Advertisement

The Opportunities and Challenges with Social Media and Big Data for Research in Human Dynamics

  • Atsushi Nara
  • Ming-Hsiang Tsou
  • Jiue-An Yang
  • Cheng-Chia Huang
Chapter
Part of the Human Dynamics in Smart Cities book series (HDSC)

Abstract

Geographers and scientists can collect and analyze social media and Big Data via smartphones, sensors, and mobile devices with locational contents, such as global positioning system tags, check-ins, place names, and user location profiles. The dynamic characteristics of social media and Big Data offer geographers research opportunities for examining and modeling human behaviors, communications, and movements. To discuss this emerging research themes in the field of geography and GIScience, a series of special paper sessions were organized at AAG annual meetings in 2015 and 2016, Human Dynamics in the Mobile Age: Linking Physical and Virtual Spaces and Symposium on Human Dynamics Research: Social Media and Big Data. This short viewpoint paper first reports on a summary of papers presented in these AAG sessions. Then we discuss the current state-of-the-arts in human dynamics research and highlight their key concepts, opportunities, and challenges.

Notes

Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant No. 1634641, IMEE project titled “Integrated Stage-Based Evacuation with Social Perception Analysis and Dynamic Population Estimation” and Grant No. 1416509, IBSS project titled “Spatiotemporal Modeling of Human Dynamics Across Social Media and Social Networks”. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.

