Abstract
We show that people’s travel destinations are predictable based on simple features of their home and destination. Using geotagged Twitter data from over 200,000 people in the U.S., with a median of 10 visits per user, we use machine learning to classify whether or not a person will visit a given location. We find that travel distance is the most important predictive feature. Ignoring distance, using only demographic features pertaining to race, age, income, land area, and household density, we can predict travel destinations with 84% accuracy. We present a careful analysis of the power of individual and grouped demographic features to show which ones have the most predictive impact for where people go.
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Krumm, J., Caruana, R., Counts, S. (2013). Learning Likely Locations. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds) User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, vol 7899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38844-6_6
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DOI: https://doi.org/10.1007/978-3-642-38844-6_6
Publisher Name: Springer, Berlin, Heidelberg
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