Over the past few years, real-time visualisation of pedestrian dynamics has become more crucial to successfully organise and monitor open-crowded events. However, the process of collecting, efficiently handling and visualising a large volume of pedestrians’ dynamic data in real time is challenging. This challenge becomes even more pronounced when pedestrians move in largesize, high-density, open and complex environments. In this article, we propose an efficient and accurate approach to acquire, process and visualise pedestrians’ dynamic behaviour in real time. Our goal in this context is to produce GPS-based heat maps that assist event organisers as well as visitors in dynamically finding crowded spots using their smartphone devices. To validate our proposal, we have developed a prototype system for experimentally evaluating the quality of the proposed solution using real-world and simulation-based experimental datasets. The first phase of experiments was conducted in an open area with 37,000 square meters in Palestine. In the second phase, we have carried out a simulation for 5000 pedestrians to quantify the level of efficiency of the proposed system. We have utilised PHP scripting language to generate a larger-scale sample of randomly moving pedestrians across the same open area. A comparison with two well-known Web-based spatial data visualisation systems was conducted in the third phase. Findings indicate that the proposed approach can collect pedestrian’s GPS-based trajectory information within 4 m horizontal accuracy in real time. The system demonstrated high efficiency in processing, storing, retrieving and visualising pedestrians’ motion data (in the form of heat maps) in real time.