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.

Este é um artigo de acesso aberto, licenciado por Creative Commons Atribuição 4.0 Internacional (CC BY 4.0), sendo permitidas reprodução, adaptação e distribuição desde que o autor e a fonte originais sejam creditados. Resumen: Este artículo explora el proceso de creación de la ciudadanía en Combarbalá, una localidad minera del norte de Chile, durante la primera mitad del siglo XIX. Queremos mostrar la realidad política de este pueblo remoto y apartado de Santiago y analizar, sobre todo, cómo su elite recibió las ideas republicanas que les presentaba el nuevo orden, situación similar a lo acontecido en otros lugares de Hispanoamérica en aquellos instantes. La opción por un análisis focalizado de este espacio local se justifica por varias razones: primero, porque permite mostrar la historicidad de estos lugares, sus proyectos y aspiraciones en momentos de construcción del Estado nacional; segundo, porque la observación de este rincón de la república ayuda a comprender lo que podríamos definir como el rayo esencial del orden republicano que se estaba construyendo (orden, justicia, inclusión política, respeto a los derechos y la educación republicana, entre otros puntos); y tercero, porque, de esta forma, se puede esquivar la tradicional visión centralista, uniformadora y generalizadora de una historiografía clásica que sigue reconociendo la fuerza del centro capitalino.

In this work, we aim at studying the diversity of human activity patterns in cities around the world. In order to do so, we use, as a proxy, 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 comprise more than 90 million check-ins in 632 cities of 84 countries in 5 continents. We analyzed more than 11 million points of interest including all sort of places: airports, train stations, restaurants, cafes, bars, parks, hospitals, gyms and many others. With this information we obtained spatial and temporal patterns of activities of 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 patterns between cities and is quantified with the relative entropy between two distributions. Then, we obtained the community structure of this network and the geographic distribution of communities. For comparison, we use a Machine Learning algorithm -unsupervised agglomerative clustering -to obtained 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 suggest that human activity patterns can be universal with some geographical, historical and cultural variations on a planetary scale.