Skip to main content Accessibility help
×
Hostname: page-component-76fb5796d-qxdb6 Total loading time: 0 Render date: 2024-04-25T13:57:34.183Z Has data issue: false hasContentIssue false

References

Published online by Cambridge University Press:  06 July 2010

Robert Haining
Affiliation:
University of Cambridge
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Spatial Data Analysis
Theory and Practice
, pp. 394 - 423
Publisher: Cambridge University Press
Print publication year: 2003

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Acheson, D. (1998). Report of the Independent Enquiry into Inequalities in Health. London: HMSO
Aitkin, M. and Longford, N. (1986). Statistical modelling issues in school effectiveness studies (with discussion). Journal Royal Statistical Society, A, 149, 1–43CrossRefGoogle Scholar
Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. Second International Symposium on Information Theory, eds. Petrov, B. N. and Csáki, F., pp. 267–81. Budapest: Akadémiai Kiadó
Alvanides, S. and Openshaw, S. (2000). Zone design for planning and policy analysis. Geographical Information and Planning: European Perspectives, eds. Geertman, S., Openshaw, S. and Stillwell, J., pp. 299–315. Berlin: Springer-Verlag
Anas, A. and Eum, S. J. (1984). Hedonic analysis of a housing market in disequilibrium. Journal of Urban Economics, 15, 87–106CrossRefGoogle Scholar
Andrienko, G. L. and Andrienko, N. V. (1999). Interactive maps for visual data exploration. International Journal of Geographical Information Science, 13, 355–74CrossRefGoogle Scholar
Andrienko, G. and Andrienko, N. (2001). Exploring spatial data with dominant attribute map and parallel co-ordinates. Computers, Environment and Urban Systems, 25, 5–15CrossRefGoogle Scholar
Anselin, L.(1988). Spatial Econometrics: Methods and Models. Dordrecht: Kluwer Academic
Anselin, L. (1995). Local indicators of spatial association – LISA. Geographical Analysis, 27, 93–115CrossRefGoogle Scholar
Anselin, L. (1996). The Moran scatterplot as an ESDA tool to assess local instability in spatial association. Spatial Analytical Perspectives on GIS, eds. Fischer, M., Scholten, H. J. and Unwin, D., pp. 111–25. London: Taylor & Francis
Anselin, L. (1998). Exploratory spatial data analysis in a geocomputational environment. Geocomputation: A Primer, eds. Longley, P. A., Brooks, S. M., McDonnell, R. and Macmillan, W., pp. 77–84. New York: Wiley
Anselin, L. and Bao, S. (1997). Exploratory spatial data analysis linking Space Stat and Arc View. Recent Developments in Spatial Analysis, eds. Fischer, M. and Getis, A., pp. 35–59. Berlin: Springer-VerlagCrossRef
Anselin, L., Dodson, R. and Hudak, S. (1993). Linking GIS and spatial data analysis in practice. Geographical Systems, 1, 3–23Google Scholar
Anselin, L. and Rey, S. J. (1991). Properties of tests for spatial dependence in linear regression models. Geographical Analysis, 23, 112–31CrossRefGoogle Scholar
Anselin, L. and Smirnov, O. (1996). Efficient algorithms for constructing proper higher order spatial lag operators. Journal of Regional Science, 36, 67–89CrossRefGoogle Scholar
Arbia, G. (1989). Spatial Data Configuration in Statistical Analysis of Regional Economic and Related Problems. Dordrecht: Kluwer
Arbia, G., Benedetti, R. and Espa, G. (1996). Effects of the MAUP on image classification. Geographical Systems, 3, 123–41Google Scholar
Arbia, G., Griffith, D. A. and Haining, R. P. (1998). Error propagation modelling in raster GIS: overlay operations. International Journal of Geographical Information Science, 12, 145–67CrossRefGoogle Scholar
Arbia, G., Griffith, D. A. and Haining, R. P. (1999). Error propagation modelling in raster GIS: adding and ratioing operations. Cartography and Geographic Information Science, 26, 297–315CrossRefGoogle Scholar
Arbia, G., Griffith, D. and Haining, R. P. (2003). Spatial error propagation when computing linear combinations of spectral bands: the case of vegetation indices. Environmental and Ecological Statistics, 10 (to appear)
Arbia, G. and Lafratta, G. (1997). Evaluating and updating the sample design in repeated environmental surveys: monitoring air quality in Padua. Journal of Agricultural, Biological and Environmental Statistics, 2, 451–66CrossRefGoogle Scholar
Armstrong, B. (2001). Comments on the papers by Guthrie, Sheppard and Best et al., Chambers, Steel and Darby et al. Journal of the Royal Statistical Society, A, 164, 205–7CrossRefGoogle Scholar
Armstrong, H. W. and Taylor, J. (2000). Regional Economics and Policy. New York: Harvester Wheatsheaf
Asimov, D. (1985). The Grand Tour: a tool for viewing multi-dimensional data. SIAM Journal on Scientific and Statistical Computing, 6, 128–43CrossRefGoogle Scholar
Assunção, and Reis, E. A. (1999). A new proposal to adjust Moran's I for population density. Statistics in Medicine, 18, 2147–623.0.CO;2-I>CrossRefGoogle ScholarPubMed
Augustin, N. H., Mugglestone, M. A. and Buckland, S. T. (1996). An autologistic model for the spatial distribution of wildlife. Journal of Applied Ecology, 33, 339–47CrossRefGoogle Scholar
Bailey, N. T. J. (1967). The simulation of stochastic epidemics in two dimensions. Proceedings, Fifth Berkeley Symposium on Mathematics and Statistics, 4, 237–57. Berkeley and Los Angeles, CA: University of California
Bailey, N. T. J. (1975). The Mathematical Theory of Infectious Diseases and Its Applications. London: Charles Griffin & Co
Bailey, T. C. and Gatrell, A. C. (1995). Interactive Spatial Data Analysis. Harlow: Longman
Bao, S. and Henry, M. (1996). Heterogeneity issues in local measurements of spatial association. Geographical Systems, 3, 1–14Google Scholar
Barcelo, J. A. and Pallares, M. (1998). Beyond GIS: the archaeology of social spaces. Archeologia e Calcolatori, 9, 47–80Google Scholar
Barro, R. J. and Sala-i-Martin, X. (1995). Economic Growth. New York: McGraw-Hill
Bartlett, M. S. (1935). Some aspects of the time correlation problem in regard to tests of significance. Journal Royal Statistical Society, 98, 536–43CrossRefGoogle Scholar
Bartlett, M. S. (1957). Measles periodicity and community size. Journal of the Royal Statistical Society, A, 120, 48–70CrossRefGoogle Scholar
Bartlett, M. S. (1960). The critical community size for measles in the United States. Journal of the Royal Statistical Society, A, 123, 37–44CrossRefGoogle Scholar
Batty, M. (1998). Urban evolution on the desktop: simulation with the use of extended cellular automata. Environment and Planning, A, 30, 1943–67CrossRefGoogle Scholar
Baumol, W. J. (1994). Multivariate growth patterns: contagion and common forces as possible sources of convergence. Convergence of Productivity – Cross National Studies and Historical Evidence, eds. Baumol, W. J., Nelson, R. R. and Wolff, E. N., pp. 62–85. New York: Oxford University Press
Bavaud, F. (1998). Models for spatial weights: a systematic look. Geographical Analysis, 30, 153–71CrossRefGoogle Scholar
Becker, N. G. (1989). Analysis of Infectious Disease Data: Monographs on Statistics and Applied Probability. London: Chapman & Hall
Becker, R. A., Cleveland, W. S. and Shyu, M-J. (1996). The visual design and control of trellis display. Journal of Computational and Graphical Statistics, 5, 123–55Google Scholar
Becker, R. A., Cleveland, W. S. and Wilks, A. R. (1987). Dynamic graphics for data analysis. Statistical Science, 2, 355–95CrossRefGoogle Scholar
Belsley, D. A., Kuh, E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley
Benenson, I. (1998). Multi-agent simulations of residential dynamics in the city. Computing, Environment and Urban Systems, 22, 25–42CrossRefGoogle Scholar
Bernardinelli, L., Clayton, D. and Montomoli, C. (1995). Bayesian estimates of disease maps: how important are priors?Statistics in Medicine, 14, 2411–31CrossRefGoogle ScholarPubMed
Bernardinelli, L. and Montomoli, C. (1992). Empirical Bayes versus fully Bayesian analysis of geographical variation in disease risk. Statistics in Medicine, 11, 983–1007CrossRefGoogle ScholarPubMed
Bernstein, R., Lotspiech, J. B., Meyers, H. J., Kolsky, H. G. and Lees, R. D. (1984). Analysis and processing of LANDSAT-4 sensor data using advanced image processing techniques and technologies. IEEE Transactions on Geoscience and Remote Sensing, GE-22, 192–221CrossRefGoogle Scholar
Berry, B. J. L. (1966). Essays on commodity flows and the spatial structure of the Indian economy. Research Paper No. 111, Department of Geography, University of Chicago, 334 pp
Berry, B. J. L. and Marble, D. F. (1968). Spatial Analysis. Englewood Cliffs, NJ: Prentice Hall
Bertin, J. (1983). Semiology of Graphics: Diagrams, Networks, Maps. Madison, University of Wisconsin Press (First published 1967)
Besag, J. E. (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society, B, 36, 192–225Google Scholar
Besag, J. E. (1975). Statistical analysis of non-lattice data. The Statistician, 24, 179–95CrossRefGoogle Scholar
Besag, J. E. (1977). Errors in variables estimation for Gaussian lattice schemes. Journal of the Royal Statistical Society, B, 39, 73–8Google Scholar
Besag, J. E. (1978). Some methods of statistical analysis for spatial data. Bulletin of the International Statistical Institute, 47, 77–92Google Scholar
Besag, J. E. (1986). On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society, B, 48, 259–302Google Scholar
Besag, J. and Clifford, P. (1989). Generalized Monte Carlo significance tests. Biometrika, 76, 633–42CrossRefGoogle Scholar
Besag, J. and Kooperberg, C. (1995). On conditional and intrinsic autoregressions. Biometrics, 82, 733–46Google Scholar
Besag, J. and Newell, J. (1991). The detection of clusters in rare diseases. Journal of the Royal Statistical Society, A, 154, 143–55CrossRefGoogle Scholar
Besag, J. E., York, J. and Mollié, A. (1991). Bayesian image restoration with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43, 1–21CrossRefGoogle Scholar
Bierkens, M. F. P, Finke, P. A. and de Willigen, P. (2000). Upscaling and Downscaling Methods for Environmental Research. London: Kluwer Academic Publishers
Bithell, J. F. (1990). An application of density estimation to geographical epidemiology. Statistics in Medicine, 9, 691–701CrossRefGoogle ScholarPubMed
Bithell, J. F. (1995). The choice of test for detecting raised disease risk near a point source. Statistics in Medicine, 14, 2309–22CrossRefGoogle Scholar
Bivand, R. (1980). A Monte Carlo study of correlation coefficient estimation with spatially autocorrelated observations. Quaestiones Geographicae, 6, 5–10Google Scholar
Block, R. (1979). Community, environment and violent crime. Criminology, 17, 46–57CrossRefGoogle Scholar
Bloom, L. M. and Kentwell, D. J. (1998). A geostatistical analysis of cropped and uncropped soil from the Jimperding Brook catchment of Western Australia. In Geo ENV II – Geostatistics for Environmental Applications, eds. Gomez-Hernández, J., Soares, A. and Froidevaux, R. pp. 369–379. London: Kluwer Academic
Bolthausen, E. (1982). On the central limit theorem for stationary mixing random fields. Annals of Probability, 10, 1047–50CrossRefGoogle Scholar
Boots, B. (1982). Comments of the use of eigenfunctions to measure structural properties of geographic networks. Environment and Planning, A, 14, 1063–72CrossRefGoogle Scholar
Boots, B. (1984). Evaluating principal eigenvalues as measures of network structure. Geographical Analysis, 16, 270–5CrossRefGoogle Scholar
Borgeson, W. T., Baston, R. M. and Keiffer, H. H. (1985). Geometric accuracy of LANDSAT-4 and LANDSAT-5 Thematic Mapper images. Photogrammetric Engineering and Remote Sensing, 51, 1893–98Google Scholar
Bottoms, A. E., Mamby, R. I. and Walker, M. A. (1987). A localised crime survey in contrasting areas of a city. British Journal of Criminology, 27, 125–54CrossRefGoogle Scholar
Bottoms, A. E. and Wiles, P. (1997). Environmental criminology. The Oxford Handbook of Criminology, second edition, eds. Maguire, M., Morgan, R. and Reiner, R., pp. 305–59. Oxford: Oxford University Press
Bowers, K. J. and Hirschfield, A. (1999). Exploring links between crime and disadvantage in N. W. England: an analysis using Geographical Information Systems. International Journal of Geographical Information Science, 13, 159–84CrossRefGoogle Scholar
Bowie, W. R., King, A. S., Werker, D. H., Isaac-Renton, J. L., Bell, A., Eng, S. B. and Marion, S. A. (1997). The outbreak of toxoplasmosis associated with municipal drinking water. The Lancet, 350, 173–77CrossRefGoogle ScholarPubMed
Bracken, I. and Martin, D. (1989). The generation of spatial population distribution from census centroid data. Environment and Planning, A, 21, 537–43CrossRefGoogle ScholarPubMed
Brantingham, P. J. and Brantingham, P. L. (1991). Environmental Criminology, second edition. Prospect Heights, IL: Waveland Press
