Most existing sensor location design studies focus on stationary or deterministic objects (e.g., fixed OD demand) yet have not well investigated uncertain mobility of inspected objects. To bridge this gap, this study aims to integrate human mobility characteristics specified by the time geography theory into sensor location design. We first investigate trajectories of moving objects to see how they can reveal detailed information about human travel characteristics and presence probability with high-resolution detail. Then, a space–time network-based modeling framework is proposed to integrate human mobility into network location design problems. We construct a probabilistic network structure to quantify human’s presence probability at different locations and time. Then, a Mixed Integer Nonlinear Programming (MINLP) model is proposed to maximize the spatial and temporal coverage of individual targets. To obtain near optimal solutions for large-scale problems, greedy heuristic, simulated annealing and Lagrangian relaxation algorithms are tested. Theoretical analysis is conducted to show the optimality gap of the greedy algorithm. The proposed algorithms are implemented on hypothetical and real-world numerical examples (some on a high-performance computing platform) to demonstrate the performance and effectiveness of the proposed model on different network sizes and promising results have been obtained.