The contextual application of artificial intelligence for improving efficacy has impacted all walks of human life, including urban planning. This work focuses on the advanced usage of geotagged social media data for urban planning, focusing its scope on identification of leisure hubs in London. It is based on broad capabilities of urban computing in combination with the sophisticated analysis of social media, revealing how the recreational activities are spatially distributed across diverse neighbourhoods of this city, jointly with public perception. This paper now introduces DigiMapper, a processing and visualisation tool for data scraped from Foursquare, which will merge sentiment analysis with machine learning algorithms and geospatial mapping. This would, therefore, enable not only new insight into the dynamics of urban leisure but also an advance on traditional methodologies in urban planning. It understands that the expected output would emerge a new synthesis of data-driven analyses combined with traditional paradigms of urban planning. The collaboration reinforces the planners and policy thinkers’ efforts to further their understanding of the role leisure hubs have in enhancing vibrancy and social interaction in an urban set-up.