Social Science Inquiry

This social science bibliography is separated into 2 lists:

  1. Inquiry into relevant human factors topics
  2. Valuation and evaluation of visualization

1.  Human Factors Topics:  Resources for exploring (1) why it’s difficult to integrate data at different scales and from different disciplines, (2) the tradeoffs between sophistication and simplicity (interpretability), (3) visualization techniques that policymakers or stakeholders can understand.

  • Foer, J. (2011). Moonwalking with Einstein: the art and science of remembering everything. New York: Penguin Press.
  • Giorgi, F., & Mearns, L. O. (1999). Introduction to special section: Regional climate modeling revisited. Journal of Geophysical Research, 104(D6), 6335-6352. doi: 10.1029/98jd02072
  • Johnson-Laird, P. N. (1983). Mental models: towards a cognitive science of language, inference, and consciousness (Vol. 6). Cambridge, Mass: Harvard University Press.
  • Kahan, D. (2010). Fixing the communications failure. Nature, 463(7279), 296-297. doi: 10.1038/463296a
  • Kahan, D. (2012). Why we are poles apart on climate change. Nature, 488(7411), 255.
  • Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus and Giroux.
  • Keim, D., Andrienko, G., Fekete, J. D., Görg, C., Kohlhammer, J., & Melançon, G. (2008). Visual analytics: Definition, process, and challenges. Information Visualization, 154-175.
  • Keim, D., Mansmann, F., & Thomas, J. (2010). Visual analytics: how much visualization and how much analytics? ACM SIGKDD Explorations Newsletter, 11(2), 5-8. doi: 10.1145/1809400.1809403
  • Moreland, K. (2012). A survey of visualization pipeline. IEEE Transactions on visualizations and computer graphics, PP(99).
  • Morgan, M. G., & Morgan, M. G. (2002). Risk communication: a mental models approach. Cambridge: Cambridge University Press.
  • Norman, D. A. (2002). The design of everyday things. New York: Basic Books.
  • Ohmann, J. L., Gregory, M. J., Henderson, E. B., & Roberts, H. M. (2011). Mapping gradients of community composition with nearest-neighbour imputation: extending plot data for landscape analysis. Journal of Vegetation Science, 22(4), 660-676. doi: 10.1111/j.1654-1103.2010.01244.x
  • Olson, R. (2009). Don’t be such a scientist: talking substance in an age of style. Washington, DC: Island Press.
  • Peters, D. P. C., Groffman, P. M., Nadelhoffer, K. J., Grimm, N. B., Collins, S. L., Michener, W. K., & Huston, M. A. (2008). Living in an Increasingly Connected World: A Framework for Continental-Scale Environmental Science. Frontiers in Ecology and the Environment, 6(5), 229-237. doi: 10.1890/070098
  • Robertson, G., Czerwinski, M., Larson, K., Robbins, D., Thiel, D., & van Dantzich, M. (1998, 1998). Data mountain: using spatial memory for document management.
  • Rogowitz, B. (2010). Theory of Visualization Panel: Visual Perspectives. Paper presented at the IEEE Visualization, University of Texas, Austin.
  • Silverman, D. (2006). Interpreting Qualitative Data, 3rd Edition: SAGE publications.
  • Watternberg, M. (2008). Visualizing Big Data: Bar Charts for Words  Retrieved November 10, 2012.
  • Wilby, R. L. (1997). Non-stationarity in daily precipitation series: implications for
  • GCM down-scaling using atmospheric circulation indices. International Journal of Climatology, 17(4), 439-454.
  • Wilby, R. L., Dawson, C. W., & Barrow, E. M. (2002). sdsm — a decision support tool for the assessment of regional climate change impacts. Environmental Modelling and Software, 17(2), 145-157. doi: 10.1016/s1364-8152(01)00060-3

2.  Valuation and evaluation of visualization:  Resources for exploring (1) the value in using social science for visualization and related software development, (2) existing visualization evaluation methods, (3) evaluation methods during development.

