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alternate data stream

The Experts below are selected from a list of 9 Experts worldwide ranked by ideXlab platform

James Goulding – 1st expert on this subject based on the ideXlab platform

  • Bigdata – The Unbanked and Poverty: Predicting area-level socio-economic vulnerability from M-Money transactions
    2018 IEEE International Conference on Big Data (Big Data), 2018
    Co-Authors: Gregor Engelmann, Gavin Smith, James Goulding

    Abstract:

    Emerging economies around the world are often characterized by governments and institutions struggling to keep key demographic data streams up to date. A demographic of interest particularly linked to social vulnerability is that of poverty and socio-economic status. The combination of mass call detail records (CDR) data with machine learning has recently been proposed as a way to obtain this data without the expense required by traditional census and household survey methods. Based on a sample of 330k mobile phone subscribers resident in Dar es Salaam, Tanzania (7.6m M-Money records, 450.2m call and SMS event logs) this paper demonstrates the improvements that can be made via an alternate data stream: M-Money transaction records. An alternative to traditional banking services, particularly utilized by citizens unable to obtain a bank account, M-Money transactions provide a currently unexplored but potentially more powerful data set held by the same telecommunication companies.Comparing directly to CDR as used in prior work the results show that M-Money provides an increase in socio-demographic classification accuracy (average F1 score) from 65.9% (0.63) to 71.3% (0.7) at much finer-grained spatial regions than previously examined. Notably, the combined use of M-Money and CDR data only increases prediction accuracy (average F1 score) from 71.3% (0.7) to 72.3% (0.71), providing evidence that M-Money is informationally subsuming CDR data. The reasons for this and the importance/contributions of individual features are subsequently investigated.

  • The Unbanked and Poverty: Predicting area-level socio-economic vulnerability from M-Money transactions
    2018 IEEE International Conference on Big Data (Big Data), 2018
    Co-Authors: Gregor Engelmann, Gavin Smith, James Goulding

    Abstract:

    Emerging economies around the world are often characterized by governments and institutions struggling to keep key demographic data streams up to date. A demographic of interest particularly linked to social vulnerability is that of poverty and socio-economic status. The combination of mass call detail records (CDR) data with machine learning has recently been proposed as a way to obtain this data without the expense required by traditional census and household survey methods. Based on a sample of 330k mobile phone subscribers resident in Dar es Salaam, Tanzania (7.6m M-Money records, 450.2m call and SMS event logs) this paper demonstrates the improvements that can be made via an alternate data stream: M-Money transaction records. An alternative to traditional banking services, particularly utilized by citizens unable to obtain a bank account, M-Money transactions provide a currently unexplored but potentially more powerful data set held by the same telecommunication companies.Comparing directly to CDR as used in prior work the results show that M-Money provides an increase in socio-demographic classification accuracy (average F1 score) from 65.9% (0.63) to 71.3% (0.7) at much finer-grained spatial regions than previously examined. Notably, the combined use of M-Money and CDR data only increases prediction accuracy (average F1 score) from 71.3% (0.7) to 72.3% (0.71), providing evidence that M-Money is informationally subsuming CDR data. The reasons for this and the importance/contributions of individual features are subsequently investigated.

Gregor Engelmann – 2nd expert on this subject based on the ideXlab platform

  • Bigdata – The Unbanked and Poverty: Predicting area-level socio-economic vulnerability from M-Money transactions
    2018 IEEE International Conference on Big Data (Big Data), 2018
    Co-Authors: Gregor Engelmann, Gavin Smith, James Goulding

    Abstract:

    Emerging economies around the world are often characterized by governments and institutions struggling to keep key demographic data streams up to date. A demographic of interest particularly linked to social vulnerability is that of poverty and socio-economic status. The combination of mass call detail records (CDR) data with machine learning has recently been proposed as a way to obtain this data without the expense required by traditional census and household survey methods. Based on a sample of 330k mobile phone subscribers resident in Dar es Salaam, Tanzania (7.6m M-Money records, 450.2m call and SMS event logs) this paper demonstrates the improvements that can be made via an alternate data stream: M-Money transaction records. An alternative to traditional banking services, particularly utilized by citizens unable to obtain a bank account, M-Money transactions provide a currently unexplored but potentially more powerful data set held by the same telecommunication companies.Comparing directly to CDR as used in prior work the results show that M-Money provides an increase in socio-demographic classification accuracy (average F1 score) from 65.9% (0.63) to 71.3% (0.7) at much finer-grained spatial regions than previously examined. Notably, the combined use of M-Money and CDR data only increases prediction accuracy (average F1 score) from 71.3% (0.7) to 72.3% (0.71), providing evidence that M-Money is informationally subsuming CDR data. The reasons for this and the importance/contributions of individual features are subsequently investigated.

