Measuring Commuting and Economic Activity inside Cities with Cell Phone Records
提出利用通勤流推断城市内收入空间分布的方法，基于手机交易数据在达卡和科伦坡验证，模型预测的收入与独立数据吻合，且无需训练数据即可达到与机器学习相当的预测能力。
Abstract We show how to use commuting flows to infer the spatial distribution of income within a city. A simple workplace choice model predicts a gravity equation for commuting flows whose destination fixed effects correspond to wages. We implement this method with cell phone transaction data from Dhaka and Colombo. Model-predicted income predicts separate income data, at the workplace and residential level, and by skill group. Unlike machine learning approaches, our method does not require training data, yet it achieves comparable predictive power. We show that hartals (transportation strikes) in Dhaka reduce commuting more for high model-predicted wage and high skill commuters.