Abstract
In Chapter 2, we introduced a series of examples of how cartography selects and depicts terrestrial, celestial, and human biological features of physical phenomena. Geographic and oceanographic maps help us to find our way on land and sea. Star maps help us to explore the universe. In this chapter, we turn our attention inwards and explore the design of mind maps, maps that represent our thought, our experience, and our knowledge. In traditional cartography, a thematic map always has a base map and a thematic overlay. For many physical phenomena, a geographic map is probably the best base map we may ever have: intuitive, solid, and real. Now we want to produce a map of the mind. In this category of phenomena, a geographic connection may no longer be valid. A geographic base map cannot be taken for granted. What metaphor do we use to hold something as fluid as our thought together? What are the design principles in constructing a metaphoric base map that can adequately represent what is by its nature invisible, intangible, and intractable?
The eyes are not responsible when the mind does the seeing.
Publilius Syrus (c. 85–43 BC)
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References
Abello, J, Pardalos, PM, and Resende, MGC (1999). On maximum clique problems in very large graphs. In: J Abello and J Vitter (eds), External Memory Algorithms. American Mathematical Society, pp. 119–30.
Albert, A, Jeong, H, and Barabási, A-L (1999). Diameter of the World Wide Web. Nature, 401, 130–1. Albert, R, Jeong, H, and Barabási, A-L (2000). Attack and error tolerance in complex networks.Nature, 406, 378–82.
lbert, R, Jeong, H, and Barabási, A-L (2000). Attack and error tolerance in complex networks.Nature, 406, 378–82.
Barabási, A-L, Albert, R, Jeong, H, and Bianconi, G (2000). Power-law distribution of the World Wide Web. Science, 287, 2115a.
Basalaj, W (2001). Proximity visualization of abstract data. Technical Report 509, University of Cambridge Computer Laboratory, January 2001. http://www.pavis.org/essay/index.html
Batagelj, V, and Mrvar, A (1998). Pajek: a program for large network analysis. Connections, 21(2),47–57.
Biglan, A (1973). The characteristics of subject matter in different academic areas. Journal of Applied Psychology, 57, 195–203.
Borg, I, and Groenen, P (1997). Modern Multidimensional Scaling. New York: Springer-Verlag.
Botafogo, R, Rivlin, E, and Shneiderman, B (1992). Structural analysis of hypertexts: Identifying hierarchies and useful metrics, ACM Transactions on Information Systems, 10(2), 142–180.
Burt, RS (1992). Structural Holes: The Social Structure of Competition. Cambridge, MA: Harvard University Press.
Burt, RS (2002). The social capital of structural holes. In: NF Guillen, R Collins, P England and M Meyer (eds), The New Economic Sociology: Development in an Emerging Field. New York:Russell Sage Foundation.
Bush, V (1945). As we may think. Atlantic Monthly, 176(1), 101–8.
Canter, D, Rivers, R, and Storrs, G (1985). Characterizing user navigation through complex data structures. Behaviour and Information Technology, 4(2), 93–102.
Chen, C, and Czerwinski, M (1997). Spatial ability and visual navigation: an empirical study. New Review of Hypermedia and Multimedia, 3, 67–89.
Collins, AM, and Quillian, MR (1969). Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior, 8, 240–8.
Conklin, J (1987). Hypertext: an introduction and survey. IEEE Computer, 20(9), September, 17–41.
Darken, RP, Allard, T, and Achille, LB (1998). Spatial orientation and wayfinding in large-scale virtual spaces: an introduction. Presence, 7(2), 101–7.
Donoho, D, and Ramos, E (1982). PRIMDATA: Data sets for use with PRIM-H (retrieved 5 November 2001). http://lib.stat.cmu.edu/data-expo/1983.html
Everitt, BS (1980). Cluster Analysis. New York: Halsted Press.
Everitt, BS, and Rabe-Hesketh, S (1997). The Analysis of Proximity Data. London: Arnold.
Granovetter, M (1973). Strength of weak ties. American Journal of Sociology, 8, 1360–80.
Greenacre, MJ (1993). Correspondence Analysis in Practice. San Diego, CA: Academic Press.
Hayes, B (2000a). Graph theory in practice: Part I. American Scientist, 88(1), 9–13.
Hayes, B (2000b). Graph theory in practice: Part II. American Scientist, 88(2), 104–9.
Helm, CE (1964). Multidimensional ratio scaling analysis of perceived color relations. Journal of the Optical Society of America, 54, 256–62.