Bras, R. L. and Rodriguez-Iturbe, I. (1976). Network design for the estimation of the areal mean of rainfall events. Water Resources Research, 12, 1185–95CrossRefGoogle Scholar
Brassel, K., Bucher, F., Stephan, E-M. and Vckovski, A. (1995). Completeness in Elements of Spatial Data Quality. eds. Guptill, S. G. and Morrison, J. L., pp. 81–108. Oxford: Elsevier Science
Brassel, K. E. and Weibel, R. (1988). A review and framework of automated map generalization. International Journal of Geographical Information Systems, 2, 229–44CrossRefGoogle Scholar
Breusch, T. and Pagan, A. (1979). A simple test for heteroskedasticity and random coefficient variation. Econometrica, 47, 1287–94CrossRefGoogle Scholar
Brindley, P., Wise, S. M., Maheswaran, R. and Haining, R. P. (2002). Small area based population exposure estimates for modelled nitrogen oxide pollution data. Submitted to Computers Environment and Urban Systems
Bronfenbrenner, U. (1979). The Ecology of Human Development. Cambridge, MA: Harvard University Press
Brook, D. (1964). On the distinction between the conditional probability and the joint probability approaches in the specification of nearest neighbour systems. Biometrika, 51, 481–3CrossRefGoogle Scholar
Brooks-Gunn, J., Duncan, G. J., Klebanov, P. K. and Sealand, N. (1993). Do neighbourhoods influence child and adolescent development?American Journal of Sociology, 99, 353–95CrossRefGoogle Scholar
Brunsdon, C. (2000). The Comap: Investigating geographical pattern via conditional spatial distributions. Proceedings of the GIS Research UK Conference, University of York, April 2000, pp. 97–101
Brunsdon, C., Fotheringham, A. S. and Charlton, M. E. (1998). An investigation of methods for visualizing highly multivariate datasets. Case Studies of Visualization in the Social Sciences, eds. Unwin, D. and Fisher, P., pp. 55–79 Advisory group on Computer Graphics, Technical Report Series No. 43 (ISSN 1356–9066)
Brus, D. J. and deGruijter, J. J. (1997). Random sampling or geostatistical modelling? Choosing between design-based and model-based sampling strategies for soil (with Discussion). Geoderma, 80, 1–44CrossRefGoogle Scholar
Brusegard, D. and Menger, G. (1989). Real data and real problems: dealing with large spatial databases. Accuracy of Spatial Databases, pp. 177–85. London: Taylor & Francis
Buck, S. F. (1960). A method of estimation of missing values in multivariate data suitable for use with an electronic computer. Journal of the Royal Statistical Society, B, 22, 302–6Google Scholar
Buja, A., Cook, D. and Swayne, D. F. (1996). Interactive high-dimensional data visualization. Journal of Computational and Graphical Statistics, 5, 78–99Google Scholar
Bunge, W. (1962). Theoretical geography. Lund Studies in Geography. Lund: Gleerup
Burgess, T. M. and Webster, M. R. (1980). Optimal interpolation and isarithmic mapping of soil properties I. The semi-variogram and punctual kriging. Journal of Soil Science, 31, 315–31CrossRefGoogle Scholar
Burgess, T. M., Webster, M. R. and McBratney, A. B. (1981). Optimal interpolation and isarithmic mapping of soil properties, IV Sampling. Journal of Soil Science, 32, 643–59CrossRefGoogle Scholar
Burrough, P. A., Bregt, A. K., Heus, M. J. and Kloosterman, E. G. (1985). Complementary use of thermal imagery and spectral analysis of soil properties and wheat yields to reveal cyclic patterns in the Flevopolders. Journal of Soil Science, 36, 141–52CrossRefGoogle Scholar
Burrough, P. A. and Frank, A. U. (1995). Concepts and paradigms in spatial information: are current GIS truly generic?International Journal of Geographical Information Systems, 9, 101–16CrossRefGoogle Scholar
Bursik, R. J. and Grasmick, H. G. (1993). Neighbourhoods and Crime. New York: Lexington
Buttenfield, B. P. and Beard, M. K. (1994). Graphical and geographical components of data quality. Visualization in Geographic Information Systems, eds. Hearnshaw, H. M. and Unwin, D. J., pp. 150–7. New York: Wiley
Buttenfield, B. P. and Mark, D. M. (1994). Expert systems in cartographic design. Geographic Information Systems: The Microcomputer and Modern Cartography, ed. Taylor, D. R. F., pp. 129–50. Oxford: Pergamon
Câmara, A. S. and Raper, J. (1999). Spatial Multi-Media and Virtual Reality. London: Taylor and Francis
Carr, D. B., Wallin, J. F. and Carr, D. A. (2000). Two new templates for epidemiology applications: linked micro map plots and conditioned choropleth maps. Statistics in Medicine, 19, 2521–383.0.CO;2-K>CrossRefGoogle Scholar
Carter, J. R. (1992). The effect of data precision on the calculation of slope and aspect using gridded DEMS. Cartographica, 29, 22–34CrossRefGoogle Scholar
Casella, G. and George, E. I. (1992). Explaining the Gibbs sampler. The American Statistician, 46, 167–74Google Scholar
Ceccato, V., Haining, R. P. and Signoretta, P. E. (2002). Exploring offence statistics in Stockholm City using spatial analysis tools. Annals of the Association of American Geographers, 92, 29–51CrossRefGoogle Scholar
Cerioli, A. (1997). Modified tests of independence in 2×2 tables with spatial data. Biometrics, 53, 619–28CrossRefGoogle Scholar
Chatterjee, S. and Price, B. (1991). Regression Analysis by Example, second edition. New York: Wiley
Chilès, J-P. and Delfiner, P. (1999). Geostatistics: Modeling Spatial Uncertainty. New York: Wiley
Chorley, R. J. (1972). Spatial Analysis in Geomorphology. London: Methuen
Chou, Y. H. H. (1991). Map resolution and spatial autocorrelation. Geographical Analysis, 23, 228–46CrossRefGoogle Scholar
, Chow G. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28, 591–605CrossRefGoogle Scholar
Choynowski, M. (1959). Maps based on probabilities. Journal of the American Statistical Association, 54, 385–8CrossRefGoogle Scholar
Chrisman, N. R. (1989). Modelling error in overlaid categorical maps. Accuracy of Spatial Databases, eds. Goodchild, M. and Gopal, S., pp. 21–34. London: Taylor & Francis
Christakos, G. (1984). On the problem of permissable covariance and variogram models. Water Resources Research, 20, 251–65CrossRefGoogle Scholar
Cislaghi, C., Biggeri, A., Braga, M., Lagazio, C. and Marchi, M. (1995). Exploratory tools for disease mapping in geographical epidemiology. Statistics in Medicine, 14, 2363–81CrossRefGoogle ScholarPubMed
Clarke, D. L. (ed.) (1977). Spatial Archaeology. London: Academic Press
Clayton, D. and Bernardinelli, L. (1992). Bayesian methods for mapping disease risk. Geographical and Environmental Epidemiology: Methods for Small Area Studies, eds. Elliott, P., Cuzick, J., English, D. and Stern, R., pp. 205–20. Oxford: Oxford University Press
Clayton, D. G., Bernardinelli, L. and Montomoli, C. (1993). Spatial correlation in ecological analysis. International Journal of Epidemiology, 22, 1193–202CrossRefGoogle ScholarPubMed
Clayton, D. and Kaldor, J. (1987). Empirical Bayes estimates of age – standardised relative risks for use in disease mapping. Biometrics, 43, 671–81CrossRefGoogle Scholar
Cleveland, W. S. and Devlin, S. J. (1988). Locally weighted regression: an approach to regression analysis by local fitting. Journal of the American Statistical Association, 83, 596–610CrossRefGoogle Scholar
Cleveland, W. S. (1993). Rejoinder: a model for studying display methods of statistical graphics. Journal of Computational and Graphical Statistics, 2, 361–4Google Scholar
Cleveland, W. S. (1993a). Visualizing Data. AT&T Bell Laboratories, Murray Hill, New Jersey, USA
Cleveland, W. S. (1994). The Elements of Graphing Data, second edition, AT&T Bell Laboratories. Murray Hill, New Jersey, USA
Cliff, A. D., Haggett, P. and Ord, J. K. (1985). Spatial Aspects of Influenza Epidemics. London: Pion
Cliff, A. D., Haggett, P., Ord, J. K., Bassett, K. and Davies, R. B. (1975). Elements of Spatial Structure: A Quantitative Approach. Cambridge: Cambridge University Press
Cliff, A. D., Haggett, P. and Smallman-Raynor, M. (1993). Measles: An Historical Geography. Oxford: Blackwell
Cliff, A. D. and Kelly, F. P. (1977). Regional taxonomy using trend surface coefficients and invariants. Environment and Planning, A, 9, 945–55CrossRefGoogle Scholar
Cliff, A. D. and Ord, J. K. (1981). Spatial Processes. London: Pion
Clifford, P. and Richardson, S. (1985). Testing the association between two spatial processes. Statistics and Decisions, Suppl. No. 2, 155–60Google Scholar
Clifford, P., Richardson, S. and Hémon, D. (1989). Assessing the significance of the correlation between two spatial processes. Biometrics, 45, 123–34CrossRefGoogle ScholarPubMed
Coale, A. J. and Stephan, F. F. (1962). The case of the Indians and teenage widows. Journal, American Statistical Association, 57, 338–47CrossRefGoogle Scholar
Cockings, S., Fisher, P. F. and Langford, M. (1997). Parametrization and visualization of the errors in areal interpolation. Geographical Analysis, 29, 314–28CrossRefGoogle Scholar
Cohen, J. and Tita, G. (1999). Editor's introduction. Journal of Quantitative Criminology, 15, 373–8CrossRefGoogle Scholar
Collins, S. (1995). Modelling spatial variations in air quality using GIS. GIS and Health, eds. Gatrell, A. and Löytönen, M., London: Taylor & Francis
Collins, S. E., Haining, R. P., Bowns, I. R., Crofts, D. J., Williams, T. S., Rigby, A. S. and Hall, D. M. B. (1998). Errors in postcode to enumeration district mapping and their effect on small area analyses of health data. Journal of Public Health Medicine, 20, 325–30CrossRefGoogle ScholarPubMed
Congalton, R. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing and the Environment, 37, 35–46CrossRefGoogle Scholar
Congdon, P. (2001). Bayesian Statistical Modelling. Chichester: Wiley
Congdon, P. (2002). A model for mental health needs and resourcing in small geographic areas: a multivariate spatial perspective. Geographical Analysis, 34, 168–86CrossRefGoogle Scholar
Conley, T. G. (1999). GMM estimation with cross-sectional dependence. Journal of Econometrics, 92, 1–45CrossRefGoogle Scholar
Cook, D., Buja, A., Cabrera, J. and Hurley, C. (1995). Grand tour and projection pursuit. Journal of Computational and Graphical Statistics, 4, 155–72Google Scholar
Cook, D. G. and Pocock, S. J. (1983). Multiple regression in geographical mortality studies with allowance for spatially correlated errors. Biometrics, 39, 361–71CrossRefGoogle ScholarPubMed
Coombes, M. and Openshaw, S. (2001). Contrasting approaches to identifying ‘localities’ for research and public administration. Life and Motion of Socio-economic Units, eds. Frank, A., Raper, J. and Cheylan, J. P., pp. 301–15. London: Taylor & Francis
Coppock, J. T. (1955). The relationship between farm and parish boundaries. Geographical Studies, 1, 12–25Google Scholar
Couclelis, H. (1985). Cellular worlds: a framework for modelling micro-macro dynamics. Environment and Planning, B, 17, 585–96CrossRefGoogle Scholar
Court, A. (1970). Map comparisons. Economic Geography, 46, 435–8CrossRefGoogle Scholar
Craglia, M., Haining, R. P. and Signoretta, P. E. (2001). Modeling high intensity crime areas in English cities. Urban Studies, 38, 1921–41CrossRefGoogle Scholar
Craglia, M., Haining, R. P. and Signoretta, P. E. (2002). Identifying areas of multiple social need: a case study in the preparation of Children Service plans. Environment and Planning, C (to appear)
Craglia, M., Haining, R. P. and Wiles, P. (2000). A comparative evaluation of approaches to urban crime pattern analysis. Urban Studies, 37, 711–29CrossRefGoogle Scholar
Craig, P., Haslett, J., Unwin, A. and Wills, G. (1989). Moving statistics – an extension of brushing for spatial data. Computing Science and Statistics. Proceedings of the 21st Symposium on the Interface, pp. 170–4. Berlin: Springer Verlag
Craig, R. G. (1979). Autocorrelation in LANDSAT data. In Proceedings of the 13th International Symposium on Remote Sensing of the Environment, pp. 1517–24 Ann Arbor, Michigan
Craig, R. G. and Labovitz, M. L. (1980). Sources of variation in LANDSAT autocorrelation. In Proceedings of the 14th International Symposium on Remote Sensing of the Environment, pp. 1755–67 San Jose, Costa Rica
Cressie, N. (1984). Towards resistant geostatistics. In Geostatistics for Natural Resources Characterization, eds. Verly, G., David, M., Journel, A. G. and Marechal, A., pp. 21–44. Dordrecht: ReidelCrossRef
Cressie, N. (1985). Fitting variogram models by weighted least squares. Journal of the International Association of Mathematical Geology, 17, 563–86CrossRefGoogle Scholar
Cressie, N. (1991). Statistics for Spatial Data. New York: Wiley
Cressie, N. (1992). Smoothing regional maps using empirical Bayes predictors. Geographical Analysis, 24, 75–95CrossRefGoogle Scholar
Cressie, N. (1996). Change of support and the modifiable areal unit problem. Geographical Systems, 3, 159–80Google Scholar
