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- 1.
Confusingly, ‘generalised entropy’ methods are also widely used in econometrics for the estimation of missing data. Routines which provide this capability, e.g. in SAS, are not helpful in the description of simulation model outputs!
References
Andrienko N, Andrienko G, Gatalsky P (2003) Exploratory spatio-temporal visualisation: an analytical review. J Vis Lang Comput 14(6):503–541
Baird AA et al (2002) Frontal lobe activation during object permanence: data from near-infrared spectroscopy. Neuroimage 16:1120–1126
Batty, M (2006) Rank Clocks. Nature, 444: 592–596
Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang D-U (2006) Complex networks: structure and dynamics. Phys Rep 424(4–5):175–308
Boroditsky L (2001) Does language shape thought? Mandarin and English Speakers’ conceptions of time. Cogn Psychol 43:1–22
Bouvrie JV, Sinha P (2007) Visual object concept discovery: observations in congenitally blind children, and a computational approach. Neurocomputing 70(13–15):2218–2233
Casdagli M (1997) Recurrence plots revisited. Phys D 108(1–2):12–44
Chua HF, Boland JE, Nisbett RE (2005) Cultural variation in eye movements during scene perception. Proc Natl Acad Sci USA 102(35):12629–12633
Clark PJ, Evans FC (1954) Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35(4):445–453
Clement DE, Sistrunk F, Guenther ZC (1970) Pattern perception among Brazilians as a function of pattern uncertainty and age. J Cross Cult Psychol 1(4):305–313
Cleveland WS (1983) Visualising data. Hobart Press, Summit
David N (2013) Validating simulations. Chapter 8 in this volume
Eckmann JP, Kamphorst SO, Reulle D (1987) Recurrence plots of dynamical systems. Europhys Lett 4(9):973–977
Evans AJ (2010) Complex spatial networks in application. Complexity 16(2):11–19
Evans AJ (2012) Uncertainty and error. In: Heppenstall AJ, Crooks AT, See LM, Batty M (eds) Agent-based models of geographical systems. Springer, Berlin, chapter 15
Fisher N, Lewis T, Embleton B (1987) Statistical analysis of spherical data. Cambridge University Press, Cambridge
Fleming L, Sorenson O (2001) Technology as a complex adaptive system: evidence from patent data. Research Policy 30: 1019–1039
Foote J, Cooper M (2001) Visualising music structure and rhythm via self-similarity. In: Proceedings of the international computer music conference, ICMC’01, Havana. ICMA, San Francisco, pp 419–422
Fotheringham AS, Brunsdon C, Charlton M (2002) Geographically weighted regression: the analysis of spatially varying relationships. Wiley, Chichester
Gahegan M (2001) Visual exploration in geography: analysis with light. In: Miller HJ, Han J (eds) Geographic data mining and knowledge discovery. Taylor & Francis, London, pp 260–287
Gehlke CE, Biehl H (1934) Certain effects of grouping upon the size of correlation coefficients in census tract material. J Am Stat Assoc 29(2):169–170
Getis A (2007) Reflections on spatial autocorrelation. Reg Sci Urban Econ 37(4):491–496
Graps A (2004) Amara’s wavelet page. Access date 8 Jun 2011. http://web.archive.org/web/20110608005544/http://www.amara.com/current/wavelet.html
Greenland S, Pearl J (2006) Causal diagrams (Technical report, R-332). UCLA Cognitive Systems Laboratory, Los Angeles. Access date 8 June 2011 http://ftp.cs.ucla.edu/pub/stat_ser/r332.pdf
Grimm V (1999) Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future? Ecol Model 115(2):129–148
Grimm V (2002) Visual debugging: a way of analyzing, understanding, and communicating bottom-up simulation models in ecology. Nat Resour Model 15:23–38
Grimm V et al (2006) A standard protocol for describing individual-based and agent-based models. Ecol Model 198(1–2):115–126
Haining R (1990) Spatial data analysis in the social and environmental sciences. Cambridge University Press, Cambridge
Heppenstall AJ, Evans AJ, Birkin MH (2006) Using hybrid agent-based systems to model spatially-influenced retail markets. J Artif Soc Soc Simul 9(3). http://jasss.soc.surrey.ac.uk/9/3/2.html
Heppenstall AJ, Evans AJ, Birkin MH (2007) Genetic algorithm optimisation of a multi-agent system for simulating a retail market. Environ Plann B 34(6):1051–1070
Hinneburg A, Keim DA, Wawryniuk M (1999) HD-eye: visual mining of high-dimensional data. IEEE Comput Graph Appl 19(5):22–31
Hipp J, Güntzer U, Nakhaeizadeh G (2002) Data mining of association rules and the process of knowledge discovery in databases. In: Perner P (ed) Advances in data mining, vol 2394, Lecture notes in computer science. Springer, Berlin, pp 207–226
Isaaks EH, Srivastava RM (1990) Applied geostatistics. Oxford University Press, Cary
Kantz H, Schreiber T (1997) Non-linear time series analysis. Cambridge University Press, Cambridge
Knudsen DC, Fotheringham AS (1986) Matrix comparison, goodness-of-fit, and spatial interaction modelling. Int Reg Sci Rev 10:127–147
