Page 229 -

226 A. Evans et al.

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, Cuba (pp.
419–422). San Francisco: ICMA.
Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression:
The analysis of spatially varying relationships. Chichester: Wiley.
Gahegan, M. (2001). Visual exploration in geography: Analysis with light. In H. J. Miller & J. Han
(Eds.), Geographic data mining and knowledge discovery (pp. 260–287). London: Taylor &
Francis.
Gehlke, C. E., & Biehl, H. (1934). Certain effects of grouping upon the size of correla-
tion coefficients in census tract material. Journal of the American Statistical Association,
29(Supplement), 169–170.
Getis, A. (2007). Reflections on spatial autocorrelation. Regional Science and Urban Economics,
37(4), 491–496.
Granger, C. W. J. (1980). Testing for causality: A personal viewpoint. Journal of Economic
Dynamics and Control, 2, 329–352.
Graps, A. (2004). Amara’s wavelet page. http://www.amara.com/current/wavelet.html
Greenland, S., & Pearl, J. (2006). Causal diagrams (Technical report, R-332). Los Angeles: UCLA
Cognitive Systems Laboratory. 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? Ecological Modelling, 115(2), 129–148.
Grimm, V. (2002). Visual debugging: A way of analyzing, understanding, and communicating
bottom-up simulation models in ecology. Natural Resource Modelling, 15, 23–38.
Grimm, V., et al. (2006). A standard protocol for describing individual-based and agent-based
models. Ecological Modelling, 198(1–2), 115–126.
Haining, R. (1990). Spatial data analysis in the social and environmental sciences. Cambridge:
Cambridge University Press.
Heppenstall, A. J., Evans, A. J., & Birkin, M. H. (2006). Using hybrid agent-based systems to
model spatially-influenced retail markets. Journal of Artificial Societies and Social Simulation,
9(3). http://jasss.soc.surrey.ac.uk/9/3/2.html
Heppenstall, A. J., Evans, A. J., & Birkin, M. H. (2007). Genetic algorithm optimisation of a multi-
agent system for simulating a retail market. Environment and Planning B: Urban Analytics and
City Science, 34(6), 1051–1070.
Hinneburg, A., Keim, D. A., & Wawryniuk, M. (1999). HD-eye: Visual mining of high-
dimensional data. IEEE Computer Graphics and Applications, 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 P. Perner (Ed.), Advances in data mining. (Lecture
Notes in Computer Science, 2394) (pp. 207–226). Berlin: Springer.
Isaaks, E. H., & Srivastava, R. M. (1990). Applied geostatistics. North Carolina: Oxford University
Press USA.
Kantz, H., & Schreiber, T. (1997). Non-linear time series analysis. Cambridge: Cambridge
University Press.
Knudsen, D. C., & Fotheringham, A. S. (1986). Matrix comparison, goodness-of-fit, and spatial
interaction modelling. International Regional Science Review, 10, 127–147.
Korie, S., et al. (1998). Analysing maps of dispersal around a single focus. Environmental and
Ecological Statistics, 5(4), 317–344.
Marwan, N., & Kruths, J. (2002). Nonlinear analysis of bivariate data with cross recurrence plots.
Physics Letters 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. Physical
Review E, 66(2), 026702.
McGarigal, K. (2002). Landscape pattern metrics. In A. H. El-Shaarawi & W. W. Piegorsch (Eds.),
Encyclopedia of environmentrics (Vol. 2, pp. 1135–1142). Chichester: Wiley.