Mining online footprints to predict user’s next location

Reference

• Alvares, L.O., et al., 2007. Towards semantic trajectory knowledge discovery. Technical Report, October, Belgium: Hasselt University.
   Google Scholar
• Asahara, A., et al., 2011. Pedestrian-movement prediction based on mixed Markov-chain model. In: Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems, 1–4 November 2011 Chicago, IL. ACM, 25–33.
   Google Scholar
• Ashbrook, D. and Starner, T., 2003. Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing, 7 (5), 275–286. doi:10.1007/s00779-003-0240-0
   Web of Science ®Google Scholar
• Chen, J., et al., 2011. Exploratory data analysis of activity diary data: a space–time GIS approach. Journal of Transport Geography, 19 (3), 394–404. doi:10.1016/j.jtrangeo.2010.11.002
   Web of Science ®Google Scholar
• Chodorow, K., 2013. MongoDB: the definitive guide. Sebastopol, CA: O’Reilly Media.
   Google Scholar
• Ester, M., et al., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In: The second international conference on knowledge discovery and data mining (KDD-96), 2–4 August Portland, OR. California: AAAI Press, 226–231.
   Google Scholar
• Gambs, S., Killijian, M.O., and Del Prado Cortez, M.N., 2011. Show me how you move and I will tell you who you are. Transactions on Data Privacy, 4 (2), 103–126.
   Google Scholar
• Gambs, S., Killijian, M.O., and Del Prado Cortez, M.N., 2012. Next place prediction using mobility Markov chains. In: Proceedings of the first workshop on measurement, privacy, and mobility, 10–13 April Bern, Switzerland. New York, NY: ACM, 3.
   Google Scholar
• Han, J., Pei, J., and Kamber, M., 2011. Data mining: concepts and techniques. Burlington, MA: Morgan Kaufmann.
   Google Scholar
• Huang, Q., Cao, G., and Wang, C., 2014. From where do tweets originate? - A GIS approach for user location inference. In: Proceedings of the 7th ACM SIGSPATIAL international workshop on location-based social networks (LBSN ‘14), 4–7 Novermber Dallas, TX. New York, NY: ACM, 1–8.
   Google Scholar
• Huang, Q. and Wong, D., 2015. Modeling and visualizing regular human mobility patterns with uncertainty: an example using twitter data. Annals of the Association of American Geographers, 105 (6), 1179–1197. doi:10.1080/00045608.2015.1081120
   Web of Science ®Google Scholar
• Huang, Q. and Wong, D., 2016. Activity patterns, socioeconomic status and urban spatial structure: what can social media data tell us? International Journal of Geographic Information Science, 30 (9), 1873–1898. doi:10.1080/13658816.2016.1145225
   Web of Science ®Google Scholar
• Huang, Q. and Xu, C., 2014. A data-driven framework for archiving and exploring social media data. Annals of GIS, 20 (4), 265–277. doi:10.1080/19475683.2014.942697
   Google Scholar
• Jeung, H., et al., 2008. A hybrid prediction model for moving objects. In: Data engineering, 2008. ICDE 2008. IEEE 24th international conference on. 7–12 April Cancun, Mexico. New York, NY: IEEE, 70–79.
   Google Scholar
• Joseph, K., Tan, C.H., and Carley, K.M., 2012. Beyond local, categories and friends: clustering foursquare users with latent topics. In: Proceedings of the 2012 ACM conference on ubiquitous computing, 5–8 September Pittsburgh, PA. New York, NY: ACM, 919–926.
   Google Scholar
• Mennis, J. and Guo, D., 2009. Spatial data mining and geographic knowledge discovery - an introduction. Computers, Environment and Urban Systems, 33 (6), 403–408. doi:10.1016/j.compenvurbsys.2009.11.001
   Web of Science ®Google Scholar
• Miller, H. and Han, J., 2009. Geographic data mining and knowledge discovery. Boca Raton, FL: CRC Press.
   Google Scholar
• Monreale, A., et al., 2009. Wherenext: a location predictor on trajectory pattern mining. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, 30 June–1 July Paris, France. New York, NY: ACM, 637–646.
   Google Scholar
• Morzy, M., 2007. Mining frequent trajectories of moving objects for location prediction. In: Proceedings of the 5th international conference on machine learning and data mining in pattern recognition, 25–27 July Leipzig. Berlin: Springer, 667–680.
   Google Scholar
• Noulas, A., et al., 2012. A tale of many cities: universal patterns in human urban mobility. Plos One, 7 (5), e37027. doi:10.1371/journal.pone.0037027
   PubMed Web of Science ®Google Scholar
• Oh, S., 2012. Using an adaptive SEARCH tree to predict user location. JIPS, 8 (3), 437–444.
   Google Scholar
• Phithakkitnukoon, S., et al., 2010. Activity-aware map: identifying human daily activity pattern using mobile phone data. In: Human behavior understanding, 7 October Vilamoura, Portugal. Berlin: Springer, 14–25.
   Google Scholar
• Quinlan, J.R., 1992. Learning with continuous classes. In: 5th Australian joint conference on artificial intelligence, 16–18 November Hobart. vol. 92. Singapore: World Scientific, 343–348.
   Google Scholar
• Scellato, S., Noulas, A., and Mascolo, C., 2011. Exploiting place features in link prediction on location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, 21–24 August San Diego, CA. New York, NY: ACM, 1046–1054.
   Google Scholar
• Xiong, H., et al., 2012. Predicting mobile phone user locations by exploiting collective behavioral patterns. In: Ubiquitous intelligence & computing and 9th international conference on autonomic & trusted computing (UIC/ATC), 2012 9th international conference on, 4–7 September Fukuoka, Japan. New York, NY: IEEE, 164–171.
   Google Scholar
• Yavaş, G., et al., 2005. A data mining approach for location prediction in mobile environments. Data & Knowledge Engineering, 54 (2), 121–146. doi:10.1016/j.datak.2004.09.004
   Web of Science ®Google Scholar
• Ying, J.J.C., et al., 2011. Semantic trajectory mining for location prediction. In: Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems, 1–4 November Chicago, IL. New York, NY: ACM, 34–43.
   Google Scholar
• Zheng, Y., Xie, X., and Ma, W.Y., 2010. GeoLife: a collaborative social networking service among user, location and trajectory. IEEE Data Engineering Bulletin, 33 (2), 32–39.
   Google Scholar
• Zhou, C., et al., 2004. Discovering personal gazetteers: an interactive clustering approach. In: Proceedings of the 12th annual ACM international workshop on geographic information systems, 8–13 November Washington, DC. New York, NY: ACM, 266–273.
   Google Scholar