Temporal visitation patterns of points of interest in cities on a planetary scale: a network science and machine learning approach

TL;DR

This study analyzes global city activity patterns using Foursquare check-in data, revealing universal temporal visitation communities across continents through network science and machine learning methods.

Contribution

It introduces a novel approach combining network analysis and machine learning to classify cities by their temporal activity patterns on a planetary scale.

Findings

Five distinct city communities identified worldwide.

Temporal patterns show universality with geographical and cultural variations.

Both network and machine learning methods agree on city classifications.

Abstract

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.