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This study introduces the Behavioral Population (BePop) framework, which calibrates mobile phone GPS data to align with both demographic and behavioral distributions using census data and time-use surveys. By embedding mobility sequences into behavioral profiles, BePop estimates person-level weights that enhance the representativeness of mobility data across various metrics, such as time allocation and activity transitions. The findings reveal that traditional demographic calibration alone is insufficient, as behavioral biases significantly impact mobility indicators, underscoring the importance of behavioral representativeness in population-level analyses.
Behavioral biases in GPS data can distort mobility indicators, but the BePop framework effectively corrects these discrepancies for more accurate population-level insights.
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.
