Covid-19 was a pandemic that hit the world in 2019. It hurt the economies of many countries and killed millions of people. The disease is spread mainly by being in close contact with an infected individual. Like other pandemics in the past such as SARS and MERS, countries implemented quarantines and lockdowns to keep the pandemic in control. This method of control was popular in the past due to factors such as speed of the disease spread, the severity of the disease as well as lack of an effective vaccine. Over time, it was apparent that keeping countries in lockdown for an extended period time was ineffective in terms of socioeconomics. This paved ways to new measures such social distancing and mask wearing that allowed a country to let their citizens out of quarantines and lockdowns to sustain a countries economy while a vaccine is being researched. As such, by analysing pedestrian trajectories in a confined space such as a shopping centre, it is possible to evaluate the contacts between individuals and identify hotspots where such contacts often occur, and control strategies can be implemented in such facilities. This would be done by analysing the data with Python and the data analytics tool, Pandas. An exposure model will also be applied in this research to a data set of collected pedestrian trajectories in a shopping centre to calculate the exposure time pedestrians come in contact with one another and identify the hot spots of where the contacts occur and visualized with heatmaps. Social distancing will be the pedestrian flow control strategy applied to this research. The results showed that the greatest number of contacts these pedestrians encountered was 10 in the shopping centre and had spent most of their time not in contact with anyone. It was also found that the greatest risk would be the exposure time the pedestrians had was the time these pedestrians spent in contact with one person. Bachelor of Engineering (Computer Science)