![]() Global Ecology & Biogeography, 12:53–64.ĭiniz-Filho, J. A., Hawkins, B. A., Bini, L. M., De Marco Jr., P., and Blackburn, T. M. Spatial autocorrelation and red herrings in geographical ecology. John Wiley & Sons, New York.ĭiniz-Filho, J. A., Bini, L. M., and Hawkins, B. A. Statistics for Spatial Data, Revised Edition. Exploring Geographic Information Systems. Oxford University Press, Oxford.Ĭhambers, J. M. Principles of Geographical Information Systems. Journal of Urban Economics, 54:199–217.īurrough, P. A. In search of yardstick competition: a spatial analysis of Italian municipality property tax setting. Economics Letters, 55:257–265.īordignon, M., Cerniglia, F., and Revelli, F. Spatial dependence through local yardstick competition: theory and testing. Journal of Regional Science, 48:1–27.īivand, R. S. Implementing representations of space in economic geography. Red herrings remain in geographical ecology: a reply to Hawkins et al. Environmental Modelling and Software, 39:116–134.īeale, C. M., Lennon, J. J., Elston, D. A., Brewer, M. J., and Yearsley, J. M. Managing uncertainty in integrated environmental modelling: The uncertweb framework. Chapman & Hall/CRC, Boca Raton/ London.īastin, L., Cornford, D., Jones, R., Heuvelink, G. B., Pebesma, E., Stasch, C., Nativi, S., Mazzetti, P., and Williams, M. Hierarchical Modeling and Analysis for Spatial Data. Longman, Harlow.īanerjee, S., Carlin, B. P., and Gelfand, A. E. This process is experimental and the keywords may be updated as the learning algorithm improves.īailey, T. C. These keywords were added by machine and not by the authors. Statistical inference for such spatial processes is often challenging, but is necessary when we try to draw conclusions about questions that interest us. These questions refer to hypothetical processes that generate the observed data. Beyond creating and viewing maps, spatial data analysis is concerned with questions not directly answered by looking at the data themselves. ![]() Making a map that is suited to its purpose and does not distort the underlying data unnecessarily is however not easy. Besides those we collect ourselves (‘is it raining?’), they confront us on television, in newspapers, on route planners, on computer screens, on mobile devices, and on plain paper maps. Spatial and spatio-temporal data are everywhere. ![]()
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