  We aim to study the temporal patterns of activity in points of interest of
cities around the world. In order to do so, we use the data provided by the
online location-based social network Foursquare, where users make check-ins
that indicate points of interest in the city. The data set comprises more than
90 million check-ins in 632 cities of 87 countries in 5 continents. We analyzed
more than 11 million points of interest including all sorts of places:
airports, restaurants, parks, hospitals, and many others. With this
information, we obtained spatial and temporal patterns of activities for each
city. We quantify similarities and differences of these patterns for all the
cities involved and construct a network connecting pairs of cities. The links
of this network indicate the similarity of temporal visitation patterns of
points of interest between cities and is quantified with the Kullback-Leibler
divergence between two distributions. Then, we obtained the community structure
of this network and the geographic distribution of these communities worldwide.
For comparison, we also use a Machine Learning algorithm - unsupervised
agglomerative clustering - to obtain clusters or communities of cities with
similar patterns. The main result is that both approaches give the same
classification of five communities belonging to five different continents
worldwide. This suggests that temporal patterns of activity can be universal,
with some geographical, historical, and cultural variations, on a planetary
scale.
