Characterization of Behavioral Patterns Exploiting Description of Geographical Areas

  • Zolzaya Dashdorj
  • Stanislav Sobolevsky
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9860)


The enormous amount of recently available mobile phone data is providing unprecedented direct measurements of human behavior. Early recognition and prediction of behavioral patterns are of great importance in many societal applications like urban planning, transportation optimization, and health-care. Understanding the relationships between human behaviors and location’s context is an emerging interest for understanding human-environmental dynamics. Growing availability of Web 2.0, i.e. the increasing amount of websites with mainly user created content and social platforms opens up an opportunity to study such location’s contexts. This paper investigates relationships existing between human behavior and location context, by analyzing log mobile phone data records. First an advanced approach to categorize areas in a city based on the presence and distribution of categories of human activity (e.g., eating, working, and shopping) found across the areas, is proposed. The proposed classification is then evaluated through its comparison with the patterns of temporal variation of mobile phone activity and applying machine learning techniques to predict a timeline type of communication activity in a given location based on the knowledge of the obtained category vs. land-use type of the locations areas. The proposed classification turns out to be more consistent with the temporal variation of human communication activity, being a better predictor for those compared to the official land use classification.


Land-use Cell phone data records Big data Human activity recognition Human behavior Knowledge management Geo-spatial data Clustering algorithms Supervised learning algorithms 



The authors would like to thank the Semantic Innovation Knowledge Lab - Telecom Italia for publicly sharing the mobile phone data records which were provided for Big Data Challenge organized in 2013, Italy. We also would like to thank MIT SENSEable City Lab Consortium partially for supporting the research.


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Copyright information

© Springer-Verlag GmbH Germany 2016

Authors and Affiliations

  1. 1.University of TrentoPovoItaly
  2. 2.SKIL LAB - Telecom ItaliaTrentoItaly
  3. 3.DKM - Fondazione Bruno KesslerTrentoItaly
  4. 4.SICT - Mongolian University of Science and TechnologyKhorooMongolia
  5. 5.New York UniversityBrooklynUSA
  6. 6.Massachusetts Institute of Technology, MITCambridgeUSA

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