feature against potential poaching activity.

The algorithm operates in multiple phases: initial node weighting, connection establishment, trail network integration, temporal analysis (increasing priority for locations not recently visited), and final significance calculation. This progressive approach ensures patrols are directed to high-value areas while maintaining comprehensive coverage over time.

While still in development, this project demonstrates the potential for algorithmic solutions to enhance conservation efforts in remote protected areas where resources are limited. Future improvements could include integration with machine learning models to predict poaching activity based on seasonal patterns, wildlife movements, and historical incident data, though this would require ongoing collaboration with field personnel to maintain data accuracy and sensitivity.

I'm proud that this algorithm has practical applications for real-world conservation challenges, potentially contributing to the protection of an endangered species while optimizing the use of limited ranger resources in a remote, challenging environment.