Behavioral calibration of mobile-phone GPS data for population-representative analyses

2026-09-01

AI Summary

本文提出BePop框架，通过结合人口普查和时间使用调查数据，校准移动电话GPS数据以解决人口代表性分析中的行为偏差问题。

Abstract

Mobile phone mobility data have transformed the study of human behavior, but demographic and behavioral biases can compromise their representativeness and distort population-level inference. Existing calibration approaches primarily address demographic and geographic representativeness, leaving behavioral discrepancies largely uncorrected. Here we introduce the Behavioral Population (BePop) framework, which jointly calibrates mobility data to representative demographic and behavioral distributions using census data and time-use surveys. BePop embeds mobility sequences into behavioral profiles and estimates person-level weights that align both population composition and daily activity patterns. Across three U.S. metropolitan areas, the framework consistently improves agreement between GPS-derived mobility and representative behavioral distributions, including time allocation, activity transitions, and mobility motifs. Calibration also substantially alters downstream mobility indicators, demonstrating that behavioral biases can propagate into commonly used mobility measures. Our results establish behavioral representativeness as a critical complement to demographic calibration and provide a general framework for population-representative mobility inference.

Problem

• demographic and behavioral biases
• representativeness
• population-level inference
• mobility data
• calibration

Innovation

• Behavioral Calibration
• BePop Framework
• Mobility Data
• Time-Use Surveys
• Population-Representative