A density-based heuristic for household detection in college communities through Big Data analysis
DOI:
https://doi.org/10.17533/udea.redin.20240938Keywords:
Big Data, Data mining, Home detection, Points of interest, Mobility patternsAbstract
In the age of big data, the wealth of information offers unprecedented opportunities to glean valuable insights into human behavior and activities. This study focuses on leveraging data collected from mobile applications used by students at a local college to identify their home locations and other shared points of interest. Through this research, we aim to enhance understanding of mobility patterns within student communities, providing valuable information for decision-making in transportation planning and mobility-related issues in surrounding areas. This paper introduces a heuristic based on density-related clustering to detect home locations from real-time big data collected by a mobile application. The results demonstrate satisfactory precision, with potential for further improvement as additional data is acquired, thus offering insights into potential future applications and services.
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J. Osorio-Arjona and J. C. García-Palomares, “Social media and urban mobility: Using twitter to calculate home-work travel matrices,” Cities, vol. 89, pp. 268–280, 2019.
L. Pappalardo, L. Ferres, M. Sacasa, C. Cattuto, and L. Bravo, “Evaluation of home detection algorithms on mobile phone data using individual-level ground truth,” EPJ data science, vol. 10, no. 1, p. 29, 2021.
R. Ahas, S. Silm, O. Järv, E. Saluveer, and M. Tiru, “Using mobile positioning data to model locations meaningful to users of mobile phones,” Journal of urban technology, vol. 17, no. 1, pp. 3–27, 2010.
L. Gauvin, M. Tizzoni, S. Piaggesi, A. Young, N. Adler, S. Verhulst, L. Ferres, and C. Cattuto, “Gender gaps in urban mobility,” Humanities and Social Sciences Communications, vol. 7, no. 1, pp. 1–13, 2020.
M. Vanhoof, F. Reis, T. Ploetz, and Z. Smoreda, “Assessing the quality of home detection from mobile phone data for official statistics,” Journal of Official Statistics, vol. 34, no. 4, pp. 935–960, 2018.
I. Bojic, E. Massaro, A. Belyi, S. Sobolevsky, and C. Ratti, “Choosing the right home location definition method for the given dataset,” in Social Informatics: 7th International Conference, SocInfo 2015, Beijing, China, December 9-12, 2015, Proceedings 7. Springer, 2015, pp. 194–208.
I. Mendoza, G. Alvarez, M. Coello, J. López, and P. Carvallo, “Automatic estimation of demand matrices for universities throughmobiledevices,”in2020IEEEANDESCON. IEEE, 2020, pp. 1–6.
E. Schubert, J. Sander, M. Ester, H. P. Kriegel, and X. Xu, “Dbscan revisited, revisited: why and how you should (still) use dbscan,” ACM Transactions on Database Systems (TODS), vol. 42, no. 3, pp. 1–21, 2017
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