  • Chi, M. T. H., Glaser, R., & Rees, E. (1982). Expertise in Problem Solving. Advances in the Pshycology of Human Intelligence, 1.
  • DeSanctis, G., & Poole, M. S. (1994). Capturing the Complexity in Advanced Technology Use: Adaptive Structuration Theory. Organization Science, 5(2), 121-147. doi: 10.1287/orsc.5.2.121
  • Forsell, C., & Cooper, M. (2012). A guide to reporting scientific evaluation in visualization.
  • Healey, C. G., & Enns, J. T. (2012, 2012). Attention and Visual Memory in Visualization and Computer Graphics, United States.
  • Julesz, B. (1978). Imaging in 3-dimensions: Three dimensional imaging techniques. Proceedings of the IEEE, 66(3), 365-366. doi: 10.1109/proc.1978.10924
  • Julesz, B. (1980). Spatial Nonlinearities in the Instantaneous Perception of Textures with Identical Power Spectra. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 290(1038), 83-94.
  • Julesz, B. (1991). Early vision and focal attention. Reviews of Modern Physics, 63(3), 735-772. doi: 10.1103/RevModPhys.63.735
  • Julesz, B. (1995). Dialogues on perception. Cambridge, Mass: MIT Press.
  • Keim, D., Andrienko, G., Fekete, J. D., Görg, C., Kohlhammer, J., & Melançon, G. (2008). Visual analytics: Definition, process, and challenges. Information Visualization, 154-175.
  • Keim, D., Mansmann, F., & Thomas, J. (2010). Visual analytics: how much visualization and how much analytics? ACM SIGKDD Explorations Newsletter, 11(2), 5-8. doi: 10.1145/1809400.1809403
  • Moreland, K. (2012). A survey of visualization pipeline. IEEE Transactions on visualizations and computer graphics, PP(99).
  • Plaisant, C., Görg, C., Liu, Z., Parekh, N., Singhal, K., Stasko, J., . . . Wright, W. (2008). Evaluating visual analytics at the 2007 VAST Symposium contest. Ieee Computer Graphics and Applications, 28(2), 12-21. doi: 10.1109/mcg.2008.27
  • Rogowitz, B. (2010). Theory of Visualization Panel: Visual Perspectives. Paper presented at the IEEE Visualization, University of Texas, Austin.
  • Scholtz, J. (2010, 2010). Developing qualitative metrics for visual analytic environments. Paper presented at the IEEE, BELIV ’10.
  • Thomas, J., & Cook, K. e. (2005). Illuminating the Path: the Research and Development Agenda for Visual Analytics. IEEE.
  • Thomas, J. J., & Cook, K. A. (2006). A visual analytics agenda. Ieee Computer Graphics and Applications, 26(1), 10-13. doi: 10.1109/mcg.2006.5
  • Treisman, A. (1998). Feature binding, attention and object perception. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 353(1373), 1295-1306. doi: 10.1098/rstb.1998.0284
  • Treisman, A. (2006). How the deployment of attention determines what we see. Visual Cognition, 14(8), 411-443. doi: 10.1080/13506280500195250
  • Wolfe, J. M. (1996). Modifying guided search: Preattentive object files. Canadian Psychology Psychologie Canadienne, 37(1), 60-60. doi: 10.1037/h0084731
  • Wolfe, J. M., Cave, K. R., & Franzel, S. L. (1989). Guided search: an alternative to the feature integration model for visual search. Journal of experimental psychology. Human perception and performance, 15(3), 419-433. doi: 10.1037/0096-1523.15.3.419
  • Zhicheng, L., & Stasko, J. T. (2010). Mental Models, Visual Reasoning and Interaction in Information Visualization: A Top-down Perspective, United States.
  • Zhu, Y. (2007). Measuring Effective Data Visualization
  • Advances in Visual Computing. In G. Bebis, R. Boyle, B. Parvin, D. Koracin, N. Paragios, S.-M. Tanveer, T. Ju, Z. Liu, S. Coquillart, C. Cruz-Neira, T. Müller & T. Malzbender (Eds.), (Vol. 4842, pp. 652-661): Springer Berlin / Heidelberg.
  • Zudilova-Seinstra, E. v., Adriaansen, T., Liere, R. v., Adriaansen, T., Liere, R. v., & Zudilova-Seinstra, E. v. (2009). Trends in interactive visualization: state-of-the-art survey. London: Springer.