Estimation of socioeconomic attributes from location information

Shohei Doi (), Takayuki Mizuno () and Naoya Fujiwara ()
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Shohei Doi: Waseda University
Takayuki Mizuno: National Institute of Informatics
Naoya Fujiwara: Tohoku University

Journal of Computational Social Science, 2021, vol. 4, issue 1, No 9, 187-205

Abstract: 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.

Keywords: Human behavior; Socioeconomic attributes; Location information; Machine learning; Survey data (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s42001-020-00073-w

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