Korie S et al (1998) Analysing maps of dispersal around a single focus. Environ Ecol Stat 5(4): 317���344
Marwan N, Kruths J (2002) Nonlinear analysis of bivariate data with cross recurrence plots. Phys Lett A 302(5–6):299–307
Marwan N, Wessel N, Meyerfeldt U, Schirdewan A, Kurths J (2002) Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. Phys Rev E 66(2):026702
McGarigal K (2002) Landscape pattern metrics. In: El-Shaarawi AH, Piegorsch WW (eds) Encyclopedia of environmentrics, vol 2. Wiley, Chichester, pp 1135–1142
Moon I-C, Schneider M, Carley K (2006) Evolution of player skill in the America’s army game. Simulation 82(11):703–718
Müller W, Schumann HS (2003) Visualisation methods for time-dependent data: an overview. In: Chick S, Sánchez PJ, Ferrin D, Morrice DJ (eds) Proceedings of winter simulation 2003, New Orleans, 7–10 Dec 2003. http://informs-sim.org/wsc03papers/090.pdf
Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45:167–256
Nisbett RE, Masuda T (2003) Culture and point of view. Proc Natl Acad Sci USA 100(19): 11163–11170
OED (2010) Oxford English dictionary. Access date 21 Dec 2010 http://www.oed.com/
Openshaw S, Craft AW, Charlton M, Birch JM (1988) Investigation of leukaemia clusters by use of a geographical analysis machine. Lancet 331(8580):272–273
Orford S, Harris R, Dorling D (1999) Geography: information visualisation in the social sciences. Soc Sci Comput Rev 17(3):289–304
Parry H (2005) Effects of Land Management upon Species Population Dynamics: A Spatially Explicit, Individual-based Model. Unpublished PhD thesis, University of Leeds. pp. 281
Patel A, Hudson-Smith A (2012) Agent tools, techniques and methods for macro and microscopic simulation. In: Heppenstall AJ, Crooks AT, See LM, Batty M (eds) Agent-based models of geographical systems. Springer, Berlin, chapter 18
Pearl J, Verma TS (1991) A theory of inferred causation. In: Allen JA, Fikes R, Sandewall E (eds) Proceedings of the 2nd international conference on principles of knowledge representation and reasoning (KR'91), Cambridge, MA, 22–25 Apr 1991. Morgan Kaufmann, San Mateo, pp 441–452
Pohlheim H (2006) Multidimensional scaling for evolutionary algorithms: visualisation of the path through search space and solution space using Sammon mapping. Artif Life 12(2):203–209
Ramsey JB (2002) Wavelets in economics and finance: past and future. Stud Nonlinear Dynam Econ 6(3):1–27
Roberson D, Davidoff J, Davies IRL, Shapiro LR (2004) The development of color categories in two languages: a longitudinal study. J Exp Psychol Gen 133(4):554–571
Robinson WS (1950) Ecological correlations and the behaviour of individuals. Am Sociol Rev 15: 351–357
Ross AN, Vosper SB (2003) Numerical simulations of stably stratified flow through a mountain pass. Q J R Meteor Soc 129:97–115
Schelling TC (1969) Models of segregation. Am Econ Rev 59(2):88–493
Schreinemachers P, Berger T (2006) Land-use decisions in developing countries and their representation in multi-agent systems. J Land Use Sci 1(1):29–44
Tobler WR (1970) A computer model simulation of urban growth in the Detroit region. Econ Geogr 46(2):234–240
Vasconcelos DB, Lopes SR, Kurths J, Viana RL (2006) Spatial recurrence plots. Phys Rev E 73(5): 056207
Viboud C et al (2006) Synchrony, waves, and spatial hierarchies in the spread of influenza. Science 312(5772):447–451
Wagner HH, Fortin M-J (2005) Spatial analysis of landscapes: concepts and statistics. Ecology 86:1975–1987
Worboys MF (2005) Event-oriented approaches to geographic phenomena. Int J Geogr Info Sci 19(1):1–28
Wyszomirski T, Wyszomirska I, Jarzyna I (1999) Simple mechanisms of size distribution dynamics in crowded and uncrowded virtual monocultures. Ecol Model 115(2–3):253–273
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Further Reading
Further Reading
Statistical techniques for spatial data are reviewed by McGarigal (2002) while for network statistics good starting points are (Newman 2003) and (Boccaletti et al. 2006), with more recent work reviewed by Evans (2010). For information on coping with auto/cross-correlation in spatial data, see (Wagner and Fortin 2005). Patel and Hudson-Smith (2012) provide an overview of the types of simulation tool (virtual worlds and virtual reality) available for visualising the outputs of spatially explicit agent-based models. Evans (2012) provides a review of techniques for analysing error and uncertainty in models, including both environmental/climate models and what they can bring to the agent-based field. He also reviews techniques for identifying the appropriate model form and parameter sets.
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Evans, A., Heppenstall, A., Birkin, M. (2013). Understanding Simulation Results. In: Edmonds, B., Meyer, R. (eds) Simulating Social Complexity. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93813-2_9
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