These references contain some of the algorithms implemented in GeoDa.
Dorling, D. (1996). Area Cartograms: Their Use and Creation. CATMOG 59. Institute of British Geographers.
Worboys, M.F. and Duckham, M. (2004). GIS: A Computing Perspective. 2nd edition. CRC Press.
Smoothing, Standardization, and Excess Risk
Anselin, L., Y. W. Kim and I. Syabri. Web-Based Analytical Tools for the Exploration of Spatial Data Journal of Geographical Systems (forthcoming). For more details on EB smoother, also see Bailey, T.C. and Gatrell, A. C. (1995). Interactive Spatial Data Analysis. John Wiley and Sons, New York, NY (pp. 303-308).
Although these references do not refer to the spatial EB smoother, it is computed the same way as the regular EB smoother except that the mean and variance of the prior are taken from a local subset (as defined by the weights) rather than the study region as a whole.
Assuncao, R. and Reis, E. A. (1999). A new proposal to adjust Moran?s I for population density. Statistics in Medicine, 18:2147-2161.
- Empirical Bayes Smoothing and Excess Risk Empirical Bayes Standardization
Anselin, L. (1996). The Moran scatterplot as an ESDA tool to assess local instability in spatial association. In Fischer, M., Scholten, H., and Unwin, D., editors, Spatial Analytical Perspectives on GIS in Environmental and Socio-Economic Sciences, pages 111-125. Taylor and Francis, London.
Local Indicators of Spatial Association (LISA)
Anselin, L. (1995). Local indicators of spatial association - LISA. Geographical Analysis, 27:93-115.
Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 22-23.
Smirnov, O. and Anselin, L. (2001). Fast maximum likelihood estimation of very large spatial autoregressive models: A characteristic polynomial approach. Computational Statistics and Data Analysis, 35:301-319.
Mount, D. and S. Arya. ANN Approximate Nearest Neighbors (Version 0.2; 1998). See Appendix B - ANN License Agreement of the GeoDa 0.9.3 User's Guide.