Virtual social interactions play an increasingly important role in the discovery of places with digital recommendations. Our hypothesis is that people define the character of a city by the type of places they frequent. With a brief description of our dataset, anomalies and observations about the data, this paper delves into three distinct approaches to visualize the dataset addressing our two goals of: 1. Arriving at a time-based region specific recommendation logic for different types of users classified by the places they frequent. 2. Analyzing the behaviors of users that check-in in groups of two or more people. The study revealed that distinct patterns exist for people that are residents of the city and for people who are short-term visitors to the city. The frequency of visits, however, is both dependent on the time of the day as well as the urban area itself (e.g. eateries, offices, local attractions). The observations can be extended for application in food and travel recommendation engines as well as for research in urban analytics, smart cities and town planning.