Cressie, N. and Chan, N. H. (1989). Spatial modeling of regional variables. Journal of the American Statistical Association, 84, 393–401CrossRefGoogle Scholar
Cressie, N. and Hawkins, D. M. (1980). Robust estimation of the variogram, I. Journal of the International Association of Mathematical Geology, 12, 115–25CrossRefGoogle Scholar
Cressie, N. and Read, T. R. C. (1989). Spatial data analysis of regional counts. Biometrical Journal, 31, 699–719CrossRefGoogle Scholar
Cromley, R. G. (1996). A comparison of optimal classification strategies for choropleth displays of spatially aggregated data. International Journal of Geographical Information Systems, 10, 405–24CrossRefGoogle Scholar
Cross, G. R. and Jain, A. K. (1983). Markov random field texture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAM 1–5, 1, 25–39CrossRefGoogle Scholar
Cuff, D. and Mattson, M. T. (1982). Thematic Maps: Their Design and Production. New York: Methuen
Curran, P. (1980). Multispectral remote sensing of vegetation amount. Progress in Physical Geography, 4, 315–41CrossRefGoogle Scholar
Cuzick, J. and Edwards, R. (1990). Spatial clustering for inhomogeneous populations. Journal of the Royal Statistical Society, B, 52, 73–104Google Scholar
Cuzick, J. and Elliott, P. (1992). Small area studies: purpose and methods. Geographical and Environmental Epidemiology: Methods for Small Area Studies, eds. Elliott, P., Cuzich, J., English, D. and Stern, R., pp. 14–21. Oxford: Oxford University Press
Decker, D. (2001). GIS Data Sources. New York: Wiley
deLepper, M. J. C., Scholten, H. J. and Stern, R. M. (eds.) (1995). The Added Value of Geographical Information Systems in Public and Environmental Health. Dordrecht: Kluwer Academic
Dempster, A. P. and Rubin, D. B. (1983). Overview in incomplete data in sample surveys. Vol II: Theory and Annotated Bibliography, eds. Madow, W. G., Olkin, I. and Rubin, D. B., pp. 3–10, New York: Academic Press
Denison, D. G. T. and Holmes, C. C. (2001). Bayesian partitioning for estimating disease risk. Biometrics, 57, 143–9CrossRefGoogle ScholarPubMed
Dent, B. D. (1985). Principles of Thematic Map Design. London: Addison-Wesley
DETR (2000). Indices of deprivation 2000: regeneration research summary. See www.regeneration.detr.gov.uk
Devine, O. J. and Louis, T. A. (1994). A constrained empirical Bayes estimator for incidence rates in areas with small populations. Statistics in Medicine, 13, 1119–33CrossRefGoogle ScholarPubMed
Devine, O. J., Louis, T. A. and Halloran, M. E. (1996). Identifying areas with elevated disease incidence rates using empirical Bayes estimators. Geographical Analysis, 28, 187–99CrossRefGoogle Scholar
Diggle, P. J. and Chetwynd, A. D. (1991). Second-order analysis of spatial clustering for inhomogeneous populations. Biometrics, 47, 1155–63CrossRefGoogle ScholarPubMed
Diggle, P., Morris, S. and Morton-Jones, T. (1999). Case control isotonic regression for investigation of elevation in risk around a point source. Statistics in Medicine, 18, 1605–133.0.CO;2-V>CrossRefGoogle ScholarPubMed
Dobson, A. J. (1999). An Introduction to Generalized Linear Models. Boca Raton: Chapman & Hall
Dockery, D. W., Pope, C. A., Xu, X., Spengler, J. D., Ware, J. H., Fay, M. E., Ferris, B. G. and Speizer, F. E. (1993). An association between air pollution and mortality in six US cities. New England Journal of Medicine, 329, 1753–9CrossRefGoogle Scholar
Doreian, P. and Hummon, N. P. (1976). Modelling Social Processes. New York: Elsevier
Dorling, D. (1992). Stretching space and splicing time: from cartographic animation to interactive visualization. Cartography and Geographic Information Systems, 19, 215–27CrossRefGoogle Scholar
Dorling, D. (1994). Cartograms for visualizing human geography. Visualization in Geographic Information Systems, eds. Hearnshaw, H. M. and Unwin, D. J., pp. 85–102. New York: Wiley
Dorling, D. (1995). Visualizing the 1991 Census. Census Users Handbook, ed. Openshaw, S., pp. 167–211. Cambridge: GeoInformation International
Dow, M. M., Burton, M. L. and White, D. R. (1982). Network autocorrelation: a simulation study of a foundational problem in regression and survey research. Social Networks, 4, 169–200CrossRefGoogle Scholar
Dowd, P. A. (1984). The variogram and kriging: robust and resistant estimators. Geostatistics for Natural Resources Characterization, eds. Verly, G., David, M., Journel, A. G. and Marechal, A., pp. 91–106. Dordrecht: ReidelCrossRef
Drummond, J. (1995). Positional Accuracy. In Elements of Spatial Data Quality, eds. Guptill, S. C. and Morrison, J. L., pp. 31–58. Oxford: Elsevier ScienceCrossRef
Dubin, R. (1997). A note on the estimation of spatial logit models. Geographical Systems, 4, 181–93Google Scholar
Dunn, R. (1987). Variable width framed rectangle charts for statistical mapping. American Statistician, 41, 153–6Google Scholar
Dunn, R. (1989). Approaches to two-variable color mapping. American Statistician, 43, 245–52Google Scholar
Dunn, R. and Harrison, A. R. (1993). Two dimensional systematic sampling of land use. Applied Statistics, 42, 585–601CrossRefGoogle Scholar
Dunn, R., Harrison, A. R. and White, J. C. (1990). Positional accuracy and measurement error in digital databases of land use: an empirical study. International Journal of Geographical Information Systems, 4, 385–98CrossRefGoogle Scholar
Dykes, J. A. (1997). Exploring spatial data representation with dynamic graphics. Computers and Geosciences, 23, 345–70CrossRefGoogle Scholar
Dykes, J. (1998). Cartographic visualization: exploratory spatial data analysis with local indicators of spatial association using Tcl/Tk and cdv. The Statistician, 47, 3, 485–97Google Scholar
Earnshaw, R. A. and Wiseman, N. (eds) (1992). An Introductory Guide to Scientific Visualization. Berlin: Springer-Verlag
Elliott, P., Cuzick, J., English, D. and Stern, R. (1992). Geographical and Environmental Epidemiology: Methods for Small Area Studies. Oxford: Oxford University Press
Elliott, P., Wakefield, J., Best, N. and Briggs, D. (2000). Spatial Epidemiology: Methods and Applications. Oxford: Oxford University Press
Emerson, J. D. and Hoaglin, D. C. (1983). Analysis of two way tables by medians. Understanding Robust and Exploratory Data Analysis, eds. Hoaglin, D. C., Mosteller, F. M. and Tukey, J. W., pp. 166–210. New York: Wiley
English, D. (1992). Geographical epidemiology and ecological studies. Geographical and Environmental Epidemiology: Methods for Small Area Studies, eds. Elliott, P., Cuzich, J., English, D. and Stern, R. pp. 3–13. Oxford: Oxford University Press
ESRC (2001). Health Variations Programme. www.lancs.ac.uk/users/apsocsci/hvp.htm
Everett, B. (1979). Cluster Analysis. London: Heinemann
Faminow, M. D. and Benson, B. L. (1990). Integration of spatial markets. American Journal of Agricultural Economics, 70, 49–62CrossRefGoogle Scholar
Farrington, D. P. and Dowds, E. A. (1984). Disentangling criminal behaviour and police reaction. Reactions to Crime, eds. Farrington, D. P. and Gunn, J. Chichester: Wiley
Fedra, K. (1993). GIS and environmental modelling. Environmental Modelling with GIS, eds. Goodchild, M. F., Parks, B. O. and Steyaert, L. T., pp. 35–50. New York: Oxford University Press
Ferreira, J. T. A. S., Denison, D. G. T. and Holmes, C. C. (2002). Partition modeling. Technical report at http://stats.ma.ic.ac.uk/∼dgtd
Fieldhouse, E. A. and Tye, R. (1996). Deprived people or deprived places? Exploring the ecological fallacy in studies of deprivation with the samples of anonymised records. Environment and Planning, A, 28, 237–59CrossRefGoogle Scholar
Fik, T. J. (1988). Spatial competitional price reporting in related food markets. Economic Geography, 64, 29–44CrossRefGoogle Scholar
Fik, T. J. (1991). Price patterns in competitively clustered markets. Environment and Planning, A, 23, 1545–60CrossRefGoogle Scholar
Firebaugh, G. (1978). A rule for inferring individual relationships from aggregate data. American Sociological Review, 43, 557–72CrossRefGoogle Scholar
Fischer, M. Scholten, H. J. and Unwin, D. (1996). Spatial Analytical Perspectives on GIS. London: Taylor & Francis
Fisher, P. and Langford, M. (1995). Modelling the errors in areal interpolation between zonal systems by Monte Carlo simulation. Environment and Planning, A, 27, 211–24CrossRefGoogle Scholar
Fisher, R. (1935). The Design of Experiments. Edinburgh: Oliver & Boyd
Flowerdew, R. and Green, M. (1989). Statistical methods for inference between incompatible zonal systems. Accuracy of Spatial Databases, eds. Goodchild, M. and Gopal, S., pp. 239–47. London: Taylor & Francis
Flowerdew, R., Green, M. and Kehris, E. (1991). Using areal interpolation methods in geographic information systems. Papers in Regional Science, 70, 303–15CrossRefGoogle Scholar
Follmer, H. (1974). Random economies with many interacting agents. Journal of Mathematical Economics, 1, 51–62CrossRefGoogle Scholar
Ford, G. E. and Zanelli, C. I. (1985). Analysis and quantification of errors in the geometric correction of images. Photogrammetric Engineering and Remote Sensing, 51, 1725–34Google Scholar
Forster, B. C. (1980). Urban residential ground cover using LANDSAT digital data. Photogrammetric Engineering and Remote Sensing, 46, 547–58Google Scholar
Fotheringham, A. S., Brunsdon, C. and Charlton, M. (2000). Quantitative Geography: Perspectives on Spatial Data Analysis. London: Sage
Fotheringham, A. S. and Charlton, M. (1994). GIS and exploratory spatial data analysis: an overview of some research issues. Geographical Systems, 1, 315–27Google Scholar
Fotheringham, A. S. and Rogerson, P. (1994). Spatial Analysis and GIS. London: Taylor & Francis
Fotheringham, A. S. and Wegener, M. (2000). Spatial Models and GIS. London: Taylor & Francis
Freedman, D. A., Klein, S. P., Sachs, J., Smyth, C. A. and Everett, C. G. (1991). Ecological regression and voting rights (with discussion). Evaluation Review, 15, 673–711CrossRefGoogle Scholar
Freedman, D. A., Ostland, M. and Roberts, M. R. (1998). A solution to the ecological inference problem. Journal of the American Statistical Association, 93, 1518–22CrossRefGoogle Scholar
Freedman, D. A., Ostland, M. and Roberts, M. R. (1999). The future of ecological inference research: a comment on Freedman et al. – a response to King's comment. Journal of the American Statistical Association, 94, 355–7CrossRefGoogle Scholar
Freund, J. (1992). Mathematical Statistics, fifth edition, Englewood Cliffs, NJ: Prentice-Hall
Friedman, G. D. (1994). Primer of Epidemiology. New York: McGraw Hill
Friendly, M. (1995). Conceptual and visual models for categorical data. The American Statistician, 49, 153–60Google Scholar
Frogbrook, Z. L. and Oliver, M. A. (2000). The effects of sampling on the accuracy of predictions of soil properties in precision agriculture. Proceedings of the 4th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, pp. 225–32. Delft: Delft University Press
Fuller, W. A. (1975). Regression analysis for sample survey. Sankhy, 37(c), 117–32Google Scholar
Gahegan, M. (1999). Four barriers to the development of effective exploratory visualization tools for the geosciences. International Journal of Geographical Information Sciences, 13, 289–309CrossRefGoogle Scholar
Gallup, J. L., Sacks, J. D. and Mellinger, A. D. (1999). Geography and economic development. International Regional Science Review, 22, 179–232CrossRefGoogle Scholar
Galtung, J. (1967). Theory and Methods of Social Research. New York: Columbia University Press
Gardiner, M. J. (1989). Review of reported increases in childhood cancer rates in the vicinity of nuclear installations in the UK. Journal of the Royal Statistical Society, A, 152, 307–25CrossRefGoogle Scholar
Gatrell, A. C. (1983). Distance and Space: A Geographical Perspective. Oxford: Clarendon Press
Gatrell, A. C. (1998). Structures of geographical and social space and their consequences for human health. Geografiska Annaler, 79, 141–54CrossRefGoogle Scholar
Gatrell, A. C., Bailey, T. C., Diggle, P. J. and Rowlingson, B. S. (1996). Spatial point pattern analysis and its application in geographical epidemiology. Transactions, Institute of British Geographers, 21, 256–74CrossRefGoogle Scholar
Gatrell, A. C. and Dunn, C. E. (1995). Geographical information systems and spatial epidemiology: modelling the possible association between cancer of the larynx and incineration in North West England. The Added Value of Geographical Information Systems in Public and Environmental Health, eds. deLepper, M. J. C., Scholten, H. J. and Stern, R. M., pp. 215–35. Dordrecht: Kluwer AcademicCrossRef
Gatrell, A. C. and Löytönen, M. (eds.) (1998). GIS and Health. London: Taylor & Francis
Geary, R. C. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician, 5, 115–45CrossRefGoogle Scholar
Gehlke, C. E. and Biehl, K. (1934). Certain effects of grouping upon the size of the correlation coefficient in census tract material. Journal of the American Statistical Association, 29, 169–70Google Scholar
