Infrequent activities predict economic outcomes in major American cities

Many studies have revealed the predictive power of the most frequent, regular and habitual mobility patterns. However, it remains unclear which components of the mobility patterns contain the most informative signals for predicting disparate economic development across urban areas. Here we use machine learning to predict economic outcomes by analyzing the heterogeneous mobility networks of 687 activities from more than 560,000 anonymized users in Boston, Chicago and Miami. We find that mobility patterns are highly predictive of the current and future economic development in major American cities but, surprisingly, the high predictive power is concentrated on infrequent, irregular and exploratory activities. These predictive activities account for only less than 2% of total visits but successfully explain more than 50% of variation in economic outcomes. Future research should shift more attention from regular visits to irregular activities, and policymakers could leverage these infrequent yet informative activities to manage urban economic development.