  • The Unbanked and Poverty: Predicting area-level socio-economic vulnerability from M-Money transactions
    2018 IEEE International Conference on Big Data (Big Data), 2018
    Co-Authors: Gregor Engelmann, Gavin Smith, James Goulding

    Abstract:

    Emerging economies around the world are often characterized by governments and institutions struggling to keep key demographic data streams up to date. A demographic of interest particularly linked to social vulnerability is that of poverty and socio-economic status. The combination of mass call detail records (CDR) data with machine learning has recently been proposed as a way to obtain this data without the expense required by traditional census and household survey methods. Based on a sample of 330k mobile phone subscribers resident in Dar es Salaam, Tanzania (7.6m M-Money records, 450.2m call and SMS event logs) this paper demonstrates the improvements that can be made via an alternate data stream: M-Money transaction records. An alternative to traditional banking services, particularly utilized by citizens unable to obtain a bank account, M-Money transactions provide a currently unexplored but potentially more powerful data set held by the same telecommunication companies.Comparing directly to CDR as used in prior work the results show that M-Money provides an increase in socio-demographic classification accuracy (average F1 score) from 65.9% (0.63) to 71.3% (0.7) at much finer-grained spatial regions than previously examined. Notably, the combined use of M-Money and CDR data only increases prediction accuracy (average F1 score) from 71.3% (0.7) to 72.3% (0.71), providing evidence that M-Money is informationally subsuming CDR data. The reasons for this and the importance/contributions of individual features are subsequently investigated.

Gavin Smith – 3rd expert on this subject based on the ideXlab platform

  • Bigdata – The Unbanked and Poverty: Predicting area-level socio-economic vulnerability from M-Money transactions
    2018 IEEE International Conference on Big Data (Big Data), 2018
    Co-Authors: Gregor Engelmann, Gavin Smith, James Goulding

    Abstract:

    Emerging economies around the world are often characterized by governments and institutions struggling to keep key demographic data streams up to date. A demographic of interest particularly linked to social vulnerability is that of poverty and socio-economic status. The combination of mass call detail records (CDR) data with machine learning has recently been proposed as a way to obtain this data without the expense required by traditional census and household survey methods. Based on a sample of 330k mobile phone subscribers resident in Dar es Salaam, Tanzania (7.6m M-Money records, 450.2m call and SMS event logs) this paper demonstrates the improvements that can be made via an alternate data stream: M-Money transaction records. An alternative to traditional banking services, particularly utilized by citizens unable to obtain a bank account, M-Money transactions provide a currently unexplored but potentially more powerful data set held by the same telecommunication companies.Comparing directly to CDR as used in prior work the results show that M-Money provides an increase in socio-demographic classification accuracy (average F1 score) from 65.9% (0.63) to 71.3% (0.7) at much finer-grained spatial regions than previously examined. Notably, the combined use of M-Money and CDR data only increases prediction accuracy (average F1 score) from 71.3% (0.7) to 72.3% (0.71), providing evidence that M-Money is informationally subsuming CDR data. The reasons for this and the importance/contributions of individual features are subsequently investigated.

  • The Unbanked and Poverty: Predicting area-level socio-economic vulnerability from M-Money transactions
    2018 IEEE International Conference on Big Data (Big Data), 2018
    Co-Authors: Gregor Engelmann, Gavin Smith, James Goulding

    Abstract:

    Emerging economies around the world are often characterized by governments and institutions struggling to keep key demographic data streams up to date. A demographic of interest particularly linked to social vulnerability is that of poverty and socio-economic status. The combination of mass call detail records (CDR) data with machine learning has recently been proposed as a way to obtain this data without the expense required by traditional census and household survey methods. Based on a sample of 330k mobile phone subscribers resident in Dar es Salaam, Tanzania (7.6m M-Money records, 450.2m call and SMS event logs) this paper demonstrates the improvements that can be made via an alternate data stream: M-Money transaction records. An alternative to traditional banking services, particularly utilized by citizens unable to obtain a bank account, M-Money transactions provide a currently unexplored but potentially more powerful data set held by the same telecommunication companies.Comparing directly to CDR as used in prior work the results show that M-Money provides an increase in socio-demographic classification accuracy (average F1 score) from 65.9% (0.63) to 71.3% (0.7) at much finer-grained spatial regions than previously examined. Notably, the combined use of M-Money and CDR data only increases prediction accuracy (average F1 score) from 71.3% (0.7) to 72.3% (0.71), providing evidence that M-Money is informationally subsuming CDR data. The reasons for this and the importance/contributions of individual features are subsequently investigated.