Gelman, A., Price, P. N. and Lin, C-Y. (2000). A method of quantifying artefacts in mapping methods illustrated by application to headbanging. Statistics in Medicine, 19, 2309–203.0.CO;2-H>CrossRefGoogle ScholarPubMed
Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (1995). Bayesian Data Analysis. London: Chapman & Hall
Gelman, A., Park, D. K., Ansolabehere, S., Price, P. N. and Minnite, L. (2001). Models, assumptions and model checking in ecological regressions. Journal of the Royal Statistical Society, A, 164, 101–18CrossRefGoogle Scholar
Gelman, A. and Price, P. N. (1999). All maps of parameter estimates are misleading. Statistics in Medicine, 18, 3221–343.0.CO;2-M>CrossRefGoogle ScholarPubMed
Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE, Transactions in Pattern Analysis and Machine Intelligence, 6, 721–41CrossRefGoogle ScholarPubMed
Getis, A. and Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24, 189–206 (with correction, 1993, 25, p. 276)CrossRefGoogle Scholar
Getis, A. and Ord, J. K. (1995). Local spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis, 27, 286–306Google Scholar
Getis, A. and Ord, J. K. (1996). Local spatial statistics: an overview. Spatial Analysis: Modelling in a GIS environment, eds. Longley, P. and Batty, M., pp. 261–77. Cambridge: Geoinformation International
Ghosh, M. and Rao, J. N. K. (1994). Small area estimation: an appraisal. Statistical Science, 9, 55–93CrossRefGoogle Scholar
Gilbert, C. L. (1986). Professor Hendry's econometric methodology. Oxford Bulletin of Economics and Statistics, 48, 283–307CrossRefGoogle Scholar
Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. (1996). Markov Chain Monte Carlo in Practice: Interdisciplinary Statistics. London: Chapman and Hall
Godambe, V. P. and Thompson, M. E. (1971). Bayes, fiducial and frequency aspects of statistical inference in regression analysis in survey sampling. Journal of the Royal Statistical Society, B, 33, 361–90Google Scholar
Goldsmith, V., McGuire, P. G., Mollenkopf, J. H. and Ross, T. A. (eds). (2000). Analyzing Crime Patterns: Frontiers of Practice. Thousand Oaks: Sage
Goldstein, H. (1994). Multi-level cross-classified models. Sociological Methods and Research, 22, 364–75CrossRefGoogle Scholar
Good, I. J. (1983). The philosophy of exploratory data analysis. Philosophy of Science, 50, 283–95CrossRefGoogle Scholar
Goodchild, M. F. (1989). Modelling error in objects and fields. Accuracy of Spatial Databases, eds. Goodchild, M. and Gopal, S. pp. 107–13. London: Taylor & Francis
Goodchild, M. F. (1995). Attribute accuracy. Elements of Spatial Data Quality, eds. Guptill, S. C. and Morrison, J. L. pp. 59–79. Oxford: Elsevier ScienceCrossRef
Goodchild, M. F., Anselin, L. and Deichmann, U. (1993). A framework for the areal interpolation of socio-economic data. Environment and Planning, A, 25, 383–97CrossRefGoogle Scholar
Goodchild, M. F. and Gopal, S. (1989). Accuracy of Spatial Databases. New York: Taylor & Francis
Goodchild, M. F., Guoqing, S. and Shiren, Y. (1992). Development and test of an error model for categorical data. International Journal of Geographical Information Systems, 6, 87–104CrossRefGoogle Scholar
Goodchild, M. F. and Lam, N. S-M. (1980). Areal interpolation: a variant of the traditional spatial problem. Geo-Processing, 1, 297–312Google Scholar
Goodchild, M. F., Parks, B. O. and Steyaert, L. T. (1993). Environmental Modelling with GIS. New York: Oxford University Press
Goodman, L. (1953). Ecological regressions and the behaviour of individuals. American Sociological Review, 18, 663–6CrossRefGoogle Scholar
Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford: Oxford University Press
Gordon, A. and Womersley, J. (1997). The use of mapping in public health and planning health services. Journal of Public Health Medicine, 19, 139–47CrossRefGoogle ScholarPubMed
Greater Glasgow Health Board (1981). Census 1981, Maps for Community Medicine Areas. Greater Glasgow Information Services Unit. 51 pp
Green, M. and Flowerdew, R. (1996). New evidence on the modifiable areal unit problem. Spatial Analysis: Modelling in a GIS Environment, eds. Longley, P. and Batty, M., pp. 41–54. Cambridge: GeoInformation International
Griffith, D. A. (1978). A spatially adjusted ANOVA model. Geographical Analysis, 10, 296–301CrossRefGoogle Scholar
Griffith, D. A. (1982). Dynamic characteristics of spatial economic systems. Economic Geography, 58, 177–96CrossRefGoogle Scholar
Griffith, D. A. (1989). Distance calculations and errors in geographic databases. Accuracy of Spatial Databases, eds. Goodchild, M. and Gopal, S., pp. 81–90. New York: Taylor & Francis
Griffith, D. A. (1996). Spatial autocorrelation and eigenfunctions of the geographic weights matrix accompanying geo-referenced data. Canadian Geographer, 40, 351–67CrossRefGoogle Scholar
Griffith, D. A., Bennett, R. J. and Haining, R. P. (1989). Statistical analysis of spatial data in the presence of missing observations: a methodological guide and an application to urban census data. Environment and Planning, A, 21, 1511–23CrossRefGoogle ScholarPubMed
Griffith, D. A., Haining, R. P. and Arbia, G. (1994). Heterogeneity of attribute sampling error in spatial data sets. Geographical Analysis, 26, 300–20CrossRefGoogle Scholar
Griffith, D. A. and Layne, L. J. (1999). A Casebook for Spatial Statistical Data Analysis – A Compilation of Different Thematic Data Sets. Oxford: Oxford University Press
Grigg, D. B. (1967). Regions, models and classes. Models in Geography, eds. Chorley, R. J. and Haggett, P., pp. 461–509. London: Methuen
Grimson, R. C. (1991). A versatile test for clustering and a proximity analysis of neurons. Methods of Information in Medicine, 30, 299–303Google Scholar
Gumpertz, M. L., Graham, J. M. and Ristaino, J. B. (1997). Autologistic model of spatial pattern of phytophthora epidemic in bell pepper: effects of soil variables on disease presence. Journal of Agricultural, Biological and Environmental Statistics, 2, 131–56CrossRefGoogle Scholar
Guptill, S. C. (1994). Synchronization of discrete geospatial databases. Advances in GIS Research. Proceedings of the 6th International Symposium on Spatial Data Handling, eds Waugh, T. C. and Healey, R. G., IGU Commission on GIS and the Association for Geographic Information, Vol. 2, pp. 945–56
Guptill, S. C. and Morrison, J. L. (1995). Elements of Spatial Data Quality. Oxford: Elsevier Science
Guyon, X. (1995). Random Fields on a Network. New York: Springer-Verlag
Hagerstand, T. (1967). Innovation Diffusion as a Spatial Process. Chicago, IL: Chicago University Press
Haining, R. P. (1978). The moving average model for spatial interaction. Transactions of the Institute for British Geographers, NS3, 202–25CrossRefGoogle Scholar
Haining, R. (1980). Spatial autocorrelation problems. Geography and the Urban Environment, eds. Herbert, D. T. and Johnston, R. J., pp. 1–44. New York: Wiley
Haining, R. P. (1983a). Anatomy of a price war. Nature, 304, 679–80CrossRefGoogle Scholar
Haining, R. P. (1983b). Modelling intra-urban price competition: an example of gasoline pricing. Journal of Regional Science, 23, 517–28CrossRefGoogle Scholar
Haining, R. P. (1985). The spatial structure of competition and equilibrium price dispersion. Geographical Analysis, 17, 231–42CrossRefGoogle Scholar
Haining, R. P. (1987). Small area aggregate income models: theory and methods with an application to urban and rural income data for Pennsylvania. Regional Studies, 21, 519–30CrossRefGoogle Scholar
Haining, R. P. (1988). Estimating spatial means with an application to remotely sensed data. Communications in Statistics, Theory and Methods, 17, 573–97CrossRefGoogle Scholar
Haining, R. P. (1990a). The use of added variable plots in regression modelling with spatial data. Professional Geographer, 42, 336–44CrossRefGoogle Scholar
Haining, R. P. (1990b). Spatial Data Analysis in the Social and Environmental Sciences. Cambridge: Cambridge University Press
Haining, R. P. (1991). Bivariate correlation with spatial data. Geographical Analysis, 23, 210–27CrossRefGoogle Scholar
Haining, R. P. (1991a). Estimation with heteroscedastic and correlated errors: a spatial analysis of intra-urban mortality data. Papers in Regional Science, 70, 223–41CrossRefGoogle Scholar
Haining, R. P. (1994). Designing spatial data analysis modules for GIS. Spatial Analysis and GIS, eds. Fotheringham, S. and Rogerson, P. London: Taylor & Francis
Haining, R. P. (1994a). Diagnostics of regression modelling in spatial econometrics. Journal of Regional Science, 34, 325–41CrossRefGoogle Scholar
Haining, R. P. (1995). Data problems in spatial econometric modelling. New Directions in Spatial Econometrics, eds. Anselin, L. and Florax, R. J. G., pp. 156–71. Berlin: SpringerCrossRef
Haining, R. P. and Arbia, G. (1993). Error propagation through map operations. Technometrics, 35, 293–305CrossRefGoogle Scholar
Haining, R. P. and Cliff, A. (2002). Using a scan statistic to map the incidence of an infectious disease: measles in the USA 1962–1995. GEOMED 2000
Haining, R. P., Griffith, D. A. and Bennett, R. J. (1983). Simulating two dimensional autocorrelated surfaces. Geographical Analysis, 15, 247–55CrossRefGoogle Scholar
Haining, R. P., Griffith, D. A. and Bennett, R. J. (1984). A statistical approach to the problem of missing data using a first order Markov model. Professional Geographer, 36, 338–48CrossRefGoogle Scholar
Haining, R. P., Griffith, D. A. and Bennett, R. J. (1989). Maximum likelihood estimation with missing spatial data and with an application to remotely sensed data. Communications in Statistics: Theory and Methods, 18, 1875–94CrossRefGoogle Scholar
Haining, R. P., Plummer, P. and Sheppard, E. (1996). Spatial price equilibrium in interdependent markets: price and sales configuration. Papers of the Regional Science Association, 75, 41–64CrossRefGoogle Scholar
Haining, R., Wise, S. and Blake, M. (1994). Constructing regions for small area analysis: material deprivation and colorectal cancer. Journal of Public Health Medicine, 16, 429–38CrossRefGoogle ScholarPubMed
Haining, R. P., Wise, S. M. and Signoretta, P. E. (2000). Providing scientific visualization for spatial data analysis: criteria and an assessment of SAGE. Journal of Geographical Systems, 2, 121–40CrossRefGoogle Scholar
Haining, R. P., Wise, S. M. and Ma, J. (1998). Exploratory spatial data analysis in a geographic information system environment. The Statistician, 47, 457–69Google Scholar
Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J. and Stahel, W. A. (1986). Robust Statistics. New York: Wiley
Hansen, K. M. (1991). Head-banging: robust smoothing in the plane. IEEE Transactions on Geoscience and Remote Sensing, 29, 369–78CrossRefGoogle Scholar
Hansen, M. H., Madow, W. G. and Tepping, B. J. (1983). An evaluation of model dependent and probability sampling inferences in sample surveys (with comments). Journal of the American Statistical Association, 78, 776–807CrossRefGoogle Scholar
Harris, R. J. and Longley, P. A. (2000). New data and approaches for urban analysis: modelling residential densities. Transactions in GIS, 4, 217–34CrossRefGoogle Scholar
Haslett, J., Bradley, R., Craig, P., Unwin, A. and Wills, G. (1991). Dynamic graphics for exploring spatial data with applications to locating global and local anomalies. The American Statistician, 45, 234–42Google Scholar
Haslett, J., Wills, G. and Unwin, A. (1990). SPIDER, an interactive tool for the analysis of spatially distributed data. International Journal of Geographical Information Systems, 4, 285–96CrossRefGoogle Scholar
Hawkins, D. M., Bradu, D. and Kass, G. (1984). Location of several outliers in multiple regression using elemental sets. Technometrics, 26, 197–208CrossRefGoogle Scholar
Heagerty, P. J. and Lele, S. R. (1998). A composite likelihood approach to binary spatial data. Journal of the American Statistical Association, 93, 1099–111CrossRefGoogle Scholar
Hearnshaw, H. M. and Unwin, D. J. (1994). Visualization in Geographic Information Systems. New York: Wiley
Hepple, L. (1979). Bayesian analysis of the linear model with spatial dependence. Exploratory and Explanatory Statistical Analysis of Spatial Data, eds. Bartels, C. P. A. and Ketellapper, R. H., pp. 179–99. Boston, MA: Martinus NijhoffCrossRef
Hepple, L. W. (1996). Directions and opportunities in spatial econometrics. Spatial Analysis Modelling in a GIS Environment, eds. Longley, P. and Batty, M., pp. 231–46. Cambridge: GeoInformation International
Hepple, L. W. (1998). Exact testing for spatial autocorrelation among regression residuals. Environment and Planning, A, 30, 85–108CrossRefGoogle Scholar
