The problem of optimally placing points of interest (POIs) in public spaces such as urban streets or parks to enhance user accessibility has been a central topic in network science and artificial intelligence. However, because this task constitutes a combinatorial optimization problem, finding exact solutions becomes computationally intractable for large-scale instances. Assuming that users move from their staying locations toward exits or key destinations within the environment, we propose an approximation algorithm based on the concept of betweenness centrality from graph theory. By prioritizing locations along the most critical paths between users and destinations, our method provides a practical and scalable approach to POI placement. The simulation results demonstrate that our approach achieves high-quality solutions with significantly reduced computation time.