This repository documents the scripts and data for the paper "Infrequent activities predict economic outcomes in major American cities", published in Nature Cities. The authors are Shenhao Wang, Yunhan Zheng, Guang Wang, Takahiro Yabe, Esteban Moro, and Alex ‘Sandy’ Pentland.
Shenhao Wang and Yunhan Zheng contributed equally.
Shenhao Wang and Esteban Moro jointly supervised this work. E-mail: shenhaowang@ufl.edu, emoro@mit.edu
Many studies have revealed the predictive power of the most frequent, regular, and habitual mobility patterns. However, it remains unclear what components of mobility patterns contain 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 visitations but successfully explain more than 50% of variation in economic outcomes. Future research should shift more attention from regular visitations to irregular activities, and policymakers could leverage these infrequent while informative activities to manage urban economic development.
- data: intermediate data sets and model outputs.
- src: scripts.