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Estimation of socioeconomic attributes from location information

Author

Listed:
  • Shohei Doi

    (Waseda University
    National Institute of Informatics)

  • Takayuki Mizuno

    (National Institute of Informatics
    The University of Tokyo)

  • Naoya Fujiwara

    (Tohoku University
    The University of Tokyo)

Abstract

Timely estimation of the distribution of socioeconomic attributes and their movement is crucial for academic as well as administrative and marketing purposes. In this study, assuming personal attributes affect human behavior and movement, we predict these attributes from location information. First, we predict the socioeconomic characteristics of individuals by supervised learning methods, i.e., logistic Lasso regression, Gaussian Naive Bayes, random forest, XGBoost, LightGBM, and support vector machine, using survey data we collected of personal attributes and frequency of visits to specific facilities, to test our conjecture. We find that gender, a crucial attribute, is as highly predictable from locations as from other sources such as social networking services, as done by existing studies. Second, we apply the model trained with the survey data to actual GPS log data to check the performance of our approach in a real-world setting. Though our approach does not perform as well as for the survey data, the results suggest that we can infer gender from a GPS log.

Suggested Citation

  • Shohei Doi & Takayuki Mizuno & Naoya Fujiwara, 2021. "Estimation of socioeconomic attributes from location information," Journal of Computational Social Science, Springer, vol. 4(1), pages 187-205, May.
    • Handle: RePEc:spr:jcsosc:v:4:y:2021:i:1:d:10.1007_s42001-020-00073-w
    DOI: 10.1007/s42001-020-00073-w
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    References listed on IDEAS

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    1. Cornelia Hammer & Ms. Diane C Kostroch & Mr. Gabriel Quiros-Romero, 2017. "Big Data: Potential, Challenges and Statistical Implications," IMF Staff Discussion Notes 2017/006, International Monetary Fund.
    2. Fabio Lamanna & Maxime Lenormand & María Henar Salas-Olmedo & Gustavo Romanillos & Bruno Gonçalves & José J Ramasco, 2018. "Immigrant community integration in world cities," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-19, March.
    3. Cornelia Hammer & Diane C Kostroch & Gabriel Quiros-Romero, 2017. "Big Data; Potential, Challenges and Statistical Implications," IMF Staff Discussion Notes 17/06, International Monetary Fund.
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