Wildlife species such as tigers and elephants are under the threat of poaching. To combat poaching, conservation agencies ('defenders') need to 1) anticipate where the poachers are likely to poach and 2) plan effective patrols. We propose an anti-poaching tool CAPTURE (Comprehensive Anti-Poaching tool with Temporal and observation Uncertainty REasoning), which helps the defenders achieve both goals. CAPTURE builds a novel hierarchical model for poacher-patroller interaction. It considers the patroller's imperfect detection of signs of poaching, the complex temporal dependencies in the poacher's behaviors, and the defender's lack of knowledge of the number of poachers. Further, CAPTURE uses a new game-Theoretic algorithm to compute the optimal patrolling strategies and plan effective patrols. This paper investigates the computational challenges that CAPTURE faces. First, we present a detailed analysis of parameter separation and cell abstraction, two novel approaches used by CAPTURE to efficiently learn the parameters in the hierarchical model. Second, we propose two heuristics-piecewise linear approximation and greedy planning-to speed up the computation of the optimal patrolling strategies. In this paper, we discuss the lessons learned from using CAPTURE to analyze real-world poaching data collected over 12 years in Queen Elizabeth National Park in Uganda.