Heuvelink, G. B. M. (1993). Error Propagation in Environmental Modelling with GIS. London: Taylor & Francis
Heuvelink, G. B. M. (1999). Aggregation and error propagation in GIS. Spatial Accuracy Assessment: Land Information Uncertainties in Natural Resources, eds. Lowell, K. and Jaton, A., pp. 219–25. Chelsea, Michigan: Ann Arbor Press
Heuvelink, G. B. M., Burrough, P. A. and Stein, A. (1989). Propagation of errors in spatial modelling with GIS. International Journal of Geographical Information Systems, 3, 303–22CrossRefGoogle Scholar
Hill, P. W. and Goldstein, H. (1998). Multi-level modelling of educational data with cross classification and missing identification of units. Journal of Educational and Behavioural Statistics, 23, 117–28CrossRefGoogle Scholar
Hirschfield, A. and Bowers, K. J. (1997). The effect of social cohesion on levels of recorded crime in disadvantaged areas. Urban Studies, 34, 1275–95CrossRefGoogle Scholar
Hirschfield, A. and Bowers, K. (2001). Mapping and Analysing Crime Data: Lessons from Research and Practice. London: Taylor & Francis
Hjalmars, U., Kulldorff, M., Gustafsson, G. and Nagarwalla, N. (1996). Childhood leukaemia in Sweden: using GIS and a spatial scan statistic for cluster detection. Statistics in Medicine, 15, 707–153.0.CO;2-4>CrossRefGoogle Scholar
Hoaglin, D. C., Mosteller, F. and Tukey, J. W. (1983). Understanding Robust and Exploratory Data Analysis. New York: Wiley
Hoaglin, D. C., Mosteller, F. and Tukey, J. W. (1985). Exploring Data Tables, Trends and Shapes. New York: Wiley
Hodder, I. (1977). Some new directions in the spatial analysis of archaeological data at the regional scale (macro). Spatial Archaeology, ed. Clarke, D. L., pp. 223–351. London: Academic Press
Hole, D. J. and Lamont, D. W. (1992). Problems in the interpretation of small area analysis of epidemiological data: the case of cancer incidence in the West of Scotland. Journal of Epidemiology and Community Health, 46, 305–10CrossRefGoogle ScholarPubMed
Holmes, J. H. and Haggett, P. (1977). Graph theory interpretation of flow matrices: a note on maximization procedures for identifying significant linkages. Geographical Analysis, 9, 388–99CrossRefGoogle Scholar
Holt, D., Steel, D. G. and Tranmer, M. (1996). Area homogeneity and the modifiable areal unit problem. Geographical Systems, 3, 181–200Google Scholar
Holt, D., Steel, D. G., Tranmer, M. and Wrigley, N. (1996). Aggregation and ecological effects in geographically based data. Geographical Analysis, 28, 244–61CrossRefGoogle Scholar
Horn, M. E. T. (1995). Solution techniques for large regional partitioning problems. Geographical Analysis, 27, 230–48CrossRefGoogle Scholar
Horton, C. W., Hempkins, W. B. and Hoffman, A. A. J. (1964). A statistical analysis of some aeromagnetic maps from the Northwestern Canadian Shield. Geophysics, 4, 582–601CrossRefGoogle Scholar
Hubert, L. J., Golledge, R. G. and Costanzo, C. M. (1981). Generalized procedures for evaluating spatial autocorrelation. Geographical Analysis, 13, 224–33CrossRefGoogle Scholar
Hubert, L. J., Golledge, R. G., Costanzo, C. M. and Gale, N. (1985). Measuring association between spatially defined variables: an alternative procedure. Geographical Analysis, 17, 36–46CrossRefGoogle Scholar
Huffer, F. W. and Wu, H. (1998). Markov Chain Monte Carlo for autologistic regression models with application to the distribution of plant species. Biometrics, 54, 509–24CrossRefGoogle Scholar
Hughes, J. P. and Lettenmaier, D. P. (1981). Data requirements for kriging: estimation and network design. Water Resources Research, 17, 1641–50CrossRefGoogle Scholar
Isaaks, E. H. and Srivastava, R. M. (1989). An Introduction to Applied Geostatistics. Oxford: Oxford University Press
Isard, W. (1960). Methods of Regional Analysis. New York: Technology Press of MIT and Wiley
Jaakkola, O. (1998). Multi-scale categorical databases with automatic generalization transformations based on map algebra. Cartography and Geographic Information Systems, 25, 195–207CrossRefGoogle Scholar
Jacquez, G. M. (1994). Cuzick and Edward's test when exact locations are unknown. American Journal of Epidemiology, 140, 58–64CrossRefGoogle Scholar
Jacquez, G. (1995). The map comparison problem: tests for the overlap of geographic boundaries. Statistics in Medicine, 14, 2343–61CrossRefGoogle ScholarPubMed
Jacquez, G. M., Maruca, S. and Fortin, M.-J. (2000). From fields to objects: a review of geographic boundary analysis. Journal of Geographical Systems, 2, 221–41CrossRefGoogle Scholar
James, W. and Stein, C. (1960). Estimation with quadratic loss. Proceedings of the Fourth Berkeley Symposium, vol. 1, ed. Neyman, J., pp. 361–80. Berkeley, CA: University of California Press
Jarman, B. (1993). Identification of underprivileged areas. British Medical Journal, 286, 1705–8CrossRefGoogle Scholar
Johnston, R. J. (1986). The neighbourhood effect revisited: spatial science or political regionalism. Environment and Planning, D, 4, 41–55Google Scholar
Johnstone, J. (1978). Social class, social areas and delinquency. Sociology and Social Research, 63, 49–72Google Scholar
Jolley, D., Jarman, B. and Elliott, P. (1992). Socio-economic confounding. Geographical and Environmental Epidemiology: Methods for Small Area Studies, eds. Elliott, P., Cuzich, J., English, D. and Stern, R. Oxford: Oxford University Press
Jones III, J. P. and Casetti, E. (1992). Applications of the Expansion Method. London: Routledge
Jones, K. and Duncan, C. (1996). People and Places: the multi level model as a general framework for the quantitative analysis of geographical data. Spatial Analysis: Modelling in a GIS Environment, eds Longley, P. and Batty, M., pp. 79–104. Cambridge: GeoInformation International
Jones, K., Gould, M. I. and Watt, R. (1998). Multiple contexts as cross classified models: the Labour vote in the British General Election of 1992. Geographical Analysis, 30, 65–93CrossRefGoogle Scholar
Jones, M. and Sibson, R. (1987). What is projection pursuit? (with discussion). Journal of the Royal Statistical Society, B, 150, 1–36CrossRefGoogle Scholar
Journel, A. E. (1983). Non parametric estimation of spatial distributions. Journal of the International Association of Mathematical Geology, 15, 445–68CrossRefGoogle Scholar
Kafadar, K. (1994). Choosing among two dimensional smoothers in practice. Computational Statistics and Data Analysis, 18, 419–39CrossRefGoogle Scholar
Kafadar, K. (1996). Smoothing geographical data, particularly rates of disease. Statistics in Medicine, 15, 2539–603.0.CO;2-B>CrossRefGoogle Scholar
Kafadar, K. (1999). Simultaneous smoothing and adjusting mortality rates in US counties: melanoma in white females and white males. Statistics in Medicine, 18, 3167–883.0.CO;2-N>CrossRefGoogle Scholar
Kahn, H. and Sempos, C. T. (1989). Statistical Methods in Epidemiology. Oxford: Oxford University Press
Kainz, W. (1995). Logical consistency. Elements of Spatial Data Quality, eds. Guptill, S. C. and Morrison, J. L., pp. 109–37. Oxford: Elsevier ScienceCrossRef
Kaiser, M. S. and Cressie, N. (1997). Modeling Poisson variables with positive spatial dependence. Statistics and Probability Letters, 35, 423–32CrossRefGoogle Scholar
Kauth, R. J. and Thomas, G. S. (1976). The tassled cap: a graphic description of the spectral-temporal development of agricultural crops as seen by LANDSAT. Proceedings of LARS 1976, Symposium on Machine Processing of Remote Sensed Data. Purdue University
Kelejian, H. H. and Robinson, D. P. (1995). Spatial correlation: a suggested alternative to the autoregressive model. New Directions in Spatial Econometrics, eds. Anselin, L. and Florax, R. J. G. M., pp. 75–93. Berlin: SpringerCrossRef
Kelsall, J. E. and Diggle, P. J. (1995). Non-parametric estimation of spatial variation in relative risk. Statistics in Medicine, 14, 2335–42CrossRefGoogle ScholarPubMed
Kennedy, S. (1989). The small number problem and the accuracy of spatial databases. Accuracy of Spatial Databases, eds. Goodchild, M. and Gopal, S., pp. 187–96. London: Taylor & Francis
Kennedy, S. and Tobler, W. R. (1983). Geographic interpolation. Geographical Analysis, 15, 151–6CrossRefGoogle Scholar
Kershaw, K. A. (1973). Quantitative and Dynamic Plant Ecology, second edition, London: Arnold
Kiefer, J. and Wynn, H. P. (1981). Optimum balanced block and Latin square designs for correlated observations. Annals of Statistics, 9. 737–57CrossRefGoogle Scholar
King, G. (1997). A Solution to the Ecological Inference Problem. Princeton, NJ: Princeton University Press
King, G. (1999). The future of ecological inference research: a comment on Freedman et al. Journal of the American Statistical Association, 94, 352–5Google Scholar
King, G. (2000). Geography, statistics and ecological inference. Annals of the Association of American Geographers, 90, 601–6CrossRefGoogle Scholar
Kingman, J. F. C. (1975). Markov models for spatial variation. The Statistician, 24, 167–74CrossRefGoogle Scholar
Knack, S. and Keefer, P. (1997). Does social capital have an economic payoff? A cross country investigation. The Quarterly Journal of Economics, 112, 1251–88CrossRefGoogle Scholar
Knorr-Held, L. and Besag, J. E. (1998). Modelling risk from a disease in space and time. Statistics in Medicine, 17, 2045–603.0.CO;2-P>CrossRefGoogle Scholar
Knorr-Held, L. and Rasser, G. (2000). Bayesian detection of clusters and discontinuities in disease maps. Biometrics, 56, 13–21CrossRefGoogle ScholarPubMed
Koenker, R. and Bassett, G. (1982). Robust tests for heteroskedasticity based on regression quantilers. Econometrica, 50, 43–61CrossRefGoogle Scholar
Koop, J. C. (1990). Systematic sampling of two dimensional surfaces and related problems. Communications in Statistics: Theory and Methods, 19, 1701–50CrossRefGoogle Scholar
Krishna Iyer, P. V. A. (1949). The first and second moments of some probability distributions arising from points on a lattice and their applications. Biometrika, 36, 135–41CrossRefGoogle Scholar
Krug, T. and Martin, R. J. (1990). Information loss on the mean for spatial processes when some values are missing. Communications in Statistics: Theory and Methods, 20, 2168–95Google Scholar
Krugman, P. (1991). Geography and Trade. London: MIT Press
Krugman, P. (1995). Development, Geography and Economic Theory. London: MIT Press
Krugman, P. (1996). Urban concentration: the role of increasing returns and transport costs. International Regional Science Review, 19, 5–30CrossRefGoogle Scholar
Krugman, P. (1998). What's new about the New Economic Geography?Oxford Review of Economic Policy, 14, 7–17CrossRefGoogle Scholar
Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics: Theory and Methods, 26, 1481–96CrossRefGoogle Scholar
Kulldorff, M. (1998). Statistical methods for spatial epidemiology: tests for randomness. GIS and Health, eds. Gatrell, A. and Löytönen, M. pp. 49–62. London: Taylor & Francis
Kulldorff, M. and Nagarwalla, N. (1995). Spatial disease clusters: detection and inference. Statistics in Medicine, 14, 799–810CrossRefGoogle ScholarPubMed
Labovitz, M. L. and Masuoka, E. J. (1984). The influence of auotcorrelation in signature extraction: an example from a geobotanical investigation of Cotter Basin, Montana. International Journal of Remote Sensing, 5, 315–32CrossRefGoogle Scholar
Langford, M., Maguire, D. J. and Unwin, D. J. (1991). The areal interpolation problem: estimating population using remote sensing in a GIS framework. Handling Geographical Information: Methodology and Potential Applications, eds. Masser, I. and Blakemore, M., pp. 55–77. Harlow: Longman
Lawson, A. B., Biggeri, A. B., Boehning, D., Lesaffre, E., Viel, J-F., Clark, A., Schlattmann, P. and Divino, F. (2000). Disease mapping models: an empirical evaluation. Statistics in Medicine, 19, 2217–42Google ScholarPubMed
Lawson, A. B. and Clark, A. (2002). Spatial mixture relative risk models applied to disease mapping. Statistics in Medicine, 21, 359–70CrossRefGoogle ScholarPubMed
Lawson, A. B. and Williams, F. L. (2001). An Introductory Guide to Disease Mapping. Chichester: Wiley
Leamer, E. E. (1978). Specification Searches: Ad Hoc Inference with Non-experimental Data. New York: Wiley
Lee, J., Snyder, P. K. and Fisher, P. F. (1992). Modelling the effect of data errors on feature extraction from digital elevation models. Photogrammetric Engineering and Remote Sensing, 58, 1461–7Google Scholar
Lee, P. (1999a). Where are the socially excluded: continuing debates in the identification of poor neighbourhoods. Regional Studies, 33, 483–9CrossRefGoogle Scholar
Lee, P. (1999b). Where are the deprived? Measuring deprivation in cities and regions. Statistics in Society: The Arithmetic of Politics, eds. Dorling, D. and Simpson, S., London: Arnold
Lee, S-I. (2001). Developing a bivariate spatial association measure: an integration of Pearson's r and Moran's I. Journal of Geographical Systems, 3, 369–86CrossRefGoogle Scholar
