sis 17(12): e5

Research Article

Relationship Among the Diameter of the Area of Influence & Refill Usage of Sri Lanka Using Anonymized Call Detail Records

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  • @ARTICLE{10.4108/eai.18-1-2017.152104,
        author={Isuru Wijesinghe and Chamath Kumarasinghe},
        title={Relationship Among the Diameter of the Area of Influence \& Refill Usage of Sri Lanka Using Anonymized Call Detail Records},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={4},
        number={12},
        publisher={EAI},
        journal_a={SIS},
        year={2017},
        month={1},
        keywords={Data Mining, Big Data, Call Detail Records, Socioeconomic Levels, User Mobility, Refill Clustering, Diameter of the Area of Influence.},
        doi={10.4108/eai.18-1-2017.152104}
    }
    
  • Isuru Wijesinghe
    Chamath Kumarasinghe
    Year: 2017
    Relationship Among the Diameter of the Area of Influence & Refill Usage of Sri Lanka Using Anonymized Call Detail Records
    SIS
    EAI
    DOI: 10.4108/eai.18-1-2017.152104
Isuru Wijesinghe1,*, Chamath Kumarasinghe1
  • 1: Department of Computer Science & Engineering, University of Moratuwa, Sri Lanka
*Contact email: isurusuranga.wijesinghe@gmail.com

Abstract

Abstract—Economic activity and human mobility are two of the three pillars of socioeconomic indicators and understanding how these correlate with each other is important to society as it may be useful in attempting to classify people into socioeconomic levels using call detail records. Refill usage is one of the key attributes that can taken to model socioeconomic levels as the general sense is that people who spend on more refill are considered as people with high purchasing power. This type of research on SES classification by CDR happened for first time in Sri Lanka and using refill features for modeling, also is not seen in any literature to date. This paper describes what the Diameter of Area of Influence (DAI) is and the relationship between the DAI of an individual which is one of many user mobility features that can be extracted from Call Detail Records (CDRs) and the amount the user refills which is the main economic activity that can be derived from CDRs. This paper also describes a methodology to find DAI using CDRs and how to cluster individual users using this distance.