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Characterization of behavioral patterns exploiting description of geographical areas
Abstract 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 1 arXiv:1510.02995v1 [cs.SI] 11 Oct 2015 be more consistent with the temporal variation of human communication activity, being a better predictor for those compared to the official land use classification
Impact of the spatial context on human communication activity
Technology development produces terabytes of data generated by hu- man
activity in space and time. This enormous amount of data often called big data
becomes crucial for delivering new insights to decision makers. It contains
behavioral information on different types of human activity influenced by many
external factors such as geographic infor- mation and weather forecast. Early
recognition and prediction of those human behaviors are of great importance in
many societal applications like health-care, risk management and urban
planning, etc. In this pa- per, we investigate relevant geographical areas
based on their categories of human activities (i.e., working and shopping)
which identified from ge- ographic information (i.e., Openstreetmap). We use
spectral clustering followed by k-means clustering algorithm based on TF/IDF
cosine simi- larity metric. We evaluate the quality of those observed clusters
with the use of silhouette coefficients which are estimated based on the
similari- ties of the mobile communication activity temporal patterns. The area
clusters are further used to explain typical or exceptional communication
activities. We demonstrate the study using a real dataset containing 1 million
Call Detailed Records. This type of analysis and its application are important
for analyzing the dependency of human behaviors from the external factors and
hidden relationships and unknown correlations and other useful information that
can support decision-making.Comment: 12 pages, 11 figure
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