Lee, Gallo, J. and Ertur, C. (2002). Exploratory spatial data analysis in the distribution of regional per capita GDP in Europe. Papers in Regional Science (forthcoming)
Legendre, P., Oden, N. L., Sikal, R. R., Vaudor, A. and Kim, J. (1990). Approximate analysis of variance of spatially autocorrelated regional data. Journal of Classification, 7, 53–75CrossRefGoogle Scholar
Leonard, T. (1983). Some philosophies of inference and modelling. Scientific Inference, Data Analysis and Robustness, eds. Box. G. E. P., Leonard, T. and Wu, C.-F., pp. 9–23. New York: Academic PressCrossRef
Little, R. J. A. and Rubin, D. B. (1987). Statistical Analysis with Missing Data. New York: Wiley
Lloyd, O. L. (1995). The exploration of the possible relationship between deaths, births and air pollution in Scottish towns. The Added Value of Geographical Information Systems in Public and Environmental Health. eds. deLepper, M. J. C., Scholten, H. J. and Stern, R. M., pp. 167–80. Dordrecht: Kluwer AcademicCrossRef
Longford, N. T. (1999). Multivariate shrinkage estimation of small area means and proportions. Journal of the Royal Statistical Society, A, 162, 227–45CrossRefGoogle Scholar
Longley, P. A. and Batty, M. (1996). Spatial Analysis: Modelling in a GIS Environment. Cambridge: GeoInformation International
Longley, P. A., Goodchild, M. F., Maguire, D. J. and Rhind, D. W. (1999). Geographical Information Systems, second edition, Vols 1 and 2. New York: Wiley
Longley, P. A., Goodchild, M. F., Maguire, D. J. and Rhind, D. W. (2001). Geographical Information Systems and Science. Chichester: Wiley
Lopez, A. D. (1992). Mortality Data. Geographical and Environmental Epidemiology: Methods for Small Area Studies, eds. Elliott, P. J., Cuzick, J., English, D. et al., pp. 37–50. Oxford: Oxford University Press
Losch, A. (1939). The Economics of Location (English translation, 1957). New York: J. Wiley & Sons
Lovett, A., Haynes, R., Bentham, G., Gale, S., Brainard, J. and Suennenberg, G. (1998). Improving health needs assessment using patient register information in a GIS. GIS and Health, eds. Gatrell, A. and Löytönen, M., pp. 191–203. London: Taylor & Francis
Lowell, K. and Jaton, A. (1999). Spatial Accuracy Assessment: Land Information Uncertainties in Natural Resources. Chelsea, MI: Ann Arbor Press
McBratney, A. B. and Webster, R. (1986). Choosing functions for semi variograms of soil properties and fitting them to sampling estimates. Journal of Soil Science, 37, 617–39CrossRefGoogle Scholar
McLain, D. H. (1974). Drawing contours from arbitrary data points. Computer Journal, 17, 318–24CrossRefGoogle Scholar
MacDougall, E. B. (1992). Exploratory analysis, dynamic statistical visualization and geographic information systems. Cartography and Geographic Information Systems, 19, 237–46CrossRefGoogle Scholar
MacEachren, A. M. (1995). How Maps Work: Representation, Visualization and Design. New York: The Guilford Press
MacEachren, A. M. and Kraak, M-J. (1997). Exploratory cartographic visualization: advancing the agenda. Computers and Geosciences, 23, 335–43CrossRefGoogle Scholar
MacEachren, A. M. and Monmonier, M. (1992). Introduction (to special edition on Geographic Visualization). Cartography and Geographic Information Systems, 19, 197–200CrossRefGoogle Scholar
MacLeod, M., Graham, E., Johnston, M., Dibben, C. and Morgan, I. (1999). How does relative deprivation affect health? ESRC Health Variations Newsletter, 3, January, Lancaster University
Macmillan, W. (2001). Redistricting in a GIS environment: an optimisation algorithm using switching points. Journal of Geographical Systems, 3, 167–80CrossRefGoogle Scholar
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–97Google Scholar
Maffini, G., Arno, M. and Bitterlich, W. (1989). Observations and comments on the generation and treatment of error in digital GIS data. Accuracy in Spatial Databases, eds. Goodchild, M. and Gopal. S., pp. 55–67. London: Taylor & Francis
Majure, J., Cook, D., Cressie, N.Kaiser, M., Lahiri, S. and Symanzik, J. (1996). Spatial CDF estimation and visualization with appliction to forest health monitoring. Computing Science and Statistics, 27, 93–101Google Scholar
Majure, J. J. and Cressie, N. (1997). Dynamic graphics for exploring spatial dependence in multivariate spatial data. Geographical Systems, 4, 131–58Google Scholar
Manton, K. G. and Stallard, E. (1981). Methods for the analysis of mortality risks across heterogeneous small populations: estimation of space-time gradients in cancer mortality in North Carolina counties 1970–75. Demography, 18, 217–30CrossRefGoogle Scholar
Mardia, K. V. and Marshall, R. J. (1984). Maximum likelihood estimation of models for residual covariance in spatial regression. Biometrika, 70, 135–46CrossRefGoogle Scholar
Mark, D. M. (1999). Spatial representation: a cognitive view. Geographical Information Systems: Volume 1 Principles and Technical Issues, eds. Longley, P. A., Goodchild, M. F., Maguire, D. J. and Rhind, D. W., pp. 81–9. New York: Wiley
Markoff, J. and Shapiro, G. (1973). The linkage of data describing overlapping geographical units. Historical Methods Newsletter, 7, 34–46CrossRefGoogle Scholar
Martens, P. L. (1993). An ecological model of socialisation in explaining offending. Integrating Individual and Ecological Aspects of Crime, eds. Farrington, D. P., Sampson, R. J. and Wikstrom, P.-O. H. Stockholm: National Council for Crime Prevention
Martin, D. J., Senior, M. L. and Williams, H. C. W. L. (1994). On measures of deprivation and the spatial allocation of resources for primary health care. Environment and Planning, A, 26, 1911–29CrossRefGoogle Scholar
Martin, D. J. (1998). Optimizing census geography: the separation of collection and output geographies. International Journal of Geographical Information Science, 12, 673–85CrossRefGoogle ScholarPubMed
Martin, D. J. (1999). Spatial representation: the social scientists' perspective. Geographical Information Systems: Volume 1. Principles and Technical Issues, second edition, eds. Longley, P. A., Goodchild, M. F., Maguire, D. J. and Rhind, D. W., pp. 71–89. New York: Wiley
Martin, R. L. (1999). The new ‘geographical turn’ in economics: some critical reflections. Cambridge Journal of Economics, 23, 68–91CrossRefGoogle Scholar
Martin, R. L. and Sunley, P. (1996). Paul Krugman's geographical economics and its implications for regional development theory: a critical assessment. Economic Geography, 72, 259–92CrossRefGoogle Scholar
Martin, R. J. (1984). Exact maximum likelihood for incomplete data from a correlated Gaussian process. Communications in Statistics: Theory and Methods, 13, 1275–88CrossRefGoogle Scholar
Martin, R. J. (1987). Some comments on correction techniques for boundary effects and missing value techniques. Geographical Analysis, 19, 273–82CrossRefGoogle Scholar
Martin, R. J. (1989). Information loss due to incomplete data from a spatial Gaussian one-parameter first-order conditional process. Communications in Statistics: Theory and Methods, 18, 4631–45CrossRefGoogle Scholar
Maruca, S. L. and Jacquez, G. M. (2002). Area-based tests for association between spatial patterns. Journal of Geographical Systems, 4, 69–83CrossRefGoogle Scholar
Matern, B. (1986). Spatial Variation. Lecture Notes in Statistics, 36. Berlin: Springer-VerlagCrossRef
Matheron, G. (1976). A simple substitute for conditional expectation: the disjunctive kriging. Advanced Geostatistics in the Mining Industry, eds. Guarascio, M., David, M. and Huijbrechts, C., pp. 221–36. Dordrecht: ReidelCrossRef
Matula, D. W. and Sokal, R. R. (1980). Properties of Gabriel graphs relevant to geographic variation and the clustering of points in the plane. Geographical Analysis, 12, 205–22CrossRefGoogle Scholar
Mawby, R. I. (1989). Policing and the criminal area. The Geography of Crime, eds. Evans, D. J. and Herbert, D. T., London: Routledge
McGuire, P. G. (2000). The New York Police Department – COMPSTAT process. Analyzing Crime Patterns: Frontiers of Practice, eds. Goldsmith, V., McGuire, P. G., Mollenkopf, J. H. and Ross, T. A., pp. 11–22. Thousand Oaks: SageCrossRef
Mencken, F. C. and Barnett, C. (1999). Murder, non-negligent manslaughter and spatial autocorrelation in mid-South counties. Journal of Quantitative Criminology, 15, 407–22CrossRefGoogle Scholar
Messner, S. F., Anselin, L., Baller, R. D., Hawkins, D. F., Deane, G. and Tolnay, S. E. (1999). The spatial patterning of county homicide rates: an application of exploratory spatial data analysis. Journal of Quantitative Criminology, 15, 423–50CrossRefGoogle Scholar
Miesch, A. T. (1975). Variograms and variance components in geochemistry and ore evaluation. Quantitiative Studies in the Geological Sciences, ed. Whitten, E. H. T., pp. 333–40. Colorado: Geological Society of America
Mikhail, E. M. (1976). Observations and Least Squares. New York: IEP-Dun-Donnely
Milner, A. (1959). A centric systematic area sample treated as a random sample. Biometrics, 15, 270–97CrossRefGoogle Scholar
Minister for the Cabinet Office (1999). White Paper: Modernising Government. London: HMSO. Also: www.cabinet-office.gov.uk/seu/exec_summary/summary.htm
Mollie, A. (1996). Bayesian mapping of disease. Markov Chain Monte Carlo in Practice: Interdisciplinary Statistics, pp. 359–79. London: Chapman & Hall
Monmonier, M. S. (1989). Geographic brushing: exhancing exploratory analysis of the scatterplot matrix. Geographical Analysis, 21, 81–4CrossRefGoogle Scholar
Monmonier, M. S. (1991). How to Lie with Maps. Chicago, IL.: University of Chicago Press
Monmonier, M. S. (1992). Authoring graphic scripts: experiences and principles. Cartography and Geographic Information Systems, 19, 247–60CrossRefGoogle Scholar
Monmonier, M. S. and MacEachren, A. M. (1992). Graphic visualization. Cartography and Geographic Information Systems, Special Edition, 19, 197–260Google Scholar
Moran, C. J. and Bui, E. N. (2000). Stochastic and partly-determined statistical perturbation of points, polygons and surface to assess certainty of environmental modelling. In Accuracy 2000, Proceedings of the 4th International Symposium on Spatial Accuracy Assessment in Natural Resource and Environmental Science, Amsterdam, July, pp. 493–500. Delft University Press
Moran, P. A. P. (1948). The interpretation of statistical maps. Journal of the Royal Statistical Society, B, 10, 243–51Google Scholar
Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37, 17–23CrossRefGoogle ScholarPubMed
Moreno, R. and Trehan, B. (1997). Location and the growth of nations. Journal of Economic Growth, 2, 399–418CrossRefGoogle Scholar
Morganstern, H. (1982). Uses of ecologic analysis in epidemiologic research. American Journal of Public Health, 72, 1336–44CrossRefGoogle Scholar
Morphet, C. S. (1993). The mapping of small-area census data – a consideration of the role of enumeration district boundaries. Environment and Planning, A, 25, 1267–77CrossRefGoogle Scholar
Morrison, D. F. (1967). Multivariate Statistical Methods. New York: McGraw Hill
Mrozinski, R. D. and Cromley, R. G. (1999). Singly and doubly constrained methods of areal interpolation for vector-based GIS. Transactions in GIS, 3, 285–301CrossRefGoogle Scholar
Mungiole, M., Pickle, L. W. and Simonson, K. H. (1999). Application of a weighted head banging algorithm to mortality data maps. Statistics in Medicine, 18, 3201–93.0.CO;2-U>CrossRefGoogle ScholarPubMed
Neprash, J. A. (1934). Some problems in the correlation of spatially distributed variables. Journal of the American Statistical Association, 29, 167–8Google Scholar
Nester, M. R. (1996). An applied statistician's creed. Applied Statistician, 45, 401–10CrossRefGoogle Scholar
Ning, X. and Haining, R. P. (2002). Spatial pricing in interdependent markets: a case study of petrol retailing in Sheffield. Submitted to Environment and Planning, A
Norcliffe, G. (1977). Inferential Statistics for Geographers. London: Hutchinson
O'Connell, P. E., Gurney, R. J., Jones, D. A., Miller, J. B., Nicholas, C. A. and Senior, M. R. (1979). A case study of rationalization of a rain guage network in SW England. Water Resources Research, 15, 1813–22CrossRefGoogle Scholar
Oden, N. (1995). Adjusting Moran's I for population density. Statistics in Medicine, 14, 17–26CrossRefGoogle ScholarPubMed
Oden, N. L., Sokal, R. R., Fortin, M-J. and Goebl, H. (1993). Categorical Wombling: detecting regions of significant change in spatially located categorical variables. Geographical Analysis, 25, 315–36CrossRefGoogle Scholar
Okabe, A. and Tagashira, N. (1996). Spatial aggregation bias in a regression model containing a distance variable. Geographical Systems, 3, 77–100Google Scholar
Oliver, M. A. and Webster, R. (1986). Semi-variograms for modelling the spatial pattern of land form and soil properties. Earth Surface Processes and Land forms, 11, 491–504CrossRefGoogle Scholar
