Despite its potential benefits of reduced traffic congestion and discounted trips, incorporating ride-pooling in a city comes with a set of challenges that require thorough analysis, optimisation, and planning. Even though, services like \textit{Uber} have existed in Amsterdam for over a decade, city wide ride-pooling has yet to be implemented. This paper uses an algorithm for exact matching of attractive shared rides (ExMAS) and Albatross travel demand data to map and analyse the spatial disparities of key performance indicators of a ride-pooling service in Amsterdam and discover the potential of certain areas in the city. The experiments utilised a set of increasing discounted fares for ride-pooling with increasing travel demand levels. A ride-pooling service with higher discounted fares generally increased the attractiveness of the system and reduced the total vehicle hours, when compared to its non-shared counterpart. It was found that the largest vehicle hour reduction were in areas on the periphery of Amsterdam (namely the West, North, and East areas) where rides of higher degree and longer trips lengths were more likely. However, the user attractiveness of the system tended to be higher in central areas of the city where trip density was higher, trip length shorter, and ride degrees lower. The study also determined that variance of the vehicle hours and user attractiveness decreased and stabilised with increasing demand level. This paper could be a starting point in optimising the possible roll out schemes for a ride-pooling service in Amsterdam.