Clustering daily patterns of human activities in the city

• Published in: Data mining and knowledge discovery Vol. 25; no. 3; pp. 478 - 510
• Main Authors: Jiang, Shan, Ferreira, Joseph, González, Marta C.
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.11.2012; Springer Nature B.V
• Subjects: Artificial Intelligence; Cellular telephones; Chemistry and Earth Sciences; Clustering; Clusters; Computer Science; Data mining; Data Mining and Knowledge Discovery; Demographics; Information Storage and Retrieval; Interdisciplinary aspects; Metropolitan areas; Mobility; Physics; Principal components analysis; Social networks; Sociodemographics; Statistical methods; Statistics for Engineering; Time of use; Transportation; Urban planning; Urban studies; Human activity; Metropolitan area; Statistical learning; Daily activity clustering; Eigen decomposition
• ISSN: 1384-5810; 1573-756X
• DOI: 10.1007/s10618-012-0264-z

Data mining and statistical learning techniques are powerful analysis tools yet to be incorporated in the domain of urban studies and transportation research. In this work, we analyze an activity-based travel survey conducted in the Chicago metropolitan area over a demographic representative sample of its population. Detailed data on activities by time of day were collected from more than 30,000 individuals (and 10,552 households) who participated in a 1-day or 2-day survey implemented from January 2007 to February 2008. We examine this large-scale data in order to explore three critical issues: (1) the inherent daily activity structure of individuals in a metropolitan area, (2) the variation of individual daily activities—how they grow and fade over time, and (3) clusters of individual behaviors and the revelation of their related socio-demographic information. We find that the population can be clustered into 8 and 7 representative groups according to their activities during weekdays and weekends, respectively. Our results enrich the traditional divisions consisting of only three groups (workers, students and non-workers) and provide clusters based on activities of different time of day. The generated clusters combined with social demographic information provide a new perspective for urban and transportation planning as well as for emergency response and spreading dynamics, by addressing when, where, and how individuals interact with places in metropolitan areas.