Oliver, M. A. and Webster, R. (1989). A geostatistical basis for spatial weighting in multivariate classification. Mathematical Geology, 21, 15–35CrossRefGoogle Scholar
Olson, J. M. (1976). Non contiguous area cartograms. Professional Geographer, 28, 371–80CrossRefGoogle Scholar
Olson, J. M. (1987). Review of ‘Elements of Graphing Data’. The American Cartographer, 14, 88–89Google Scholar
Openshaw, S. (1978). An optimal zoning approach to the study of spatially aggregated data. In Spatial Representation and Spatial Interaction, eds. Masser, I. and Brown, P. J. B., pp. 93–113. Leiden: Martinus NijhoffCrossRef
Openshaw, S. (1995). The future of the Census. Census User's Handbook, ed. Openshaw, S., pp. 389–411. Cambridge: GeoInformation International
Openshaw, S. (1996). Developing GIS-relevant zone based spatial analysis methods. Spatial Analysis: Modelling in a GIS Environment, eds. Longley, P. and Batty, M., pp. 55–78. Cambridge: GeoInformation International
Openshaw, S. and Abrahart, R. (1996). Geocomputation. Geocomputation'96: Proceedings of First International Conference on Geocomputation, University of Leeds, pp. 665–6. Leeds: Department of Geography
Openshaw, S., Charlton, M., Wymer, C. and Craft, A. (1987). A mark 1 Geographical Analysis Machine for the automated analysis of point data sets. International Journal of Geographical Information Systems, 1, 335–58CrossRefGoogle Scholar
Openshaw, S. and Rao, L. (1995). Algorithms for re-engineering 1991 Census geography. Environment and Planning, A, 27, 425–46CrossRefGoogle Scholar
Openshaw, S. and Wymer, C. (1995). Classifying and regionalizing Census data. Census User's Handbook, ed. Openshaw, S., pp. 239–70. Cambridge: GeoInformation International
Orchard, R. and Woodbury, M. (1972). The missing information principle: theory and application. Proceedings of the 6th Berkeley Symposium on Mathematical Statistics and Probability, eds. LeCam, L., Neyman, J. and Scott, E., Vol. 1, pp. 697–715. Berkeley, CA: University of California Press
Ord, J. K. (1975). Estimation methods for models of spatial interaction. Journal of the American Statistical Association, 70, 120–6CrossRefGoogle Scholar
Orford, S., Dorling, D. and Harris, R. (1998). Review of visualization in the social sciences: a state of the art survey and report. Advisory Group on Computer Graphics. Technical Report Series, No. 41, July ISSN 1356–9066Google Scholar
Pace, R. K., Barry, R., Clapp, J. M. and Rodriguez, M. (1998). Spatiotemporal autoregressive models of neighbourhood effects. The Journal of Real Estate Finance and Economics, 17, 15–33CrossRefGoogle Scholar
Pelto, C. R., Elkins, T. A. and Boyd, H. A. (1968). Automatic contouring of irregularly spaced data. Geophysics, 33, 424–30CrossRefGoogle Scholar
Pereira, J. M. C., Carreiras, J. M. B. and Perestrello de Vasconcelos, M. J. (1998). Exploratory data analysis of the spatial distribution of wildfire in Portugal, 1980–89. Geographical Systems, 5, 355–90Google Scholar
Perruchet, C. (1983). Constrained agglomerative hierarchical classification. Pattern Recognition, 16, 213–17CrossRefGoogle Scholar
Phipps, M. (1989). Dynamical behaviour of cellular automata under the constraint of neighbourhood coherence. Geographical Analysis, 21, 197–215CrossRefGoogle Scholar
Pickett, K. E. and Pearl, M. (2001). Multi-level analyses of neighbourhood socio-economic context and health outcomes: a critical review. Journal of Epidemiology and Community Health, 55, 111–22CrossRefGoogle Scholar
Plummer, P., Haining, R. and Sheppard, E. (1998). Spatial pricing in interdependent markets: testing assumptions and modeling price variation. A case study of gasoline retailing in St Cloud, Minnesota. Environment and Planning, A, 30, 67–84CrossRefGoogle Scholar
Pocock, S. J., Cook, D. G. and Beresford, S. A. A. (1981). Regression of area mortality rates on explanatory variables: what weighting is appropriate. Applied Statistician, 30, 286–96CrossRefGoogle ScholarPubMed
Pocock, S. J., Cook, D. G. and Shaper, A. G. (1982). Analyzing geographic variation in cardiovascular mortality: methods and results. Journal of the Royal Statistical Society, A, 145, 313–41CrossRefGoogle Scholar
Porter, M. E. (1998). The Competitive Advantage of Nations. London: MacMillan
Portugali, J., Benenson, I. and Omer, I. (1994). Sociospatial residential dynamics: stability and instability within a self-organizing city. Geographical Analysis, 26, 321–40CrossRefGoogle Scholar
Putnam, R. D. (1993). Making Democracy Work: Civic Traditions in Modern Italy. Princeton, NJ: Princeton University Press
Raper, J. F. (1999). Spatial representation: the scientist's perspective. Geographic Information Systems, second edition, eds. Longley, P. A., Goodchild, M. F., Maguire, D. J. and Rhind, D. W., pp. 61–70. New York: Wiley
Raper, J. F. (2001). Defining spatial socio-economic units: retrospective and prospective. Life and Motion of Socio-economic Units, eds. Frank, A., Raper, J. and Cheylan, J-P., pp. 13–20. London: Taylor & Francis
Raybould, S. and Walsh, S. (1995). Road traffic accidents involving children in North-East England. The Added Value of Geographical Information Systems in Public and Environmental Health. eds. deLepper, M. J. C., Scholten, H. J. et al., pp. 181–8. Dordrecht: Kluwer AcademicCrossRef
Rey, S. J. (2001). Spatial empirics for economic growth and convergence. Geographical Analysis, 33, 195–214CrossRefGoogle Scholar
Rey, S. J. and Montouri, B. D. (1999). US regional income convergence: a spatial econometric perspective. Regional Studies, 33, 143–56CrossRefGoogle Scholar
Richards, J. A. (1986). Remote Sensing Digital Image Analysis. Berlin: Springer-Verlag
Richardson, H. W. (1970). Regional Economics. London: MacMillan
Richardson, S. (1992). Statistical methods for geographical correlation studies. Geographical and Environmental Epidemiology: Methods for Small Area Studies, eds. Elliot, P., Cuzich, J., English, D. and Stern, R., pp. 181–204. Oxford: Oxford University Press
Richardson, S. and Hémon, D. (1981). On the variance of the sample correlation between two independent lattice processes. Journal of Applied Probability, 18, 943–8CrossRefGoogle Scholar
Richardson, S., Monfort, C., Green, M., Draper, G. and Muirhead, C. (1995). Spatial variation of natural radiation and childhood leukaemia incidence in Great Britain. Statistics in Medicine, 14, 2487–501CrossRefGoogle ScholarPubMed
Riedwyl, H. and Schuepbach, M. (1994). Parquet diagram to plot contingency tables. Advances in Statistical Software, ed. Faulbaum, F., pp. 293–9. Stuttgart: Gustav Fischer
Ripley, B. D. (1981). Spatial Statistics. New York: Wiley
Ripley, B. D. (1988). Statistical Inference for Spatial Processes. Cambridge: Cambridge University Press
Robinson, W. S. (1950). Ecological correlations and the behaviour of individuals. American Sociological Review, 15, 351–7CrossRefGoogle Scholar
Rodriguez-Iturbe, I. and Mejia, J. M. (1974). The design of rainfall networks in time and space. Water Resources Research, 10, 713–28CrossRefGoogle Scholar
Rogerson, P. A. (1999). The detection of clusters using a spatial version of the chi-square goodness of fit statistic. Geographical Analysis, 31, 130–47CrossRefGoogle Scholar
Rosenthal, R. (1978). How often are our numbers wrong?American Psychologist, 33, 1005–8CrossRefGoogle Scholar
Rossiter, D. J. and Johnston, R. J. (1981). Program GROUP: the identification of all possible solutions to a constituency-delimitation problem. Environment and Planning, A, 13, 231–8CrossRefGoogle Scholar
Rossmo, D. K. (2000). Geographic Profiling. Boca Raton: CRC Press
Rushton, G. (1998). Improving the geographic basis of health surveillance using GIS. GIS and Health, eds. Gatrell, A. and Löytönen, M., pp. 63–79. London: Taylor & Francis
Rushton, G. and Lolonis, P. (1996). Exploratory spatial analysis of birth defect rates in an urban population. Statistics in Medicine, 15, 717–263.0.CO;2-0>CrossRefGoogle Scholar
Sadahiro, Y. (1999). Accuracy of areal interpolation: a comparison of alternative methods. Journal of Geographical Systems, 1, 323–46CrossRefGoogle Scholar
Sadahiro, Y. (2000). Accuracy of count data estimated by the point-in-polygon method. Geographical Analysis, 32, 64–89CrossRefGoogle Scholar
Salgé, F. (1995). Elements of Spatial Data Quality, eds. Guptill, S. C. and Morrison, J. L., pp. 139–51. Oxford: Elsevier Science
Sampson, R. J., Raudenbush, S. W. and Earls, F. (1997). Neighborhoods and violent crime: a multi-level study of collective efficacy. Science, 277, 918–24CrossRefGoogle Scholar
Savelieva, E. Kanevski, M., Demyanov, V., Chernov, S. and Maignan, M. (1998). Conditional stochastic co-simulations of the Chernobyl fallout. geoENV II – Geostatistics for Environmental Applications, eds. Gómez-Hernández, J., Soares, A. and Froidevaux, R., pp. 453–65 Dordrecht: Kluwer Academic
Schaeffer, F. (1953). Exceptionalism in Geography. Annals of the Association of American Geographers 43, 226–49CrossRefGoogle Scholar
Schulman, J., Selvin, S. and Merrill, D. W. (1988). Density equalised map projections: a method for analysing clustering around a fixed point. Statistics in Medicine, 7, 491–505CrossRefGoogle Scholar
Shaw, C. R. and McKay, H. D. (1942). Juvenile Delinquency and Urban Areas. Chicago, IL: Chicago University Press
Shepard, D. S. (1983). Computer mapping: the symap interpolation algorithm. Spatial Statistics and Models, eds. Gaile, G. L. and Willmott, C. J., pp. 55–79. Dordrecht: Reidel
Sheppard, E., Haining, R. and Plummer, P. (1992). Spatial pricing in interdependent markets. Journal of Regional Science, 32, 55–75CrossRefGoogle Scholar
Short, N. M. (1999). Remote Sensing Tutorial. NASA publ. http://rst.gsfc.nasa.gov/Front/tofc.html
Sibson, R. (1981). A brief description of natural neighbour interpolation. Interpreting Multivariate Data, ed. Barnett, V., pp. 21–36. Chichester: Wiley
Silverman, B. W. (1986). Density Estimation of Statistics and Data Analysis. Andover, Hants: Routledge, Chapman & Hall
Simpson, C. H. (1951). The interpretation of interaction in contingency tables. Journal of the Royal Statistical Society, B, 13, 242–9Google Scholar
Smans, M. and Estive, J. (1992). Practical approaches to disease mapping. Geographical and Environmental Epidemiology: Methods for Small Area Studies, eds. Elliott, P., Cuzick, J., English, E. and Stern, R., pp. 141–9 Oxford: Oxford University Press
Sokal, R. R., Oden, N. L., Thompson, B. A. and Kim, J. (1993). Testing for regional differences in means: distinguishing inherent from spurious spatial autocorrelation by restricted randomization. Geographical Analysis, 25, 199–210CrossRefGoogle Scholar
Sooman, A. and Macintyre, S. (1995). Health and perceptions of the local environment in socially contrasting neighbourhoods in Glasgow. Health and Place, 1, 15–26CrossRefGoogle Scholar
Spence, N. A. (1968). A multivariate uniform regionalization of British counties on the basis of employment data for 1961. Regional Studies, 2, 87–104CrossRefGoogle Scholar
Spiegelhalter, D. J., Best, N. G. Carlin, B. P. and van der Linde, A. (2001). Bayesian measures of model complexity and fit. Technical report, Medical Research Council Biostatistics Unit, Cambridge, UK (www.mrc-bsu.cam.ac.uk/Publications/preslid.shtml)
Stehman, S. (1996). Estimating the Kappa coefficient and its variance under stratified random sampling. Photogrammetric Engineering and Remote Sensing, 62, 401–7Google Scholar
Stephan, F. (1934). Sampling errors and interpretations of social data ordered in time and space. Journal of the American Statistical Association, 29, Suppl. 165–6Google Scholar
Stone, R. A. (1988). Investigations of excess environmental risks around putative sources: statistical problems and a proposed test. Statistics in Medicine, 7, 649–60CrossRefGoogle Scholar
Student, (1914). The elimination of spurious correlation due to position in time and space. Biometrika, 10, 179–80CrossRefGoogle Scholar
Swartz, C. (2000). The spatial analysis of crime. Analyzing Crime Patterns: Frontiers of Practice, eds. Goldsmith, V., McGuire, P. G., Mollenkopf, J. H. and Ross, R. A., pp. 33–46. Thousand Oaks: SageCrossRef
Swerdlow, A. J. (1992). Cancer incidence data for adults. Geographical and Environmental Epidemiology: Methods for Small Area Studies, eds. Elliott, P. J., Cuzick, J., English D. and Stern, R., pp. 51–62. Oxford: Oxford University Press
Switzer, P. (2000). Multiple simulation of spatial fields. Proceedings of the 4th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, pp. 629–35. Delft: Delft University Press
Switzer, P. (2000). Probabilistic exploration strategies. Spatial archaeometry: using spatial statistical methods in archaeology and cultural heritage safeguard research. Report of the Interdisciplinary Workshop on spatial statistical methods in archaeology and cultural heritage research, Pescara, Italy, July