References

  1. Aggarwal, C. C., & Zhai, C. (2012). A survey of text clustering algorithms. In C. C. Aggarwal & C. Zhai (Eds.), Mining text data (pp. 77–128). Boston, MA: Springer.CrossRefGoogle Scholar
  2. An, L., Zvoleff, A., Liu, J., & Axinn, W. (2014). Agent-Based modeling in Coupled Human and Natural Systems (CHANS): Lessons from a comparative analysis. Annals of the Association of American Geographers, 104, 723–745.CrossRefGoogle Scholar
  3. Andris, C. (2016). Integrating social network data into GISystems. International Journal of Geographical Information Science, 30, 2009–2031.Google Scholar
  4. Allen, C., Tsou, M.-H., Aslam, A., Nagel, A., & Gawron, J.-M. (2016). Applying GIS and Machine learning methods to Twitter data for multiscale surveillance of influenza. PLoS ONE, 11, e0157734.CrossRefGoogle Scholar
  5. Boy, J. D., & Uitermark, J. (2016). How to study the city on Instagram. PLoS ONE, 11, e0158161.CrossRefGoogle Scholar
  6. Brockmann, D., & Helbing, D. (2013). The hidden geometry of complex, network-driven contagion phenomena. Science, 342, 1337–1342.CrossRefGoogle Scholar
  7. Crooks, A. T., Croitoru, A., Jenkins, A., Mahabir, R., Agouris, P., & Stefanidis, A. (2016). User-generated big data and urban morphology. Built Environment, 42, 396–414.CrossRefGoogle Scholar
  8. De Longueville, B., Smith, R. S., & Luraschi, G. (2009). “OMG, from Here, I Can See the Flames!”: A use case of mining location based social networks to acquire spatio-temporal data on forest fires. In Proceedings of 2009 International Workshop Location Based Social Networks ACM (pp. 73–80). New York, NY, USA.Google Scholar
  9. Dodge, S., Laube, P., & Weibel, R. (2012). Movement similarity assessment using symbolic representation of trajectories. International Journal of Geographical Information Science, 26, 1563–1588.CrossRefGoogle Scholar
  10. Doreian, P., & Conti, N. (2012). Social context, spatial structure and social network structure. Social Networks, 34, 32–46.CrossRefGoogle Scholar
  11. Fischer, E. (2014). Making the most detailed tweet map ever. In: Mapbox. https://www.mapbox.com/blog/twitter-map-every-tweet/. Accessed 15 October 2016.
  12. Ghosh, D., & Guha, R. (2013). What are we “tweeting” about obesity? Mapping tweets with topic modeling and Geographic Information System. Cartography and Geographic Information Science, 40, 90–102.CrossRefGoogle Scholar
  13. Helbich, M., Hagenauer, J., Leitner, M., & Edwards, R. (2013). Exploration of unstructured narrative crime reports: An unsupervised neural network and point pattern analysis approach. Cartography and Geographic Information Science, 40, 326–336.CrossRefGoogle Scholar
  14. Heppenstall, A. J., Crooks, A. T., See, L. M., & Batty, M. (Eds.). (2012). Agent-Based models of geographical systems. Netherlands, Dordrecht: Springer.Google Scholar
  15. Hristova, D., Williams, M. J., Musolesi, M., Panzarasa, P., & Mascolo, C. (2016). Measuring urban social diversity using interconnected geo-social networks. In: Proceedings 25th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee (pp. 21–30). Republic and Canton of Geneva, Switzerland.Google Scholar
  16. Instagram. (2016). Instagram developer API. https://www.instagram.com/developer/. Accessed 23 August 2016.
  17. Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1, 2053951714528481.CrossRefGoogle Scholar
  18. Kwan, M.-P. (2016). Algorithmic geographies: Big data, algorithmic uncertainty, and the production of geographic knowledge. American Association of Geographers Annals, 106, 274–282.Google Scholar
  19. Lansley, G., & Longley, P. A. (2016). The geography of Twitter topics in London. Computers, Environment and Urban Systems, 58, 85–96.CrossRefGoogle Scholar
  20. Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google flu: Traps in big data analysis. Science, 343, 1203–1205.CrossRefGoogle Scholar
  21. Longley, P. A., Adnan, M., & Lansley, G. (2015). The geotemporal demographics of Twitter usage. Environment and Planning, 47, 465–484.CrossRefGoogle Scholar
  22. Malleson, N., & Andresen, M. A. (2015). The impact of using social media data in crime rate calculations: Shifting hot spots and changing spatial patterns. Cartography and Geographic Information Science, 42, 112–121.CrossRefGoogle Scholar
  23. Miller, H. J., Tribby, C. P., Brown, B. B., Smith, K. R., Werner, C. M., Wolf, J., et al. (2015). Public transit generates new physical activity: Evidence from individual GPS and accelerometer data before and after light rail construction in a neighborhood of Salt Lake City, Utah, USA. Health Place, 36, 8–17.CrossRefGoogle Scholar
  24. Mitchell, L., Frank, M. R., Harris, K. D., Dodds, P. S., & Danforth, C. M. (2013). The geography of happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place. PLoS ONE, 8, e64417.CrossRefGoogle Scholar
  25. Nagel, A. C., Tsou, M.-H., Spitzberg, B. H., et al. (2013). The complex relationship of Realspace events and messages in cyberspace: Case study of influenza and pertussis using tweets. Journal of Medical Internet Research, 15, e237.CrossRefGoogle Scholar
  26. Nara, A., Allen, C., & Izumi, K. (2017). Surgical phase recognition using movement data from video imagery and location sensor data. In D. A. Griffith, Y. Chun, & D. J. Dean (Eds.), Advances in Geocomputation (pp. 229–237). Berlin: Springer International Publishing.CrossRefGoogle Scholar
  27. Niedzielski, M. A., O’Kelly, M. E., & Boschmann, E. E. (2015). Synthesizing spatial interaction data for social science research: Validation and an investigation of spatial mismatch in Wichita, Kansas. Computers, Environment and Urban Systems, 54, 204–218.CrossRefGoogle Scholar
  28. Shaw, S.-L., Tsou, M.-H., & Ye, X. (2016). Editorial: Human dynamics in the mobile and big data era. International Journal of Geographical Information Science, 30, 1687–1693.CrossRefGoogle Scholar
  29. Shaw, S.-L., & Yu, H. (2009). A GIS-based time-geographic approach of studying individual activities and interactions in a hybrid physical–virtual space. Journal of Transport Geography, 17, 141–149.CrossRefGoogle Scholar
  30. Sui, D., & Goodchild, M. (2011). The convergence of GIS and social media: Challenges for GIScience. International Journal of Geographical Information Science, 25, 1737–1748.CrossRefGoogle Scholar
  31. Torrens, P. M. (2015). Intertwining agents and environments. Environmental Earth Sciences, 74, 7117–7131.CrossRefGoogle Scholar
  32. Torrens, P. M. (2016). Computational streetscapes. Computation, 4, 37.CrossRefGoogle Scholar
  33. Torrens, P. M., Nara, A., Li, X., Zhu, H., Griffin, W. A., & Brown, S. B. (2012). An extensible simulation environment and movement metrics for testing walking behavior in agent-based models. Computers, Environment and Urban Systems, 36, 1–17.CrossRefGoogle Scholar
  34. Tsou, M.-H. (2015). Research challenges and opportunities in mapping social media and big data. Cartography and Geographic Information Science, 42, 70–74.CrossRefGoogle Scholar
  35. Tsou, M.-H., Yang, J.-A., Lusher, D., Han, S., Spitzberg, B., Gawron, J. M., et al. (2013). Mapping social activities and concepts with social media (Twitter) and web search engines (Yahoo and Bing): A case study in 2012 US Presidential Election. Cartography and Geographic Information Science, 40, 337–348.CrossRefGoogle Scholar
  36. Wang, Z., Ye, X., & Tsou, M.-H. (2016). Spatial, temporal, and content analysis of Twitter for wildfire hazards. Natural Hazards, 83, 523–540.CrossRefGoogle Scholar
  37. Yang, J.-A., Tsou, M.-H., Jung, C.-T., Allen, C., Spitzberg, B. H., Gawron, J. M., et al. (2016). Social media analytics and research testbed (SMART): Exploring spatiotemporal patterns of human dynamics with geo-targeted social media messages. Big Data & Society, 3, 2053951716652914.CrossRefGoogle Scholar
  38. Yuan, M., & Nara, A. (2015). Space-time analytics of tracks for the understanding of patterns of life. Space-Time Integration Geography and GIScience, 373–398.Google Scholar
  39. Yuan, M., Nara, A., & Bothwell, J. (2014). Space–time representation and analytics. Annals of GIS, 20, 1–9.CrossRefGoogle Scholar
  40. Zhao, Z., Shaw, S.-L., Xu, Y., Lu, F., Chen, J., & Yin, L. (2016). Understanding the bias of call detail records in human mobility research. International Journal of Geographical Information Science, 30, 1738–1762.CrossRefGoogle Scholar
  41. Zhao, B., & Sui, D. (2017). True lies in geospatial big data: Detecting location spoofing in social media. Annals of GIS, 1–14.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Atsushi Nara
    • 1
  • Ming-Hsiang Tsou
    • 1
  • Jiue-An Yang
    • 2
  • Cheng-Chia Huang
    • 1
  1. 1.Department of GeographySan Diego State UniversitySan DiegoUSA
  2. 2.Qualcomm InstituteUniversity of CaliforniaSan DiegoUSA

Personalised recommendations