A large-scale study of cultural differences using urban data about eating and drinking preferences

Traditional ways to study urban social behavior, e.g. surveys, are costly and do not scale. Recently, some studies have been showing new ways of obtaining data through location-based social networks (LBSNs), such as Foursquare, which could revolutionize the study of urban social behavior. We use Foursquare check-ins to represent user preferences regarding eating and drinking habits. Considering datasets differing in terms of volume of data and observation window size, our results indicate that spatio-temporal eating and drinking habits of users voluntarily expressed in LBSNs has the potential to explain cultural habits of the users. From this, we propose a methodology to identify cultural boundaries and similarities across populations at different scales, e.g., countries, cities, or neighborhoods. This methodology is extensively evaluated in several aspects. For instance, by proposing some variations of it disregarding some of the considered dimensions, as well as analyzing the results using datasets from different periods and window of observation. The results indicate that our proposed methodology is a promising approach for automatic cultural habits separation, which could enable new urban services.