With robots becoming increasingly common in human occupied spaces, there has been a growing body of research into the problem of socially conscious robot navigation. A robot must be able to predict and anticipate the movements of people around it in order to navigate in a way that is socially acceptable, or it may face rejection and therefore failure. Often this motion prediction is achieved using neural networks or artificial intelligence to predict the trajectories or flow of people, requiring large amounts of expensive and time-consuming real-world data collection. Therefore, many recent studies have attempted to find a way to create simulated human trajectory data. A variety of methods have been used to achieve this, the main ones being path planning algorithms and pedestrian simulators, but no study has evaluated these methods against each other and real-world data. This thesis compares the ability of two path planning algorithms (A* and RRT*) and a pedestrian simulator (PTV Vissim) to make realistic maps of dynamics. It concludes that A*-based path planners are the best choice when balancing the ability to replicate realistic people flow with the ease of generating large amounts of data.