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Journal Article


Yoo E. Ann. Am. Assoc. Geogr. 2019; 109(5): 1415-1432.


(Copyright © 2019, Informa - Taylor and Francis Group)






Time-location data collected from location-sensing technologies have the potential to advance our understanding of human mobility. Existing human activity studies tend to ignore a critical issue in data collection--the time period for which the activity data will be collected. Our study investigated this significant gap in the literature on temporal aspects of human mobility behavior--how many days constitute a period long enough to capture individuals' highly organized activity episodes and how they vary among individuals with heterogeneous demographic and social-economic characteristics. To determine a minimum number of days to capture individuals' highly organized activity episodes in activity space, we examined a distribution of Kullback-Leibler divergence indexes. To evaluate the differences in the minimal number of observation days per subgroup whose demographic and economic characteristics are heterogenous, we used a Bayesian profile regression model. Our study showed that the estimated minimum number of days required to capture routine activity patterns was 13.5 days with a standard deviation of 6.64. We found that participant's age, employment status, size of household, and accessibility to downtown, food, and physical activity, as well as economic status of residential environment, are important factors that affect temporal aspects of mobility behavior. Key Words: Bayesian profile regression, human mobility, Kullback-Leibler divergence, mobile phone data, temporal regularity.

Language: en


divergencia Kullback–Leibler; movilidad humana; Palabras clave: datos del teléfono móvil; regresión de perfil bayesiano; regularidad temporal.


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