Symarzik, J., Majure, J. and Cook, D. (1996). Dynamic graphics in a GIS: a bidirectional link between Arc View 2.0 and Xgobi. Computing Science and Statistics, 27, 299–303Google Scholar
Tango, T. (1995). A class of tests for detecting ‘general’ and ‘focused’ clustering of rare diseases. Statistics in Medicine, 7, 649–60Google Scholar
Taylor, J. R. (1982). An Introduction to Error Analysis. Mill Valley, CA: University Science Books
Taylor, P. (1969). The location variable in taxonomy. Geographical Analysis, 1, 181–95CrossRefGoogle Scholar
Theobald, D. M. (1989). Accuracy and bias issues in surface representation. Accuracy in Spatial Databases, eds. Goodchild, M. and Gopal, S., pp. 99–105. London: Taylor & Francis
Thompson, D. (1998). The National Health Service breast cancer screening programme in Sheffield: service delivery and uptake. Ph. D. Thesis, University of Sheffield
Tiefelsdorf, M. and Boots, B. (1995). The exact distribution of Moran's I. Environment and Planning, A, 27, 985–99CrossRefGoogle Scholar
Tinkler, K. (1972). The physical interpretation of eigenfunctions of dichotomous matrices. Transactions of the Institute of British Geographers, 55, 17–46CrossRefGoogle Scholar
Tjostheim, D. (1978). A measure of association for spatial variables. Biometrika, 65, 109–14CrossRefGoogle Scholar
Tobler, W. (1979). Smooth pycnophylactic interpolation for geographical regions. Journal of the American Statistical Association, 74, 519–36 (with discussion)CrossRefGoogle ScholarPubMed
Tobler, W. (1989). Frame independent spatial analysis. Accuracy of Spatial Databases, eds. Goodchild, M. and Gopal, S., pp. 115–22. London: Taylor & Francis
Tobler, W. R. and Kennedy, S. (1985). Smooth multi-dimensional interpolation. Geographical Analysis, 17, 251–7CrossRefGoogle Scholar
Tobler, W. R. and Lau, J. (1978). Isopleth mapping using histosplines. Geographical Analysis, 10, 273–9CrossRefGoogle Scholar
Townsend, P., Phillimore, P. and Beattie, A. (1988). Health and Deprivation: Inequality and the North. London: Croom Helm
Tranmer, M. and Steel, D. G. (1998). Using census data to investigate the causes of the ecological fallacy. Environment and Planning, A, 30, 817–31CrossRefGoogle ScholarPubMed
Tufte, E. R. (1983). The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press
Tufte, E. R. (1990). Envisioning Information. Chesire, CT: Graphics Press
Tukey, J. W. (1977). Exploratory Data Analysis. Reading: Addison-Wesley
Tukey, J. W. (1979). Statistical mapping: what should not be plotted. The Collected Works of John W. Tukey, Volume V: Graphics, 1965–1985, pp. 109–21. Belmont, CA: Wadsworth (1988)
Tukey, P. A. and Tukey, J. W. (1981). Graphical display of data sets in three or more dimensions. Interpreting Multivariate Data, ed. Barnett, V., New York: Wiley
Turnbull, B. W., Iwano, E. J., Burnett, W. S., Howe, H. L. and Clark, L. C. (1990). Monitoring for clusters of disease: application to leukaemia incidence in upstate New York. American Journal of Epidemiology, 132, S136–S143CrossRefGoogle ScholarPubMed
Ulm, K. (1990). A simple method to calculate the confidence interval of a standardized mortality ratio. American Journal of Epidemiology, 131, 373–5CrossRefGoogle ScholarPubMed
Unwin, A., Hawkins, G., Hofmann, H. and Siegl, B. (1996). Interactive graphics for data sets with missing values – MANET. Journal of Computational and Graphical Statistics, 5, 113–22Google Scholar
Unwin, D. J. (1995). Geographical Information Systems and the problem of ‘error and uncertainty’. Progress in Human Geography, 19, 549–58CrossRefGoogle Scholar
Unwin, D. J. (1997). Graphics, visualiztion and the social sciences. Advisory Group on Computer Graphics, Technical Report Series, No. 33, ISSN 1356–9066, pp. 103–8 Burleigh Court, Loughborough University
Unwin, D. J. and Wrigley, N. (1987). Control point distribution in trend surface modelling revisited: an application of the concept of leverage. Transactions, Institute of British Geographers, 12, 147–60CrossRefGoogle Scholar
Unwin, D. J. and Wrigley, N. (1987a). Towards a general theory of control point distribution effects in trend surface models. Computers and Geosciences, 13, 351–5CrossRefGoogle Scholar
Upton, G. J. G. (1985). Distance weighted geographic interpolations. Environment and Planning, A, 17, 667–71CrossRefGoogle Scholar
Upton, G. J. G. (1991). Rectangular cartograms, spatial autocorrelation and interpolation. Papers in Regional Science, 70, 287–302CrossRefGoogle Scholar
Upton, G. J. G. and Fingleton, B. (1985). Spatial data analysis by example. Vol. 1, Point pattern and quantitative data. Chichester: Wiley
Upton, G. J. G. and Fingleton, B. (1989). Spatial Data Analysis by Example: Volume 2. Categorical and Directional Data. Chichester: Wiley
Venables, A. J. (1999). But why does geography matter, and which geography matters?International Regional Science Review, 22(2), 238–41CrossRefGoogle Scholar
Veregin, H. (1994). Integration of simulation modelling and error propagation for the buffer operation in GIS. Photogrammetric Engineering and Remote Sensing, 60, 427–35Google Scholar
Veregin, H. (1995). Developing and testing of an error propagation model for GIS overlay operations. International Journal of Geographical Information Systems, 9, 595–619CrossRefGoogle Scholar
Veregin, H. and Hargitai, P. (1995). An evaluation matrix for geographical data quality. Elements of Spatial Data Quality, eds. Guptill, S. C. and Morrison, J. L., pp. 167–188. Oxford: Elsevier ScienceCrossRef
Verly, G., David, M., Journel, A. G. and Marechal, A. (1984). Geostatistics for Natural Resources Characterization. Dordrecht: Reidel
Viel, J-F., Arveux, P., Baverel, J. and Cahn, J-Y. (1999). Soft-tissue sarcoma and Non-Hodgkin's Lymphoma clusters around a municipal solid waste incinerator with high dioxin emission levels. American Journal of Epidemiology, 152, 13–19CrossRefGoogle Scholar
Visvalingam, M. (1983). Operational definitions of area based social indicators. Environment and Planning, A, 15, 831–9CrossRefGoogle Scholar
Wakefield, J. and Elliott, P. (1999). Issues in the statistical analysis of small area health data. Statistics in Medicine, 18, 2377–993.0.CO;2-G>CrossRefGoogle ScholarPubMed
Waldhor, T. (1996). The spatial autocorrelation coefficient Moran's I under hetero-scedasticity. Statistics in Medicine, 15, 887–923.0.CO;2-E>CrossRefGoogle Scholar
Waldrop, M. W. (1992). Complexity. New York: Simon & Schuster
Wang, J., Wise, S. and Haining, R. (1997). An integrated regionalization of earthquake, flood and drought hazards in China. Transactions in Geographic Information Systems, 2, 25–44Google Scholar
Webster, M. R. (1977). Quantitative and Numerical Methods in Soil Classification and Survey. Oxford: Clarendon Press
Webster, M. R. (1985). Quantitative analysis of soil in the field. Advances in SoilSciences, 3, 1–70Google Scholar
Webster, M. R. and Burgess, T. M. (1981). Optimal interpolation and isarithmic mapping of soil properties. III Changing drift and universal kriging. Journal of Soil Sciences, 32, 505–24Google Scholar
Webster, R. and Burrough, P. A. (1972). Computer based soil mapping of small areas from sample data. II Classification smoothing. Journal of Soil Science, 23, 222–34CrossRefGoogle Scholar
Webster, R. and Oliver, M. A. (2001). Geostatistics for Environmental Scientists. Chichester: John Wiley
Webster, R., Oliver, M. A., Muir, K. R. and Mann, J. R. (1994). Kriging the local risk of a rare disease from a register of diagnoses. Geographical Analysis, 26, 168–85CrossRefGoogle Scholar
Wegener, M. (2000). Spatial models and GIS. Spatial Models and GIS, eds. Fotheringham, A. S. and Wegener, M., pp. 3–20. London: Taylor & Francis
Wegman, E. (1995). Huge data sets and the frontiers of computational feasibility. Journal of Computational and Graphical Statistics, 4, 281–95Google Scholar
Wegman, E. J., Posta, W. L. and Solka, J. L. Image grand tour. (www.galaxy.gmu.edn/stats/center.html)
Weisberg, S. (1985). Applied Linear Regression. New York: John Wiley
Welch, R., Jordan, T. R. and Ehlers, M. (1985). Comparative evaluations of the geodetic accuracy and cartographic potential of LANDSAT-4 and LANDSAT-5 Thematic Mapper image data. Photogrammetric Engineering and Remote Sensing, 51, 1799–812Google Scholar
White, H. (1980). A heteroskedasticity – consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48, 817–38CrossRefGoogle Scholar
White, R. and Engelen, G. (1994). Cellular dynamics and GIS: modelling spatial complexity. Geographical Systems, 1, 237–53Google Scholar
Whittle, P. (1954). On stationary processes in the plane. Biometrika, 41, 434–49CrossRefGoogle Scholar
Wikström, P.-O. H. (1990). Delinquency and the urban structure. Crime and Measures against Crime in the City, ed. Wikström, P.-O. H., Stockholm: National Council for Crime Prevention
Wikström, P.-O. H. (1991). Urban Crime, Criminals and Victims: The Swedish Experience in an Anglo-American Comparative Perspective. New York: Springer-Verlag
Wikström, P-O. and Loeber, R. (2000). Do disadvantaged neighbourhoods cause well-adjusted children to become adolescent delinquents? A study of male juvenile serious offending, individual risk and protective factors and neighbourhood context. Criminology, 38, 1109–42CrossRefGoogle Scholar
Wilhelm, A. and Sander, M. (1998). Interactive statistical analysis of dialect features. The Statistician, 47, 445–55Google Scholar
Wilkinson, C., Grundy, C., Landon, M. and Stevenson, S. (1998). GIS in public health. GIS and Health, eds. Gatrell, A. C. and Löytönen, M., pp. 179–89. London: Taylor & Francis
Wilkinson, R. G. (1996). Unhealthy Societies. London: Routledge
Wilson, W. J. (1997). When Work Disappears: The World of the New Urban Poor. New York: Alfred Knopf
Wise, S. M., Haining, R. P. and Ma, J. (1997). Regionalization tools for the exploratory spatial analysis of health data. Recent Developments in Spatial Analysis: Spatial Statistics, Behavioural Modelling and Neuro-Computing, eds. Fischer, M. and Getis, A., pp. 83–100. Berlin: Springer-VerlagCrossRef
Wise, S. M., Haining, R. P. and Signoretta, P. E. (1999). Scientific visualization and the exploratory analysis of area data. Environment and Planning, A, 31, 1823–38CrossRefGoogle Scholar
Womble, W. H. (1951). Differential systematics. Science, 114, 315–22CrossRefGoogle ScholarPubMed
Wong, D. and Amrhein, C. (1996). Research on the MAUP: old wine in a new bottle or real breakthrough?Geographical Systems, 3, 73–76Google Scholar
Wray, N. R., Alexander, F. E., Muirhead, C. R., Pukkala, E., Schmidtmann, I. and Stiller, C. (1999). A comparison of some simple methods to identify geographical areas with excess incidence of a rare disease such as childhood leukaemia. Statistics in Medicine, 18, 1501–163.0.CO;2-E>CrossRefGoogle ScholarPubMed
Wright, D. L. Stern, H. S. and Cressie, N. (2002). Loss functions for estimation of extrema with an application to disease mapping. Paper presented at the Spatial Econometrics workshop, Toulouse, France, June 15, 2002 and submitted to the Canadian Journal of Statistics
Wrigley, N., Holt, T., Steel, D. and Tranmer, M. (1996). Analysing, modelling and resolving the ecological fallacy. Spatial Analysis: Modelling in a GIS Environment, eds. Longley, P. and Batty, M., pp. 23–40. Cambridge: GeoInformation International
Xie, Yichun. (1996). A generalized model for cellular urban dynamics. Geographical Analysis, 28, 350–73CrossRefGoogle Scholar
Youden, W. J. and Mehlich, A. (1937). Selection of efficient methods for soil sampling. Contributions to the Boyce Thompson Institute of Plant Research, 9, 59–70Google Scholar
Yule, G. U. (1926). Why do we sometimes get nonsense correlations between two time series?Journal of the Royal Statistical Society, 89, 1–69CrossRefGoogle Scholar
Zhang, J. and Kirby, R. P. (2000). A geostatistical approach to modelling positional errors in vector data. Transactions in GIS, 4, 145–59CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • References
  • Robert Haining, University of Cambridge
  • Book: Spatial Data Analysis
  • Online publication: 06 July 2010
  • Chapter DOI: https://doi.org/10.1017/CBO9780511754944.018
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • References
  • Robert Haining, University of Cambridge
  • Book: Spatial Data Analysis
  • Online publication: 06 July 2010
  • Chapter DOI: https://doi.org/10.1017/CBO9780511754944.018
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • References
  • Robert Haining, University of Cambridge
  • Book: Spatial Data Analysis
  • Online publication: 06 July 2010
  • Chapter DOI: https://doi.org/10.1017/CBO9780511754944.018
Available formats
×