Ingram, R, and Benford, S (1995). Legibility enhancement for information visualisation. Proceedings of the 6th Annual IEEE Computer Society Conference on Visualization, October 1995, Atlanta, GA,USA, pp. 209–16.
Kleinberg, J (1998). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5),604–32.
Kochen, M (ed.) (1989). The Small World: A Volume of Recent Research Advances Commemorating Ithiel de Sola Pool, Stanley Milgram, Theodore Newcomb. Norwood, NJ: Ablex.
Krumhansl, CL (1978). Concerning the applicability of geometric models to similar data: the interrelationship between similarity and spatial density. Psychological Review, 85(5), 445–63.
Kruskal, JB (1977). The relationship between multidimensional scaling and clustering. In: J van Ryzin (ed.), Classification and Clustering. New York: Academic Press, pp. 17–44.
Kruskal, JB, and Wish, M (1978). Multidimensional Scaling. Beverly Hills, CA: SAGE Publications.
Levine, M, Jankovic, IN, and Palij, M (1982). Principles of spatial problem solving. Journal of Experimental Psychology: General, 111(2), 157–75.
Levine, M, Marchon, I, and Hanley, G (1984). The placement and misplacement of You-Are-Here maps. Environment and Behavior, 16(2), 139–57.
Lynch, K (1960). The Image of the City. Cambridge, MA: MIT Press.
McCallum, RC (1974). Relations between factor analysis and multidimensional scaling. Psychological Bulletin, 81(8), 505–16.
Milgram, S (1967). The small world problem. Psychology Today, 2, 60–7.
Miller, GA (1969). A psychological method to investigate verbal concepts. Journal of Mathematical Psychology, 6, 169–91.
Morris, TA, and McCain, K (1998). The structure of medical informatics journal literature. Journal of the American Medical Informatics Association, 5(5), 448–66.
Rapoport, A, and Horvath, WJ (1961). A study of a large sociogram. Behavioural Science, 6(4), 279–91.
Roweis, ST, and Saul, LK (2000). Nonlinear dimensionality reduction by locally linear embedding.Science, 290(5500), 2323–6.
Small, H (1986). The synthesis of specialty narratives from co-citation clusters. Journal of the American Society for Information Science, 37(3), 97–110.
Small, H (2000). Charting pathways through science: exploring Garfield’s vision of a unified index to science. In: B Cronin and HB Atkins (eds), Web of Knowledge - A Festschrift in Honor of Eugene Garfield. Washington: ASIST, pp. 449–73.
Steyvers, M (2000). Multidimensional scaling. In: Encyclopedia of Cognitive Science. London:Macmillan Reference.
Steyvers, M, and Tenenbaum, J (2001). Small worlds in semantic networks (retrieved December 2001).http://www-psych.stanford.edu/~msteyver/small_worlds.htm
Tenenbaum, JB, Silva, Vd, and Langford, JC (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319–23.
Thorndyke, P, and Hayes-Roth, B (1982). Differences in spatial knowledge acquired from maps and navigation. Cognitive Psychology, 14, 560–89.
Tolman, EC (1948). Cognitive maps in rats and men. Psychological Review, 55, 189–208.
Trochim, W (1989). Concept mapping: soft science or hard art? Evaluation and Program Planning, 12,87–110.
Trochim, W (1993). Reliability of concept mapping. Paper presented at the Annual Conference of the American Evaluation Association, Dallas, Texas. November, 1993. Available at http://trochim.human.cornell.edu/research/reliable/reliable.htm
Trochim, W, and Linton, R (1986). Conceptualization for evaluation and planning. Evaluation and Program Planning, 9, 289–308.
Trochim, W, Cook, J, and Setze, R (1994). Using concept mapping to develop a conceptual framework of staffs views of a supported employment program for persons with severe mental illness.Consulting and Clinical Psychology, 62(4), 766–75.
Tversky, A (1977). Features of similarity. Psychological Review, 84(4), 327–52.
Watts, DJ (1999). Small Worlds: The Dynamics of Networks between Order and Randomness.Princeton, NJ: Princeton University Press.
Watts, DJ, and Strogatz, SJ (1998). Collective dynamics of “small-world” networks. Nature, 393, 440.
Zahn, CT (1917). Graph-theoretical methods for detecting and describing Gestalt clusters. IEEE Transactions on Computers, C20, 68–86.
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Chen, C. (2003). Mapping the Mind. In: Mapping Scientific Frontiers: The Quest for Knowledge Visualization. Springer, London. https://doi.org/10.1007/978-1-4471-0051-5_3
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DOI: https://doi.org/10.1007/978-1-4471-0051-5_3
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