Clustering daily patterns of human activities in the city

By Shan Jiang, Joseph Ferreira Jr., Marta C. González. Data mining and knowledge discovery, 2012

302 citations recorded by the source corpus.

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.

Subfields: Transportation

View Paper: https://www.semanticscholar.org/paper/238682f10b73591c156c2b927ca9f7f9ce4c4241

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Citations in the corpus

Returners and explorers dichotomy in human mobility (2015, background): “…microscope has attracted scientists from diverse disciplines, from physics and network science 1–4 to data mining 5–8 , and has fuelled advances from public health 9–14 to transportation engineering 15–17 , urban planning 18–21 , ofﬁcial statistics 22,23 and the design of smart cities 24–27 .”
A review of urban computing for mobile phone traces: current methods, challenges and opportunities (2013, background): “INFERRING INDIVIDUAL ACTIVITIES AND TRAVEL To make the million users’ mobile phone traces useful for urban land use, community planning and transportation planning, it is crucial to answer one of the most important questions: “What are people doing in space and time?” [1, 6, 16, 30, 24, 25, 26].”
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Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2017, background): “TO improve urban mobility, accessibility, and quality of life, understanding how individuals travel and conduct activities has been the major focus of city and transportation planners and geographers [1], [2], [3], [4].”